From 51fc0afa779aaee888e3bf369389f4abf76c0d8b Mon Sep 17 00:00:00 2001
From: stephenturner <vustephen@gmail.com>
Date: Wed, 25 Sep 2024 20:56:08 +0000
Subject: [PATCH] =?UTF-8?q?Deploying=20to=20gh-pages=20from=20@=20stephent?=
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 =?UTF-8?q?=F0=9F=9A=80?=
MIME-Version: 1.0
Content-Type: text/plain; charset=UTF-8
Content-Transfer-Encoding: 8bit

---
 404.html                                |   4 +-
 CONTRIBUTING.html                       |   6 +-
 LICENSE-text.html                       |   4 +-
 LICENSE.html                            |   6 +-
 authors.html                            |  14 +-
 index.html                              | 201 +++++++++++++++---------
 news/index.html                         |  13 +-
 pkgdown.js                              |   8 +
 pkgdown.yml                             |   4 +-
 reference/add_prompt.html               |  34 ++--
 reference/add_prompt_subject.html       |  17 +-
 reference/add_summary.html              |   8 +-
 reference/biorecap-package.html         |  16 +-
 reference/biorecap_report.html          |  14 +-
 reference/build_prompt_preprint.html    |   6 +-
 reference/build_prompt_subject.html     |  11 +-
 reference/example_preprints.html        |  34 ++--
 reference/figures/report_screenshot.jpg | Bin 0 -> 457542 bytes
 reference/get_preprints.html            |  54 +++----
 reference/index.html                    |   8 +-
 reference/reexports.html                |   6 +-
 reference/subjects.html                 |  66 +++++++-
 reference/tt_preprints.html             |  48 +++---
 search.json                             |   2 +-
 24 files changed, 353 insertions(+), 231 deletions(-)
 create mode 100644 reference/figures/report_screenshot.jpg

diff --git a/404.html b/404.html
index 8c0e86f..048e8ce 100644
--- a/404.html
+++ b/404.html
@@ -27,7 +27,7 @@
 
     <a class="navbar-brand me-2" href="https://stephenturner.github.io/biorecap/index.html">biorecap</a>
 
-    <small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">0.1.1</small>
+    <small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">0.2.0</small>
 
 
     <button class="navbar-toggler" type="button" data-bs-toggle="collapse" data-bs-target="#navbar" aria-controls="navbar" aria-expanded="false" aria-label="Toggle navigation">
@@ -67,7 +67,7 @@
 </div>
 
 <div class="pkgdown-footer-right">
-  <p>Site built with <a href="https://pkgdown.r-lib.org/" class="external-link">pkgdown</a> 2.1.0.</p>
+  <p>Site built with <a href="https://pkgdown.r-lib.org/" class="external-link">pkgdown</a> 2.1.1.</p>
 </div>
 
     </footer>
diff --git a/CONTRIBUTING.html b/CONTRIBUTING.html
index 9216d7f..2d125d3 100644
--- a/CONTRIBUTING.html
+++ b/CONTRIBUTING.html
@@ -7,7 +7,7 @@
 
     <a class="navbar-brand me-2" href="index.html">biorecap</a>
 
-    <small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">0.1.1</small>
+    <small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">0.2.0</small>
 
 
     <button class="navbar-toggler" type="button" data-bs-toggle="collapse" data-bs-target="#navbar" aria-controls="navbar" aria-expanded="false" aria-label="Toggle navigation">
@@ -28,7 +28,7 @@
 <div class="row">
   <main id="main" class="col-md-9"><div class="page-header">
       <img src="logo.png" class="logo" alt=""><h1>Contributing to biorecap</h1>
-      <small class="dont-index">Source: <a href="https://github.com/stephenturner/biorecap/blob/v0.1.1/.github/CONTRIBUTING.md" class="external-link"><code>.github/CONTRIBUTING.md</code></a></small>
+      <small class="dont-index">Source: <a href="https://github.com/stephenturner/biorecap/blob/main/.github/CONTRIBUTING.md" class="external-link"><code>.github/CONTRIBUTING.md</code></a></small>
     </div>
 
 <div id="contributing-to-biorecap" class="section level1">
@@ -71,7 +71,7 @@ <h2 id="code-of-conduct">Code of Conduct<a class="anchor" aria-label="anchor" hr
 </div>
 
 <div class="pkgdown-footer-right">
-  <p>Site built with <a href="https://pkgdown.r-lib.org/" class="external-link">pkgdown</a> 2.1.0.</p>
+  <p>Site built with <a href="https://pkgdown.r-lib.org/" class="external-link">pkgdown</a> 2.1.1.</p>
 </div>
 
     </footer></div>
diff --git a/LICENSE-text.html b/LICENSE-text.html
index 4de556c..0692ace 100644
--- a/LICENSE-text.html
+++ b/LICENSE-text.html
@@ -7,7 +7,7 @@
 
     <a class="navbar-brand me-2" href="index.html">biorecap</a>
 
-    <small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">0.1.1</small>
+    <small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">0.2.0</small>
 
 
     <button class="navbar-toggler" type="button" data-bs-toggle="collapse" data-bs-target="#navbar" aria-controls="navbar" aria-expanded="false" aria-label="Toggle navigation">
@@ -43,7 +43,7 @@
 </div>
 
 <div class="pkgdown-footer-right">
-  <p>Site built with <a href="https://pkgdown.r-lib.org/" class="external-link">pkgdown</a> 2.1.0.</p>
+  <p>Site built with <a href="https://pkgdown.r-lib.org/" class="external-link">pkgdown</a> 2.1.1.</p>
 </div>
 
     </footer></div>
diff --git a/LICENSE.html b/LICENSE.html
index ed9a7c6..6ba8e78 100644
--- a/LICENSE.html
+++ b/LICENSE.html
@@ -7,7 +7,7 @@
 
     <a class="navbar-brand me-2" href="index.html">biorecap</a>
 
-    <small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">0.1.1</small>
+    <small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">0.2.0</small>
 
 
     <button class="navbar-toggler" type="button" data-bs-toggle="collapse" data-bs-target="#navbar" aria-controls="navbar" aria-expanded="false" aria-label="Toggle navigation">
@@ -28,7 +28,7 @@
 <div class="row">
   <main id="main" class="col-md-9"><div class="page-header">
       <img src="logo.png" class="logo" alt=""><h1>MIT License</h1>
-      <small class="dont-index">Source: <a href="https://github.com/stephenturner/biorecap/blob/v0.1.1/LICENSE.md" class="external-link"><code>LICENSE.md</code></a></small>
+      <small class="dont-index">Source: <a href="https://github.com/stephenturner/biorecap/blob/main/LICENSE.md" class="external-link"><code>LICENSE.md</code></a></small>
     </div>
 
 <div id="mit-license" class="section level1">
@@ -47,7 +47,7 @@
 </div>
 
 <div class="pkgdown-footer-right">
-  <p>Site built with <a href="https://pkgdown.r-lib.org/" class="external-link">pkgdown</a> 2.1.0.</p>
+  <p>Site built with <a href="https://pkgdown.r-lib.org/" class="external-link">pkgdown</a> 2.1.1.</p>
 </div>
 
     </footer></div>
diff --git a/authors.html b/authors.html
index 6799935..f50947d 100644
--- a/authors.html
+++ b/authors.html
@@ -7,7 +7,7 @@
 
     <a class="navbar-brand me-2" href="index.html">biorecap</a>
 
-    <small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">0.1.1</small>
+    <small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">0.2.0</small>
 
 
     <button class="navbar-toggler" type="button" data-bs-toggle="collapse" data-bs-target="#navbar" aria-controls="navbar" aria-expanded="false" aria-label="Toggle navigation">
@@ -41,17 +41,17 @@ <h2>Authors</h2>
 
     <div class="section level2">
       <h2 id="citation">Citation</h2>
-      <p><small class="dont-index">Source: <a href="https://github.com/stephenturner/biorecap/blob/v0.1.1/DESCRIPTION" class="external-link"><code>DESCRIPTION</code></a></small></p>
+      <p><small class="dont-index">Source: <a href="https://github.com/stephenturner/biorecap/blob/main/DESCRIPTION" class="external-link"><code>DESCRIPTION</code></a></small></p>
 
       <p>Turner S (2024).
-<em>biorecap: Retrieve and summarize bioRxiv preprints with a local LLM using ollama</em>.
-R package version 0.1.1, <a href="https://stephenturner.github.io/biorecap/">https://stephenturner.github.io/biorecap/</a>.
+<em>biorecap: Retrieve and summarize bioRxiv and medRxiv preprints with a local LLM using ollama</em>.
+R package version 0.2.0, <a href="https://stephenturner.github.io/biorecap/">https://stephenturner.github.io/biorecap/</a>.
 </p>
       <pre>@Manual{,
-  title = {biorecap: Retrieve and summarize bioRxiv preprints with a local LLM using ollama},
+  title = {biorecap: Retrieve and summarize bioRxiv and medRxiv preprints with a local LLM using ollama},
   author = {Stephen Turner},
   year = {2024},
-  note = {R package version 0.1.1},
+  note = {R package version 0.2.0},
   url = {https://stephenturner.github.io/biorecap/},
 }</pre>
     </div>
@@ -65,7 +65,7 @@ <h2 id="citation">Citation</h2>
 </div>
 
 <div class="pkgdown-footer-right">
-  <p>Site built with <a href="https://pkgdown.r-lib.org/" class="external-link">pkgdown</a> 2.1.0.</p>
+  <p>Site built with <a href="https://pkgdown.r-lib.org/" class="external-link">pkgdown</a> 2.1.1.</p>
 </div>
 
     </footer></div>
diff --git a/index.html b/index.html
index a8b676f..0f2cca3 100644
--- a/index.html
+++ b/index.html
@@ -5,7 +5,7 @@
 <meta charset="utf-8">
 <meta http-equiv="X-UA-Compatible" content="IE=edge">
 <meta name="viewport" content="width=device-width, initial-scale=1, shrink-to-fit=no">
-<title>Retrieve and summarize bioRxiv preprints with a local LLM using ollama • biorecap</title>
+<title>Retrieve and summarize bioRxiv and medRxiv preprints with a local LLM using ollama • biorecap</title>
 <!-- favicons --><link rel="icon" type="image/png" sizes="16x16" href="favicon-16x16.png">
 <link rel="icon" type="image/png" sizes="32x32" href="favicon-32x32.png">
 <link rel="apple-touch-icon" type="image/png" sizes="180x180" href="apple-touch-icon.png">
@@ -16,9 +16,9 @@
 <link href="deps/bootstrap-5.3.1/bootstrap.min.css" rel="stylesheet">
 <script src="deps/bootstrap-5.3.1/bootstrap.bundle.min.js"></script><link href="deps/font-awesome-6.4.2/css/all.min.css" rel="stylesheet">
 <link href="deps/font-awesome-6.4.2/css/v4-shims.min.css" rel="stylesheet">
-<script src="deps/headroom-0.11.0/headroom.min.js"></script><script src="deps/headroom-0.11.0/jQuery.headroom.min.js"></script><script src="deps/bootstrap-toc-1.0.1/bootstrap-toc.min.js"></script><script src="deps/clipboard.js-2.0.11/clipboard.min.js"></script><script src="deps/search-1.0.0/autocomplete.jquery.min.js"></script><script src="deps/search-1.0.0/fuse.min.js"></script><script src="deps/search-1.0.0/mark.min.js"></script><!-- pkgdown --><script src="pkgdown.js"></script><meta property="og:title" content="Retrieve and summarize bioRxiv preprints with a local LLM using ollama">
-<meta name="description" content="Retrieve and summarize bioRxiv preprints with a local LLM using ollama.">
-<meta property="og:description" content="Retrieve and summarize bioRxiv preprints with a local LLM using ollama.">
+<script src="deps/headroom-0.11.0/headroom.min.js"></script><script src="deps/headroom-0.11.0/jQuery.headroom.min.js"></script><script src="deps/bootstrap-toc-1.0.1/bootstrap-toc.min.js"></script><script src="deps/clipboard.js-2.0.11/clipboard.min.js"></script><script src="deps/search-1.0.0/autocomplete.jquery.min.js"></script><script src="deps/search-1.0.0/fuse.min.js"></script><script src="deps/search-1.0.0/mark.min.js"></script><!-- pkgdown --><script src="pkgdown.js"></script><meta property="og:title" content="Retrieve and summarize bioRxiv and medRxiv preprints with a local LLM using ollama">
+<meta name="description" content="Retrieve and summarize bioRxiv and medRxiv preprints with a local LLM using ollama.">
+<meta property="og:description" content="Retrieve and summarize bioRxiv and medRxiv preprints with a local LLM using ollama.">
 <meta property="og:image" content="https://stephenturner.github.io/biorecap/logo.png">
 </head>
 <body>
@@ -29,7 +29,7 @@
 
     <a class="navbar-brand me-2" href="index.html">biorecap</a>
 
-    <small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">0.1.1</small>
+    <small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">0.2.0</small>
 
 
     <button class="navbar-toggler" type="button" data-bs-toggle="collapse" data-bs-target="#navbar" aria-controls="navbar" aria-expanded="false" aria-label="Toggle navigation">
@@ -61,7 +61,7 @@
 </div>
 <!-- badges: start -->
 
-<p>Retrieve and summarize <a href="https://www.biorxiv.org/" class="external-link">bioRxiv</a> preprints using a local LLM with <a href="https://ollama.com/" class="external-link">Ollama</a> via <a href="https://cran.r-project.org/package=ollamar" class="external-link">ollamar</a>.</p>
+<p>Retrieve and summarize <a href="https://www.biorxiv.org/" class="external-link">bioRxiv</a> and <a href="https://www.medrxiv.org/" class="external-link">medRxiv</a> preprints using a local LLM with <a href="https://ollama.com/" class="external-link">Ollama</a> via <a href="https://cran.r-project.org/package=ollamar" class="external-link">ollamar</a>.</p>
 <p>Turner, S. D. (2024). biorecap: an R package for summarizing bioRxiv preprints with a local LLM. <em>arXiv</em>, 2408.11707. <a href="https://doi.org/10.48550/arXiv.2408.11707" class="external-link uri">https://doi.org/10.48550/arXiv.2408.11707</a>.</p>
 <div class="section level2">
 <h2 id="installation">Installation<a class="anchor" aria-label="anchor" href="#installation"></a>
@@ -92,61 +92,65 @@ <h3 id="quick-start">Quick start<a class="anchor" aria-label="anchor" href="#qui
 <p>Next we can list our available models:</p>
 <div class="sourceCode" id="cb5"><pre class="downlit sourceCode r">
 <code class="sourceCode R"><span><span class="fu"><a href="https://hauselin.github.io/ollama-r/reference/list_models.html" class="external-link">list_models</a></span><span class="op">(</span><span class="op">)</span></span></code></pre></div>
-<div class="sourceCode" id="cb6"><pre class="sourceCode R"><code class="sourceCode r"><span id="cb6-1"><a href="#cb6-1" tabindex="-1"></a><span class="co">#&gt; # A tibble: 3 × 4</span></span>
-<span id="cb6-2"><a href="#cb6-2" tabindex="-1"></a><span class="co">#&gt;   name            model           parameter_size quantization_level</span></span>
-<span id="cb6-3"><a href="#cb6-3" tabindex="-1"></a><span class="co">#&gt;   &lt;chr&gt;           &lt;chr&gt;           &lt;chr&gt;          &lt;chr&gt;             </span></span>
-<span id="cb6-4"><a href="#cb6-4" tabindex="-1"></a><span class="co">#&gt; 1 gemma2:latest   gemma2:latest   9.2B           Q4_0              </span></span>
-<span id="cb6-5"><a href="#cb6-5" tabindex="-1"></a><span class="co">#&gt; 2 llama3.1:latest llama3.1:latest 8.0B           Q4_0              </span></span>
-<span id="cb6-6"><a href="#cb6-6" tabindex="-1"></a><span class="co">#&gt; 3 llama3.1:70b    llama3.1:70b    70.6B          Q4_0           </span></span></code></pre></div>
-<p>Write an HTML report containing summaries of recent preprints in select subject areas to the current working directory.</p>
+<div class="sourceCode" id="cb6"><pre class="sourceCode R"><code class="sourceCode r"><span id="cb6-1"><a href="#cb6-1" tabindex="-1"></a>             name   size parameter_size quantization_level            modified</span>
+<span id="cb6-2"><a href="#cb6-2" tabindex="-1"></a><span class="dv">1</span>   gemma2<span class="sc">:</span>latest <span class="fl">5.4</span> GB           <span class="fl">9.2</span>B               Q4_0 <span class="dv">2024-08-07</span>T07<span class="sc">:</span><span class="dv">35</span><span class="sc">:</span><span class="dv">15</span></span>
+<span id="cb6-3"><a href="#cb6-3" tabindex="-1"></a><span class="dv">3</span>    llama3<span class="fl">.1</span><span class="sc">:</span><span class="dv">70</span>b  <span class="dv">40</span> GB          <span class="fl">70.6</span>B               Q4_0 <span class="dv">2024-07-24</span>T10<span class="sc">:</span><span class="dv">57</span><span class="sc">:</span><span class="dv">08</span></span>
+<span id="cb6-4"><a href="#cb6-4" tabindex="-1"></a><span class="dv">4</span> llama3<span class="fl">.1</span><span class="sc">:</span>latest <span class="fl">4.7</span> GB           <span class="fl">8.0</span>B               Q4_0 <span class="dv">2024-07-31</span>T09<span class="sc">:</span><span class="dv">38</span><span class="sc">:</span><span class="dv">38</span></span>
+<span id="cb6-5"><a href="#cb6-5" tabindex="-1"></a><span class="dv">5</span> llama3<span class="fl">.2</span><span class="sc">:</span>latest   <span class="dv">2</span> GB           <span class="fl">3.2</span>B             Q4_K_M <span class="dv">2024-09-25</span>T14<span class="sc">:</span><span class="dv">54</span><span class="sc">:</span><span class="dv">23</span></span>
+<span id="cb6-6"><a href="#cb6-6" tabindex="-1"></a><span class="dv">6</span>     phi3<span class="sc">:</span>latest <span class="fl">2.2</span> GB           <span class="fl">3.8</span>B               Q4_0 <span class="dv">2024-08-28</span>T04<span class="sc">:</span><span class="dv">37</span><span class="sc">:</span><span class="dv">58</span>      </span></code></pre></div>
+<p>Write an HTML report containing summaries of recent preprints in select subject areas to the current working directory. You can include both bioRxiv and medRxiv subjects, and biorecap will know which RSS feed to use.</p>
 <div class="sourceCode" id="cb7"><pre class="downlit sourceCode r">
 <code class="sourceCode R"><span><span class="fu"><a href="reference/biorecap_report.html">biorecap_report</a></span><span class="op">(</span>output_dir<span class="op">=</span><span class="st">"."</span>, </span>
-<span>                subject<span class="op">=</span><span class="fu"><a href="https://rdrr.io/r/base/c.html" class="external-link">c</a></span><span class="op">(</span><span class="st">"bioinformatics"</span>, <span class="st">"genomics"</span>, <span class="st">"synthetic_biology"</span><span class="op">)</span>, </span>
+<span>                subject<span class="op">=</span><span class="fu"><a href="https://rdrr.io/r/base/c.html" class="external-link">c</a></span><span class="op">(</span><span class="st">"bioinformatics"</span>, <span class="st">"infectious_diseases"</span><span class="op">)</span>, </span>
 <span>                model<span class="op">=</span><span class="st">"llama3.1"</span><span class="op">)</span></span></code></pre></div>
-<p>Example HTML report generated from the bioRxiv RSS feed on August 6, 2024:</p>
-<p><img src="reference/figures/report_screenshot.png" width="100%"></p>
+<p>Example HTML report generated from bioRxiv (bioinformatics) and infectious diseases (medRxiv) subjects on September 25, 2024:</p>
+<p><img src="reference/figures/report_screenshot.jpg" width="100%"></p>
 </div>
 <div class="section level3">
 <h3 id="details">Details<a class="anchor" aria-label="anchor" href="#details"></a>
 </h3>
-<p>The <code><a href="reference/get_preprints.html">get_preprints()</a></code> function retrieves preprints from bioRxiv’s RSS feeds. You pass one or more subjects to the <code>subject</code> argument.</p>
+<p>The <code><a href="reference/get_preprints.html">get_preprints()</a></code> function retrieves preprints from the RSS feed of either bioRxiv or medRxiv, based on the subject you provided. You pass one or more subjects to the <code>subject</code> argument.</p>
 <div class="sourceCode" id="cb8"><pre class="downlit sourceCode r">
 <code class="sourceCode R"><span><span class="va">pp</span> <span class="op">&lt;-</span> <span class="fu"><a href="reference/get_preprints.html">get_preprints</a></span><span class="op">(</span>subject<span class="op">=</span><span class="fu"><a href="https://rdrr.io/r/base/c.html" class="external-link">c</a></span><span class="op">(</span><span class="st">"bioinformatics"</span>, </span>
-<span>                              <span class="st">"genomics"</span>, </span>
-<span>                              <span class="st">"synthetic_biology"</span><span class="op">)</span><span class="op">)</span></span>
-<span><span class="va">pp</span></span></code></pre></div>
-<div class="sourceCode" id="cb9"><pre class="sourceCode R"><code class="sourceCode r"><span id="cb9-1"><a href="#cb9-1" tabindex="-1"></a><span class="co">#&gt; # A tibble: 90 × 4</span></span>
-<span id="cb9-2"><a href="#cb9-2" tabindex="-1"></a><span class="co">#&gt;    subject        title                                           url   abstract</span></span>
-<span id="cb9-3"><a href="#cb9-3" tabindex="-1"></a><span class="co">#&gt;    &lt;chr&gt;          &lt;chr&gt;                                           &lt;chr&gt; &lt;chr&gt;   </span></span>
-<span id="cb9-4"><a href="#cb9-4" tabindex="-1"></a><span class="co">#&gt;  1 bioinformatics Integrity and miss grouping as support for clu… http… "The hi…</span></span>
-<span id="cb9-5"><a href="#cb9-5" tabindex="-1"></a><span class="co">#&gt;  2 bioinformatics Sainsc: a computational tool for segmentation-… http… "Spatia…</span></span>
-<span id="cb9-6"><a href="#cb9-6" tabindex="-1"></a><span class="co">#&gt;  3 bioinformatics BRACE: A novel Bayesian-based imputation appro… http… "Bayesi…</span></span>
-<span id="cb9-7"><a href="#cb9-7" tabindex="-1"></a><span class="co">#&gt;  4 bioinformatics Topological embedding and directional feature … http… "Cancer…</span></span>
-<span id="cb9-8"><a href="#cb9-8" tabindex="-1"></a><span class="co">#&gt;  5 bioinformatics SeuratExtend: Streamlining Single-Cell RNA-Seq… http… "Single…</span></span>
-<span id="cb9-9"><a href="#cb9-9" tabindex="-1"></a><span class="co">#&gt;  6 bioinformatics An Evolutionary Statistics Toolkit for Simplif… http… "We pre…</span></span>
-<span id="cb9-10"><a href="#cb9-10" tabindex="-1"></a><span class="co">#&gt;  7 bioinformatics A map of integrated cis-regulatory elements en… http… "Cis-re…</span></span>
-<span id="cb9-11"><a href="#cb9-11" tabindex="-1"></a><span class="co">#&gt;  8 bioinformatics MOSTPLAS: A Self-correction Multi-label Learni… http… "Plasmi…</span></span>
-<span id="cb9-12"><a href="#cb9-12" tabindex="-1"></a><span class="co">#&gt;  9 bioinformatics Bootstrap Evaluation of Association Matrices (… http… "Motiva…</span></span>
-<span id="cb9-13"><a href="#cb9-13" tabindex="-1"></a><span class="co">#&gt; 10 bioinformatics Thermodynamic modeling of Csr/Rsm- RNA interac… http… "Backgr…</span></span>
-<span id="cb9-14"><a href="#cb9-14" tabindex="-1"></a><span class="co">#&gt; # ℹ 80 more rows</span></span></code></pre></div>
+<span>                              <span class="st">"infectious_diseases"</span><span class="op">)</span><span class="op">)</span></span>
+<span><span class="fu"><a href="https://rdrr.io/r/utils/head.html" class="external-link">head</a></span><span class="op">(</span><span class="va">pp</span><span class="op">)</span></span>
+<span><span class="fu"><a href="https://rdrr.io/r/utils/head.html" class="external-link">tail</a></span><span class="op">(</span><span class="va">pp</span><span class="op">)</span></span></code></pre></div>
+<div class="sourceCode" id="cb9"><pre class="sourceCode R"><code class="sourceCode r"><span id="cb9-1"><a href="#cb9-1" tabindex="-1"></a><span class="co">#&gt; # A tibble: 6 × 5</span></span>
+<span id="cb9-2"><a href="#cb9-2" tabindex="-1"></a><span class="co">#&gt;   source  subject        title                                    url   abstract</span></span>
+<span id="cb9-3"><a href="#cb9-3" tabindex="-1"></a><span class="co">#&gt;   &lt;chr&gt;   &lt;chr&gt;          &lt;chr&gt;                                    &lt;chr&gt; &lt;chr&gt;   </span></span>
+<span id="cb9-4"><a href="#cb9-4" tabindex="-1"></a><span class="co">#&gt; 1 bioRxiv bioinformatics MedGraphNet: Leveraging Multi-Relationa… http… Genetic…</span></span>
+<span id="cb9-5"><a href="#cb9-5" tabindex="-1"></a><span class="co">#&gt; 2 bioRxiv bioinformatics High-throughput bacterial aggregation a… http… The com…</span></span>
+<span id="cb9-6"><a href="#cb9-6" tabindex="-1"></a><span class="co">#&gt; 3 bioRxiv bioinformatics scParadise: Tunable highly accurate mul… http… scRNA-s…</span></span>
+<span id="cb9-7"><a href="#cb9-7" tabindex="-1"></a><span class="co">#&gt; 4 bioRxiv bioinformatics Camera Paths, Modeling, and Image Proce… http… The enh…</span></span>
+<span id="cb9-8"><a href="#cb9-8" tabindex="-1"></a><span class="co">#&gt; 5 bioRxiv bioinformatics dScaff - an automatic bioinformatics fr… http… Rapid e…</span></span>
+<span id="cb9-9"><a href="#cb9-9" tabindex="-1"></a><span class="co">#&gt; 6 bioRxiv bioinformatics Jaeger: an accurate and fast deep-learn… http… Abstrac…</span></span>
+<span id="cb9-10"><a href="#cb9-10" tabindex="-1"></a><span class="co">#&gt; # A tibble: 6 × 5</span></span>
+<span id="cb9-11"><a href="#cb9-11" tabindex="-1"></a><span class="co">#&gt;   source  subject             title                               url   abstract</span></span>
+<span id="cb9-12"><a href="#cb9-12" tabindex="-1"></a><span class="co">#&gt;   &lt;chr&gt;   &lt;chr&gt;               &lt;chr&gt;                               &lt;chr&gt; &lt;chr&gt;   </span></span>
+<span id="cb9-13"><a href="#cb9-13" tabindex="-1"></a><span class="co">#&gt; 1 medRxiv infectious_diseases Reactogenicity and immunogenicity … http… "The re…</span></span>
+<span id="cb9-14"><a href="#cb9-14" tabindex="-1"></a><span class="co">#&gt; 2 medRxiv infectious_diseases A next generation CRISPR diagnosti… http… "The WH…</span></span>
+<span id="cb9-15"><a href="#cb9-15" tabindex="-1"></a><span class="co">#&gt; 3 medRxiv infectious_diseases Hospital-onset bacteraemia and fun… http… "Backgr…</span></span>
+<span id="cb9-16"><a href="#cb9-16" tabindex="-1"></a><span class="co">#&gt; 4 medRxiv infectious_diseases Co-circulating pathogens of humans… http… "Histor…</span></span>
+<span id="cb9-17"><a href="#cb9-17" tabindex="-1"></a><span class="co">#&gt; 5 medRxiv infectious_diseases Integration of Group A Streptococc… http… "The Ca…</span></span>
+<span id="cb9-18"><a href="#cb9-18" tabindex="-1"></a><span class="co">#&gt; 6 medRxiv infectious_diseases Deep Learning Models for Predictin… http… "The Nu…</span></span></code></pre></div>
 <p>The <code><a href="reference/add_prompt.html">add_prompt()</a></code> function adds a prompt to each preprint that will be used to prompt the model.</p>
 <div class="sourceCode" id="cb10"><pre class="downlit sourceCode r">
 <code class="sourceCode R"><span><span class="va">pp</span> <span class="op">&lt;-</span> <span class="va">pp</span> <span class="op">|&gt;</span> <span class="fu"><a href="reference/add_prompt.html">add_prompt</a></span><span class="op">(</span><span class="op">)</span></span>
 <span><span class="va">pp</span></span></code></pre></div>
-<div class="sourceCode" id="cb11"><pre class="sourceCode R"><code class="sourceCode r"><span id="cb11-1"><a href="#cb11-1" tabindex="-1"></a><span class="co">#&gt; # A tibble: 90 × 5</span></span>
-<span id="cb11-2"><a href="#cb11-2" tabindex="-1"></a><span class="co">#&gt;    subject        title                                    url   abstract prompt</span></span>
-<span id="cb11-3"><a href="#cb11-3" tabindex="-1"></a><span class="co">#&gt;    &lt;chr&gt;          &lt;chr&gt;                                    &lt;chr&gt; &lt;chr&gt;    &lt;chr&gt; </span></span>
-<span id="cb11-4"><a href="#cb11-4" tabindex="-1"></a><span class="co">#&gt;  1 bioinformatics Integrity and miss grouping as support … http… "The hi… "I am…</span></span>
-<span id="cb11-5"><a href="#cb11-5" tabindex="-1"></a><span class="co">#&gt;  2 bioinformatics Sainsc: a computational tool for segmen… http… "Spatia… "I am…</span></span>
-<span id="cb11-6"><a href="#cb11-6" tabindex="-1"></a><span class="co">#&gt;  3 bioinformatics BRACE: A novel Bayesian-based imputatio… http… "Bayesi… "I am…</span></span>
-<span id="cb11-7"><a href="#cb11-7" tabindex="-1"></a><span class="co">#&gt;  4 bioinformatics Topological embedding and directional f… http… "Cancer… "I am…</span></span>
-<span id="cb11-8"><a href="#cb11-8" tabindex="-1"></a><span class="co">#&gt;  5 bioinformatics SeuratExtend: Streamlining Single-Cell … http… "Single… "I am…</span></span>
-<span id="cb11-9"><a href="#cb11-9" tabindex="-1"></a><span class="co">#&gt;  6 bioinformatics An Evolutionary Statistics Toolkit for … http… "We pre… "I am…</span></span>
-<span id="cb11-10"><a href="#cb11-10" tabindex="-1"></a><span class="co">#&gt;  7 bioinformatics A map of integrated cis-regulatory elem… http… "Cis-re… "I am…</span></span>
-<span id="cb11-11"><a href="#cb11-11" tabindex="-1"></a><span class="co">#&gt;  8 bioinformatics MOSTPLAS: A Self-correction Multi-label… http… "Plasmi… "I am…</span></span>
-<span id="cb11-12"><a href="#cb11-12" tabindex="-1"></a><span class="co">#&gt;  9 bioinformatics Bootstrap Evaluation of Association Mat… http… "Motiva… "I am…</span></span>
-<span id="cb11-13"><a href="#cb11-13" tabindex="-1"></a><span class="co">#&gt; 10 bioinformatics Thermodynamic modeling of Csr/Rsm- RNA … http… "Backgr… "I am…</span></span>
-<span id="cb11-14"><a href="#cb11-14" tabindex="-1"></a><span class="co">#&gt; # ℹ 80 more rows</span></span></code></pre></div>
+<div class="sourceCode" id="cb11"><pre class="sourceCode R"><code class="sourceCode r"><span id="cb11-1"><a href="#cb11-1" tabindex="-1"></a><span class="co">#&gt; # A tibble: 60 × 6</span></span>
+<span id="cb11-2"><a href="#cb11-2" tabindex="-1"></a><span class="co">#&gt;    source  subject        title                            url   abstract prompt</span></span>
+<span id="cb11-3"><a href="#cb11-3" tabindex="-1"></a><span class="co">#&gt;    &lt;chr&gt;   &lt;chr&gt;          &lt;chr&gt;                            &lt;chr&gt; &lt;chr&gt;    &lt;chr&gt; </span></span>
+<span id="cb11-4"><a href="#cb11-4" tabindex="-1"></a><span class="co">#&gt;  1 bioRxiv bioinformatics MedGraphNet: Leveraging Multi-R… http… Genetic… I am …</span></span>
+<span id="cb11-5"><a href="#cb11-5" tabindex="-1"></a><span class="co">#&gt;  2 bioRxiv bioinformatics High-throughput bacterial aggre… http… The com… I am …</span></span>
+<span id="cb11-6"><a href="#cb11-6" tabindex="-1"></a><span class="co">#&gt;  3 bioRxiv bioinformatics scParadise: Tunable highly accu… http… scRNA-s… I am …</span></span>
+<span id="cb11-7"><a href="#cb11-7" tabindex="-1"></a><span class="co">#&gt;  4 bioRxiv bioinformatics Camera Paths, Modeling, and Ima… http… The enh… I am …</span></span>
+<span id="cb11-8"><a href="#cb11-8" tabindex="-1"></a><span class="co">#&gt;  5 bioRxiv bioinformatics dScaff - an automatic bioinform… http… Rapid e… I am …</span></span>
+<span id="cb11-9"><a href="#cb11-9" tabindex="-1"></a><span class="co">#&gt;  6 bioRxiv bioinformatics Jaeger: an accurate and fast de… http… Abstrac… I am …</span></span>
+<span id="cb11-10"><a href="#cb11-10" tabindex="-1"></a><span class="co">#&gt;  7 bioRxiv bioinformatics AI-Augmented R-Group Exploratio… http… Efficie… I am …</span></span>
+<span id="cb11-11"><a href="#cb11-11" tabindex="-1"></a><span class="co">#&gt;  8 bioRxiv bioinformatics OPLS-based Multiclass Classific… http… Multicl… I am …</span></span>
+<span id="cb11-12"><a href="#cb11-12" tabindex="-1"></a><span class="co">#&gt;  9 bioRxiv bioinformatics STANCE: a unified statistical m… http… A signi… I am …</span></span>
+<span id="cb11-13"><a href="#cb11-13" tabindex="-1"></a><span class="co">#&gt; 10 bioRxiv bioinformatics AsaruSim: a single-cell and spa… http… Motivat… I am …</span></span>
+<span id="cb11-14"><a href="#cb11-14" tabindex="-1"></a><span class="co">#&gt; # ℹ 50 more rows</span></span></code></pre></div>
 <p>Let’s take a look at one of these prompts:</p>
 <blockquote>
 <p>I am giving you a paper’s title and abstract. Summarize the paper in as many sentences as I instruct. Do not include any preamble text. Just give me the summary.</p>
@@ -157,26 +161,26 @@ <h3 id="details">Details<a class="anchor" aria-label="anchor" href="#details"></
 <p>The <code><a href="reference/add_summary.html">add_summary()</a></code> function uses a locally running LLM available through Ollama to summarize the preprint. Let’s add the summary. Notice that we can do this all in a single pipeline. This takes a few minutes!</p>
 <div class="sourceCode" id="cb12"><pre class="downlit sourceCode r">
 <code class="sourceCode R"><span><span class="va">pp</span> <span class="op">&lt;-</span> </span>
-<span>  <span class="fu"><a href="reference/get_preprints.html">get_preprints</a></span><span class="op">(</span>subject<span class="op">=</span><span class="fu"><a href="https://rdrr.io/r/base/c.html" class="external-link">c</a></span><span class="op">(</span><span class="st">"bioinformatics"</span>, <span class="st">"genomics"</span>, <span class="st">"synthetic_biology"</span><span class="op">)</span><span class="op">)</span> <span class="op">|&gt;</span> </span>
+<span>  <span class="fu"><a href="reference/get_preprints.html">get_preprints</a></span><span class="op">(</span>subject<span class="op">=</span><span class="fu"><a href="https://rdrr.io/r/base/c.html" class="external-link">c</a></span><span class="op">(</span><span class="st">"bioinformatics"</span>, <span class="st">"infectious_diseases"</span><span class="op">)</span><span class="op">)</span> <span class="op">|&gt;</span> </span>
 <span>  <span class="fu"><a href="reference/add_prompt.html">add_prompt</a></span><span class="op">(</span><span class="op">)</span> <span class="op">|&gt;</span> </span>
-<span>  <span class="fu"><a href="reference/add_summary.html">add_summary</a></span><span class="op">(</span>model<span class="op">=</span><span class="st">"llama3.1"</span><span class="op">)</span></span></code></pre></div>
+<span>  <span class="fu"><a href="reference/add_summary.html">add_summary</a></span><span class="op">(</span>model<span class="op">=</span><span class="st">"llama3.2"</span><span class="op">)</span></span></code></pre></div>
 <p>Let’s take a look at the results:</p>
 <div class="sourceCode" id="cb13"><pre class="downlit sourceCode r">
 <code class="sourceCode R"><span><span class="va">pp</span></span>
-<span><span class="co">#&gt; # A tibble: 90 × 6</span></span>
-<span><span class="co">#&gt;    subject        title                            url   abstract prompt summary</span></span>
-<span><span class="co">#&gt;    &lt;chr&gt;          &lt;chr&gt;                            &lt;chr&gt; &lt;chr&gt;    &lt;chr&gt;  &lt;chr&gt;  </span></span>
-<span><span class="co">#&gt;  1 bioinformatics Integrity and miss grouping as … http… "The hi… "I am… "The p…</span></span>
-<span><span class="co">#&gt;  2 bioinformatics Sainsc: a computational tool fo… http… "Spatia… "I am… "Sains…</span></span>
-<span><span class="co">#&gt;  3 bioinformatics BRACE: A novel Bayesian-based i… http… "Bayesi… "I am… "Alter…</span></span>
-<span><span class="co">#&gt;  4 bioinformatics Topological embedding and direc… http… "Cancer… "I am… "Resea…</span></span>
-<span><span class="co">#&gt;  5 bioinformatics SeuratExtend: Streamlining Sing… http… "Single… "I am… "Seura…</span></span>
-<span><span class="co">#&gt;  6 bioinformatics An Evolutionary Statistics Tool… http… "We pre… "I am… "The \…</span></span>
-<span><span class="co">#&gt;  7 bioinformatics A map of integrated cis-regulat… http… "Cis-re… "I am… "The a…</span></span>
-<span><span class="co">#&gt;  8 bioinformatics MOSTPLAS: A Self-correction Mul… http… "Plasmi… "I am… "Plasm…</span></span>
-<span><span class="co">#&gt;  9 bioinformatics Bootstrap Evaluation of Associa… http… "Motiva… "I am… "The a…</span></span>
-<span><span class="co">#&gt; 10 bioinformatics Thermodynamic modeling of Csr/R… http… "Backgr… "I am… "Resea…</span></span>
-<span><span class="co">#&gt; # ℹ 80 more rows</span></span></code></pre></div>
+<span><span class="co">#&gt; # A tibble: 60 × 7</span></span>
+<span><span class="co">#&gt;    source  subject        title                    url   abstract prompt summary</span></span>
+<span><span class="co">#&gt;    &lt;chr&gt;   &lt;chr&gt;          &lt;chr&gt;                    &lt;chr&gt; &lt;chr&gt;    &lt;chr&gt;  &lt;chr&gt;  </span></span>
+<span><span class="co">#&gt;  1 bioRxiv bioinformatics MedGraphNet: Leveraging… http… Genetic… I am … MedGra…</span></span>
+<span><span class="co">#&gt;  2 bioRxiv bioinformatics High-throughput bacteri… http… The com… I am … The co…</span></span>
+<span><span class="co">#&gt;  3 bioRxiv bioinformatics scParadise: Tunable hig… http… scRNA-s… I am … scAdam…</span></span>
+<span><span class="co">#&gt;  4 bioRxiv bioinformatics Camera Paths, Modeling,… http… The enh… I am … ArtiaX…</span></span>
+<span><span class="co">#&gt;  5 bioRxiv bioinformatics dScaff - an automatic b… http… Rapid e… I am … dScaff…</span></span>
+<span><span class="co">#&gt;  6 bioRxiv bioinformatics Jaeger: an accurate and… http… Abstrac… I am … Jaeger…</span></span>
+<span><span class="co">#&gt;  7 bioRxiv bioinformatics AI-Augmented R-Group Ex… http… Efficie… I am … The pa…</span></span>
+<span><span class="co">#&gt;  8 bioRxiv bioinformatics OPLS-based Multiclass C… http… Multicl… I am … OPLS-D…</span></span>
+<span><span class="co">#&gt;  9 bioRxiv bioinformatics STANCE: a unified stati… http… A signi… I am … STANCE…</span></span>
+<span><span class="co">#&gt; 10 bioRxiv bioinformatics AsaruSim: a single-cell… http… Motivat… I am … AsaruS…</span></span>
+<span><span class="co">#&gt; # ℹ 50 more rows</span></span></code></pre></div>
 <p>Let’s look at one of those summaries. Here’s the summary for the SeuratExtend paper (abstract above):</p>
 <blockquote>
 <p>SeuratExtend is an R package that integrates essential tools and databases for single-cell RNA sequencing (scRNA-seq) data analysis, streamlining the process through a user-friendly interface. The package offers various analyses, including functional enrichment and gene regulatory network reconstruction, and seamlessly integrates multiple databases and popular Python tools.</p>
@@ -184,11 +188,11 @@ <h3 id="details">Details<a class="anchor" aria-label="anchor" href="#details"></
 <p>The <code><a href="reference/biorecap_report.html">biorecap_report()</a></code> function runs this code in an RMarkdown template, writing the resulting HTML and CSV file with results to the current working directory.</p>
 <div class="sourceCode" id="cb14"><pre class="downlit sourceCode r">
 <code class="sourceCode R"><span><span class="fu"><a href="reference/biorecap_report.html">biorecap_report</a></span><span class="op">(</span>output_dir<span class="op">=</span><span class="st">"."</span>, </span>
-<span>                subject<span class="op">=</span><span class="fu"><a href="https://rdrr.io/r/base/c.html" class="external-link">c</a></span><span class="op">(</span><span class="st">"bioinformatics"</span>, <span class="st">"genomics"</span>, <span class="st">"synthetic_biology"</span><span class="op">)</span>, </span>
-<span>                model<span class="op">=</span><span class="st">"llama3.1"</span><span class="op">)</span></span></code></pre></div>
-<p>The built-in <code>subjects</code> vector contains all the available bioRxiv subjects.</p>
+<span>                subject<span class="op">=</span><span class="fu"><a href="https://rdrr.io/r/base/c.html" class="external-link">c</a></span><span class="op">(</span><span class="st">"bioinformatics"</span>, <span class="st">"infectious_diseases"</span><span class="op">)</span>, </span>
+<span>                model<span class="op">=</span><span class="st">"llama3.2"</span><span class="op">)</span></span></code></pre></div>
+<p>The built-in <code>subjects</code> is a list with vectors containing all the available bioRxiv and medRxiv subjects.</p>
 <div class="sourceCode" id="cb15"><pre class="downlit sourceCode r">
-<code class="sourceCode R"><span><span class="va">subjects</span></span>
+<code class="sourceCode R"><span><span class="va">subjects</span><span class="op">$</span><span class="va">biorxiv</span></span>
 <span><span class="co">#&gt;  [1] "all"                                   </span></span>
 <span><span class="co">#&gt;  [2] "animal_behavior_and_cognition"         </span></span>
 <span><span class="co">#&gt;  [3] "biochemistry"                          </span></span>
@@ -215,12 +219,67 @@ <h3 id="details">Details<a class="anchor" aria-label="anchor" href="#details"></
 <span><span class="co">#&gt; [24] "scientific_communication_and_education"</span></span>
 <span><span class="co">#&gt; [25] "synthetic_biology"                     </span></span>
 <span><span class="co">#&gt; [26] "systems_biology"                       </span></span>
-<span><span class="co">#&gt; [27] "zoology"</span></span></code></pre></div>
+<span><span class="co">#&gt; [27] "zoology"</span></span>
+<span><span class="va">subjects</span><span class="op">$</span><span class="va">medrxiv</span></span>
+<span><span class="co">#&gt;  [1] "all"                                         </span></span>
+<span><span class="co">#&gt;  [2] "addiction_medicine"                          </span></span>
+<span><span class="co">#&gt;  [3] "allergy_and_immunology"                      </span></span>
+<span><span class="co">#&gt;  [4] "anesthesia"                                  </span></span>
+<span><span class="co">#&gt;  [5] "cardiovascular_medicine"                     </span></span>
+<span><span class="co">#&gt;  [6] "dentistry_and_oral_medicine"                 </span></span>
+<span><span class="co">#&gt;  [7] "dermatology"                                 </span></span>
+<span><span class="co">#&gt;  [8] "dermatology"                                 </span></span>
+<span><span class="co">#&gt;  [9] "endocrinology"                               </span></span>
+<span><span class="co">#&gt; [10] "epidemiology"                                </span></span>
+<span><span class="co">#&gt; [11] "ecology"                                     </span></span>
+<span><span class="co">#&gt; [12] "epidemiology"                                </span></span>
+<span><span class="co">#&gt; [13] "forensic_medicine"                           </span></span>
+<span><span class="co">#&gt; [14] "gastroenterology"                            </span></span>
+<span><span class="co">#&gt; [15] "genetic_and_genomic_medicine"                </span></span>
+<span><span class="co">#&gt; [16] "geriatric_medicine"                          </span></span>
+<span><span class="co">#&gt; [17] "health_economics"                            </span></span>
+<span><span class="co">#&gt; [18] "health_informatics"                          </span></span>
+<span><span class="co">#&gt; [19] "health_policy"                               </span></span>
+<span><span class="co">#&gt; [20] "health_systems_and_quality_improvement"      </span></span>
+<span><span class="co">#&gt; [21] "hematology"                                  </span></span>
+<span><span class="co">#&gt; [22] "hivaids"                                     </span></span>
+<span><span class="co">#&gt; [23] "infectious_diseases"                         </span></span>
+<span><span class="co">#&gt; [24] "intensive_care_and_critical_care_medicine"   </span></span>
+<span><span class="co">#&gt; [25] "medical_education"                           </span></span>
+<span><span class="co">#&gt; [26] "medical_ethics"                              </span></span>
+<span><span class="co">#&gt; [27] "nephrology"                                  </span></span>
+<span><span class="co">#&gt; [28] "neurology"                                   </span></span>
+<span><span class="co">#&gt; [29] "nursing"                                     </span></span>
+<span><span class="co">#&gt; [30] "nutrition"                                   </span></span>
+<span><span class="co">#&gt; [31] "obstetrics_and_gynecology"                   </span></span>
+<span><span class="co">#&gt; [32] "occupational_and_environmental_health"       </span></span>
+<span><span class="co">#&gt; [33] "oncology"                                    </span></span>
+<span><span class="co">#&gt; [34] "ophthalmology"                               </span></span>
+<span><span class="co">#&gt; [35] "orthopedics"                                 </span></span>
+<span><span class="co">#&gt; [36] "otolaryngology"                              </span></span>
+<span><span class="co">#&gt; [37] "pain_medicine"                               </span></span>
+<span><span class="co">#&gt; [38] "palliative_medicine"                         </span></span>
+<span><span class="co">#&gt; [39] "pathology"                                   </span></span>
+<span><span class="co">#&gt; [40] "pediatrics"                                  </span></span>
+<span><span class="co">#&gt; [41] "pharmacology_and_therapeutics"               </span></span>
+<span><span class="co">#&gt; [42] "primary_care_research"                       </span></span>
+<span><span class="co">#&gt; [43] "psychiatry_and_clinical_psychology"          </span></span>
+<span><span class="co">#&gt; [44] "public_and_global_health"                    </span></span>
+<span><span class="co">#&gt; [45] "radiology_and_imaging"                       </span></span>
+<span><span class="co">#&gt; [46] "rehabilitation_medicine_and_physical_therapy"</span></span>
+<span><span class="co">#&gt; [47] "respiratory_medicine"                        </span></span>
+<span><span class="co">#&gt; [48] "rheumatology"                                </span></span>
+<span><span class="co">#&gt; [49] "sexual_and_reproductive_health"              </span></span>
+<span><span class="co">#&gt; [50] "sports_medicine"                             </span></span>
+<span><span class="co">#&gt; [51] "surgery"                                     </span></span>
+<span><span class="co">#&gt; [52] "toxicology"                                  </span></span>
+<span><span class="co">#&gt; [53] "transplantation"                             </span></span>
+<span><span class="co">#&gt; [54] "urology"</span></span></code></pre></div>
 <p>You could create a report for <em>all</em> subjects like this (note, this could take some time):</p>
 <div class="sourceCode" id="cb16"><pre class="downlit sourceCode r">
 <code class="sourceCode R"><span><span class="fu"><a href="reference/biorecap_report.html">biorecap_report</a></span><span class="op">(</span>output_dir<span class="op">=</span><span class="st">"."</span>, </span>
 <span>                subject<span class="op">=</span><span class="va">subjects</span>, </span>
-<span>                model<span class="op">=</span><span class="st">"llama3.1"</span><span class="op">)</span></span></code></pre></div>
+<span>                model<span class="op">=</span><span class="st">"llama3.2"</span><span class="op">)</span></span></code></pre></div>
 </div>
 </div>
 </div>
@@ -279,7 +338,7 @@ <h2 data-toc-skip>Dev status</h2>
 </div>
 
 <div class="pkgdown-footer-right">
-  <p>Site built with <a href="https://pkgdown.r-lib.org/" class="external-link">pkgdown</a> 2.1.0.</p>
+  <p>Site built with <a href="https://pkgdown.r-lib.org/" class="external-link">pkgdown</a> 2.1.1.</p>
 </div>
 
     </footer>
diff --git a/news/index.html b/news/index.html
index 967bcad..d1c743f 100644
--- a/news/index.html
+++ b/news/index.html
@@ -7,7 +7,7 @@
 
     <a class="navbar-brand me-2" href="../index.html">biorecap</a>
 
-    <small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">0.1.1</small>
+    <small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">0.2.0</small>
 
 
     <button class="navbar-toggler" type="button" data-bs-toggle="collapse" data-bs-target="#navbar" aria-controls="navbar" aria-expanded="false" aria-label="Toggle navigation">
@@ -28,9 +28,16 @@
 <div class="row">
   <main id="main" class="col-md-9"><div class="page-header">
       <img src="../logo.png" class="logo" alt=""><h1>Changelog</h1>
-      <small>Source: <a href="https://github.com/stephenturner/biorecap/blob/v0.1.1/NEWS.md" class="external-link"><code>NEWS.md</code></a></small>
+      <small>Source: <a href="https://github.com/stephenturner/biorecap/blob/main/NEWS.md" class="external-link"><code>NEWS.md</code></a></small>
     </div>
 
+    <div class="section level2">
+<h2 class="pkg-version" data-toc-text="0.2.0" id="biorecap-020">biorecap 0.2.0<a class="anchor" aria-label="anchor" href="#biorecap-020"></a></h2>
+<ul><li>Added medRxiv support. The <code><a href="../reference/get_preprints.html">get_preprints()</a></code> function will now pull from either the bioRxiv or medRxiv RSS feed depending on the subject passed to it. All downstream functions and reporting updated to reflect this change (fixes <a href="https://github.com/stephenturner/biorecap/issues/5" class="external-link">#5</a>).</li>
+<li>Changed default model to llama 3.2 3B.</li>
+<li>Added new source column for the returned preprints indicating whether the preprint came from bioRxiv or medRxiv.</li>
+<li>Updated tests.</li>
+</ul></div>
     <div class="section level2">
 <h2 class="pkg-version" data-toc-text="0.1.1" id="biorecap-011">biorecap 0.1.1<a class="anchor" aria-label="anchor" href="#biorecap-011"></a></h2>
 <ul><li>Fix bug in <code><a href="../reference/add_summary.html">add_summary()</a></code> caused by upstream changes in ollamar (fixes <a href="https://github.com/stephenturner/biorecap/issues/1" class="external-link">#1</a>).</li>
@@ -49,7 +56,7 @@ <h2 class="pkg-version" data-toc-text="0.1.0" id="biorecap-010">biorecap 0.1.0<a
 </div>
 
 <div class="pkgdown-footer-right">
-  <p>Site built with <a href="https://pkgdown.r-lib.org/" class="external-link">pkgdown</a> 2.1.0.</p>
+  <p>Site built with <a href="https://pkgdown.r-lib.org/" class="external-link">pkgdown</a> 2.1.1.</p>
 </div>
 
     </footer></div>
diff --git a/pkgdown.js b/pkgdown.js
index 9757bf9..1a99c65 100644
--- a/pkgdown.js
+++ b/pkgdown.js
@@ -152,3 +152,11 @@ async function searchFuse(query, callback) {
   });
   });
 })(window.jQuery || window.$)
+
+document.addEventListener('keydown', function(event) {
+  // Check if the pressed key is '/'
+  if (event.key === '/') {
+    event.preventDefault();  // Prevent any default action associated with the '/' key
+    document.getElementById('search-input').focus();  // Set focus to the search input
+  }
+});
diff --git a/pkgdown.yml b/pkgdown.yml
index ebfaf10..e2330da 100644
--- a/pkgdown.yml
+++ b/pkgdown.yml
@@ -1,8 +1,8 @@
 pandoc: 3.1.11
-pkgdown: 2.1.0
+pkgdown: 2.1.1
 pkgdown_sha: ~
 articles: {}
-last_built: 2024-08-26T00:09Z
+last_built: 2024-09-25T20:56Z
 urls:
   reference: https://stephenturner.github.io/biorecap/reference
   article: https://stephenturner.github.io/biorecap/articles
diff --git a/reference/add_prompt.html b/reference/add_prompt.html
index ad31353..cf2545f 100644
--- a/reference/add_prompt.html
+++ b/reference/add_prompt.html
@@ -7,7 +7,7 @@
 
     <a class="navbar-brand me-2" href="../index.html">biorecap</a>
 
-    <small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">0.1.1</small>
+    <small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">0.2.0</small>
 
 
     <button class="navbar-toggler" type="button" data-bs-toggle="collapse" data-bs-target="#navbar" aria-controls="navbar" aria-expanded="false" aria-label="Toggle navigation">
@@ -28,7 +28,7 @@
 <div class="row">
   <main id="main" class="col-md-9"><div class="page-header">
       <img src="../logo.png" class="logo" alt=""><h1>Add prompt to a data frame of preprints</h1>
-      <small class="dont-index">Source: <a href="https://github.com/stephenturner/biorecap/blob/v0.1.1/R/biorecap.R" class="external-link"><code>R/biorecap.R</code></a></small>
+      <small class="dont-index">Source: <a href="https://github.com/stephenturner/biorecap/blob/main/R/biorecap.R" class="external-link"><code>R/biorecap.R</code></a></small>
       <div class="d-none name"><code>add_prompt.Rd</code></div>
     </div>
 
@@ -55,7 +55,7 @@ <h2 id="arguments">Arguments<a class="anchor" aria-label="anchor" href="#argumen
 </dl></div>
     <div class="section level2">
     <h2 id="value">Value<a class="anchor" aria-label="anchor" href="#value"></a></h2>
-    <p>A data frame of bioRxiv preprints with a prompt added.</p>
+    <p>A data frame of preprints with a prompt added.</p>
     </div>
     <div class="section level2">
     <h2 id="see-also">See also<a class="anchor" aria-label="anchor" href="#see-also"></a></h2>
@@ -67,19 +67,19 @@ <h2 id="ref-examples">Examples<a class="anchor" aria-label="anchor" href="#ref-e
     <div class="sourceCode"><pre class="sourceCode r"><code><span class="r-in"><span><span class="va">preprints</span> <span class="op">&lt;-</span> <span class="fu"><a href="get_preprints.html">get_preprints</a></span><span class="op">(</span>subject<span class="op">=</span><span class="fu"><a href="https://rdrr.io/r/base/c.html" class="external-link">c</a></span><span class="op">(</span><span class="st">"bioinformatics"</span>, <span class="st">"genomics"</span><span class="op">)</span><span class="op">)</span></span></span>
 <span class="r-in"><span><span class="va">preprints</span> <span class="op">&lt;-</span> <span class="fu">add_prompt</span><span class="op">(</span><span class="va">preprints</span><span class="op">)</span></span></span>
 <span class="r-in"><span><span class="va">preprints</span></span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="color: #949494;"># A tibble: 60 × 5</span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span>    subject        title                                    url   abstract prompt</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span>    <span style="color: #949494; font-style: italic;">&lt;chr&gt;</span>          <span style="color: #949494; font-style: italic;">&lt;chr&gt;</span>                                    <span style="color: #949494; font-style: italic;">&lt;chr&gt;</span> <span style="color: #949494; font-style: italic;">&lt;chr&gt;</span>    <span style="color: #949494; font-style: italic;">&lt;chr&gt;</span> </span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="color: #BCBCBC;"> 1</span> bioinformatics <span style="color: #949494;">"</span>PHIStruct: Improving phage-host intera… http… Recent … <span style="color: #949494;">"</span>I am…</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="color: #BCBCBC;"> 2</span> bioinformatics <span style="color: #949494;">"</span>BGC Atlas: A Web Resource for Explorin… http… Seconda… <span style="color: #949494;">"</span>I am…</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="color: #BCBCBC;"> 3</span> bioinformatics <span style="color: #949494;">"</span>Enrichment analysis for spatial and si… http… Imaging… <span style="color: #949494;">"</span>I am…</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="color: #BCBCBC;"> 4</span> bioinformatics <span style="color: #949494;">"</span>SpatialLeiden - Spatially-aware Leiden… http… Cluster… <span style="color: #949494;">"</span>I am…</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="color: #BCBCBC;"> 5</span> bioinformatics <span style="color: #949494;">"</span>Protein sequence classification using … http… Protein… <span style="color: #949494;">"</span>I am…</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="color: #BCBCBC;"> 6</span> bioinformatics <span style="color: #949494;">"</span>A multi-omics approach to identify del… http… Crops l… <span style="color: #949494;">"</span>I am…</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="color: #BCBCBC;"> 7</span> bioinformatics <span style="color: #949494;">"</span>Automatic crystal identification for c… http… Crystal… <span style="color: #949494;">"</span>I am…</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="color: #BCBCBC;"> 8</span> bioinformatics <span style="color: #949494;">"</span>Solving the \"Blind men and the elepha… http… Biologi… <span style="color: #949494;">"</span>I am…</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="color: #BCBCBC;"> 9</span> bioinformatics <span style="color: #949494;">"</span>Evolutionary mismatch between nuclear … http… Serrano… <span style="color: #949494;">"</span>I am…</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="color: #BCBCBC;">10</span> bioinformatics <span style="color: #949494;">"</span>Predicting small-molecule inhibition o… http… Protein… <span style="color: #949494;">"</span>I am…</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="color: #949494;"># A tibble: 60 × 6</span></span>
+<span class="r-out co"><span class="r-pr">#&gt;</span>    source  subject        title                            url   abstract prompt</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span>    <span style="color: #949494; font-style: italic;">&lt;chr&gt;</span>   <span style="color: #949494; font-style: italic;">&lt;chr&gt;</span>          <span style="color: #949494; font-style: italic;">&lt;chr&gt;</span>                            <span style="color: #949494; font-style: italic;">&lt;chr&gt;</span> <span style="color: #949494; font-style: italic;">&lt;chr&gt;</span>    <span style="color: #949494; font-style: italic;">&lt;chr&gt;</span> </span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="color: #BCBCBC;"> 1</span> bioRxiv bioinformatics SeaMoon: Prediction of molecula… http… How pro… <span style="color: #949494;">"</span>I am…</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="color: #BCBCBC;"> 2</span> bioRxiv bioinformatics Clustering individuals using IN… http… Motivat… <span style="color: #949494;">"</span>I am…</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="color: #BCBCBC;"> 3</span> bioRxiv bioinformatics INSPIRE: interpretable, flexibl… http… Recent … <span style="color: #949494;">"</span>I am…</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="color: #BCBCBC;"> 4</span> bioRxiv bioinformatics MedGraphNet: Leveraging Multi-R… http… Genetic… <span style="color: #949494;">"</span>I am…</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="color: #BCBCBC;"> 5</span> bioRxiv bioinformatics High-throughput bacterial aggre… http… The com… <span style="color: #949494;">"</span>I am…</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="color: #BCBCBC;"> 6</span> bioRxiv bioinformatics scParadise: Tunable highly accu… http… scRNA-s… <span style="color: #949494;">"</span>I am…</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="color: #BCBCBC;"> 7</span> bioRxiv bioinformatics Camera Paths, Modeling, and Ima… http… The enh… <span style="color: #949494;">"</span>I am…</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="color: #BCBCBC;"> 8</span> bioRxiv bioinformatics dScaff - an automatic bioinform… http… Rapid e… <span style="color: #949494;">"</span>I am…</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="color: #BCBCBC;"> 9</span> bioRxiv bioinformatics Jaeger: an accurate and fast de… http… Abstrac… <span style="color: #949494;">"</span>I am…</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="color: #BCBCBC;">10</span> bioRxiv bioinformatics AI-Augmented R-Group Exploratio… http… Efficie… <span style="color: #949494;">"</span>I am…</span>
 <span class="r-out co"><span class="r-pr">#&gt;</span> <span style="color: #949494;"># ℹ 50 more rows</span></span>
 <span class="r-in"><span></span></span>
 </code></pre></div>
@@ -93,7 +93,7 @@ <h2 id="ref-examples">Examples<a class="anchor" aria-label="anchor" href="#ref-e
 </div>
 
 <div class="pkgdown-footer-right">
-  <p>Site built with <a href="https://pkgdown.r-lib.org/" class="external-link">pkgdown</a> 2.1.0.</p>
+  <p>Site built with <a href="https://pkgdown.r-lib.org/" class="external-link">pkgdown</a> 2.1.1.</p>
 </div>
 
     </footer></div>
diff --git a/reference/add_prompt_subject.html b/reference/add_prompt_subject.html
index 99fcb75..8c3fc9f 100644
--- a/reference/add_prompt_subject.html
+++ b/reference/add_prompt_subject.html
@@ -7,7 +7,7 @@
 
     <a class="navbar-brand me-2" href="../index.html">biorecap</a>
 
-    <small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">0.1.1</small>
+    <small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">0.2.0</small>
 
 
     <button class="navbar-toggler" type="button" data-bs-toggle="collapse" data-bs-target="#navbar" aria-controls="navbar" aria-expanded="false" aria-label="Toggle navigation">
@@ -28,7 +28,7 @@
 <div class="row">
   <main id="main" class="col-md-9"><div class="page-header">
       <img src="../logo.png" class="logo" alt=""><h1>Add prompts for an entire subject</h1>
-      <small class="dont-index">Source: <a href="https://github.com/stephenturner/biorecap/blob/v0.1.1/R/biorecap.R" class="external-link"><code>R/biorecap.R</code></a></small>
+      <small class="dont-index">Source: <a href="https://github.com/stephenturner/biorecap/blob/main/R/biorecap.R" class="external-link"><code>R/biorecap.R</code></a></small>
       <div class="d-none name"><code>add_prompt_subject.Rd</code></div>
     </div>
 
@@ -66,12 +66,11 @@ <h2 id="ref-examples">Examples<a class="anchor" aria-label="anchor" href="#ref-e
 <span class="r-in"><span>  <span class="fu">add_prompt_subject</span><span class="op">(</span><span class="op">)</span></span></span>
 <span class="r-wrn co"><span class="r-pr">#&gt;</span> <span class="warning">Warning: </span>Expecting a tibble of class 'preprints_prompt' returned from get_preprints() |&gt; add_prompt().</span>
 <span class="r-in"><span><span class="va">subjects</span></span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="color: #949494;"># A tibble: 3 × 2</span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span>   subject           prompt                                                      </span>
-<span class="r-out co"><span class="r-pr">#&gt;</span>   <span style="color: #949494; font-style: italic;">&lt;chr&gt;</span>             <span style="color: #949494; font-style: italic;">&lt;chr&gt;</span>                                                       </span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="color: #BCBCBC;">1</span> bioinformatics    <span style="color: #949494;">"</span>I am giving you information about preprints published in b…</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="color: #BCBCBC;">2</span> genomics          <span style="color: #949494;">"</span>I am giving you information about preprints published in b…</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="color: #BCBCBC;">3</span> synthetic_biology <span style="color: #949494;">"</span>I am giving you information about preprints published in b…</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="color: #949494;"># A tibble: 2 × 2</span></span>
+<span class="r-out co"><span class="r-pr">#&gt;</span>   subject             prompt                                                    </span>
+<span class="r-out co"><span class="r-pr">#&gt;</span>   <span style="color: #949494; font-style: italic;">&lt;chr&gt;</span>               <span style="color: #949494; font-style: italic;">&lt;chr&gt;</span>                                                     </span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="color: #BCBCBC;">1</span> bioinformatics      <span style="color: #949494;">"</span>I am giving you information about recent bioRxiv/medRxiv…</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="color: #BCBCBC;">2</span> infectious_diseases <span style="color: #949494;">"</span>I am giving you information about recent bioRxiv/medRxiv…</span>
 </code></pre></div>
     </div>
   </main><aside class="col-md-3"><nav id="toc" aria-label="Table of contents"><h2>On this page</h2>
@@ -83,7 +82,7 @@ <h2 id="ref-examples">Examples<a class="anchor" aria-label="anchor" href="#ref-e
 </div>
 
 <div class="pkgdown-footer-right">
-  <p>Site built with <a href="https://pkgdown.r-lib.org/" class="external-link">pkgdown</a> 2.1.0.</p>
+  <p>Site built with <a href="https://pkgdown.r-lib.org/" class="external-link">pkgdown</a> 2.1.1.</p>
 </div>
 
     </footer></div>
diff --git a/reference/add_summary.html b/reference/add_summary.html
index a7f8f1e..93e0ef5 100644
--- a/reference/add_summary.html
+++ b/reference/add_summary.html
@@ -7,7 +7,7 @@
 
     <a class="navbar-brand me-2" href="../index.html">biorecap</a>
 
-    <small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">0.1.1</small>
+    <small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">0.2.0</small>
 
 
     <button class="navbar-toggler" type="button" data-bs-toggle="collapse" data-bs-target="#navbar" aria-controls="navbar" aria-expanded="false" aria-label="Toggle navigation">
@@ -28,7 +28,7 @@
 <div class="row">
   <main id="main" class="col-md-9"><div class="page-header">
       <img src="../logo.png" class="logo" alt=""><h1>Generate a summary from a data frame of prompts</h1>
-      <small class="dont-index">Source: <a href="https://github.com/stephenturner/biorecap/blob/v0.1.1/R/biorecap.R" class="external-link"><code>R/biorecap.R</code></a></small>
+      <small class="dont-index">Source: <a href="https://github.com/stephenturner/biorecap/blob/main/R/biorecap.R" class="external-link"><code>R/biorecap.R</code></a></small>
       <div class="d-none name"><code>add_summary.Rd</code></div>
     </div>
 
@@ -38,7 +38,7 @@
 
     <div class="section level2">
     <h2 id="ref-usage">Usage<a class="anchor" aria-label="anchor" href="#ref-usage"></a></h2>
-    <div class="sourceCode"><pre class="sourceCode r"><code><span><span class="fu">add_summary</span><span class="op">(</span><span class="va">preprints</span>, model <span class="op">=</span> <span class="st">"llama3.1"</span><span class="op">)</span></span></code></pre></div>
+    <div class="sourceCode"><pre class="sourceCode r"><code><span><span class="fu">add_summary</span><span class="op">(</span><span class="va">preprints</span>, model <span class="op">=</span> <span class="st">"llama3.2"</span><span class="op">)</span></span></code></pre></div>
     </div>
 
     <div class="section level2">
@@ -80,7 +80,7 @@ <h2 id="ref-examples">Examples<a class="anchor" aria-label="anchor" href="#ref-e
 </div>
 
 <div class="pkgdown-footer-right">
-  <p>Site built with <a href="https://pkgdown.r-lib.org/" class="external-link">pkgdown</a> 2.1.0.</p>
+  <p>Site built with <a href="https://pkgdown.r-lib.org/" class="external-link">pkgdown</a> 2.1.1.</p>
 </div>
 
     </footer></div>
diff --git a/reference/biorecap-package.html b/reference/biorecap-package.html
index 123d18f..2c92736 100644
--- a/reference/biorecap-package.html
+++ b/reference/biorecap-package.html
@@ -1,7 +1,7 @@
 <!DOCTYPE html>
-<!-- Generated by pkgdown: do not edit by hand --><html lang="en"><head><meta http-equiv="Content-Type" content="text/html; charset=UTF-8"><meta charset="utf-8"><meta http-equiv="X-UA-Compatible" content="IE=edge"><meta name="viewport" content="width=device-width, initial-scale=1, shrink-to-fit=no"><title>biorecap: Retrieve and summarize bioRxiv preprints with a local LLM using ollama — biorecap-package • biorecap</title><!-- favicons --><link rel="icon" type="image/png" sizes="16x16" href="../favicon-16x16.png"><link rel="icon" type="image/png" sizes="32x32" href="../favicon-32x32.png"><link rel="apple-touch-icon" type="image/png" sizes="180x180" href="../apple-touch-icon.png"><link rel="apple-touch-icon" type="image/png" sizes="120x120" href="../apple-touch-icon-120x120.png"><link rel="apple-touch-icon" type="image/png" sizes="76x76" href="../apple-touch-icon-76x76.png"><link rel="apple-touch-icon" type="image/png" sizes="60x60" href="../apple-touch-icon-60x60.png"><script src="../deps/jquery-3.6.0/jquery-3.6.0.min.js"></script><meta name="viewport" content="width=device-width, initial-scale=1, shrink-to-fit=no"><link href="../deps/bootstrap-5.3.1/bootstrap.min.css" rel="stylesheet"><script src="../deps/bootstrap-5.3.1/bootstrap.bundle.min.js"></script><link href="../deps/font-awesome-6.4.2/css/all.min.css" rel="stylesheet"><link href="../deps/font-awesome-6.4.2/css/v4-shims.min.css" rel="stylesheet"><script src="../deps/headroom-0.11.0/headroom.min.js"></script><script src="../deps/headroom-0.11.0/jQuery.headroom.min.js"></script><script src="../deps/bootstrap-toc-1.0.1/bootstrap-toc.min.js"></script><script src="../deps/clipboard.js-2.0.11/clipboard.min.js"></script><script src="../deps/search-1.0.0/autocomplete.jquery.min.js"></script><script src="../deps/search-1.0.0/fuse.min.js"></script><script src="../deps/search-1.0.0/mark.min.js"></script><!-- pkgdown --><script src="../pkgdown.js"></script><meta property="og:title" content="biorecap: Retrieve and summarize bioRxiv preprints with a local LLM using ollama — biorecap-package"><meta name="description" content="
-Retrieve and summarize bioRxiv preprints with a local LLM using ollama."><meta property="og:description" content="
-Retrieve and summarize bioRxiv preprints with a local LLM using ollama."><meta property="og:image" content="https://stephenturner.github.io/biorecap/logo.png"></head><body>
+<!-- Generated by pkgdown: do not edit by hand --><html lang="en"><head><meta http-equiv="Content-Type" content="text/html; charset=UTF-8"><meta charset="utf-8"><meta http-equiv="X-UA-Compatible" content="IE=edge"><meta name="viewport" content="width=device-width, initial-scale=1, shrink-to-fit=no"><title>biorecap: Retrieve and summarize bioRxiv and medRxiv preprints with a local LLM using ollama — biorecap-package • biorecap</title><!-- favicons --><link rel="icon" type="image/png" sizes="16x16" href="../favicon-16x16.png"><link rel="icon" type="image/png" sizes="32x32" href="../favicon-32x32.png"><link rel="apple-touch-icon" type="image/png" sizes="180x180" href="../apple-touch-icon.png"><link rel="apple-touch-icon" type="image/png" sizes="120x120" href="../apple-touch-icon-120x120.png"><link rel="apple-touch-icon" type="image/png" sizes="76x76" href="../apple-touch-icon-76x76.png"><link rel="apple-touch-icon" type="image/png" sizes="60x60" href="../apple-touch-icon-60x60.png"><script src="../deps/jquery-3.6.0/jquery-3.6.0.min.js"></script><meta name="viewport" content="width=device-width, initial-scale=1, shrink-to-fit=no"><link href="../deps/bootstrap-5.3.1/bootstrap.min.css" rel="stylesheet"><script src="../deps/bootstrap-5.3.1/bootstrap.bundle.min.js"></script><link href="../deps/font-awesome-6.4.2/css/all.min.css" rel="stylesheet"><link href="../deps/font-awesome-6.4.2/css/v4-shims.min.css" rel="stylesheet"><script src="../deps/headroom-0.11.0/headroom.min.js"></script><script src="../deps/headroom-0.11.0/jQuery.headroom.min.js"></script><script src="../deps/bootstrap-toc-1.0.1/bootstrap-toc.min.js"></script><script src="../deps/clipboard.js-2.0.11/clipboard.min.js"></script><script src="../deps/search-1.0.0/autocomplete.jquery.min.js"></script><script src="../deps/search-1.0.0/fuse.min.js"></script><script src="../deps/search-1.0.0/mark.min.js"></script><!-- pkgdown --><script src="../pkgdown.js"></script><meta property="og:title" content="biorecap: Retrieve and summarize bioRxiv and medRxiv preprints with a local LLM using ollama — biorecap-package"><meta name="description" content="
+Retrieve and summarize bioRxiv and medRxiv preprints with a local LLM using ollama."><meta property="og:description" content="
+Retrieve and summarize bioRxiv and medRxiv preprints with a local LLM using ollama."><meta property="og:image" content="https://stephenturner.github.io/biorecap/logo.png"></head><body>
     <a href="#main" class="visually-hidden-focusable">Skip to contents</a>
 
 
@@ -9,7 +9,7 @@
 
     <a class="navbar-brand me-2" href="../index.html">biorecap</a>
 
-    <small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">0.1.1</small>
+    <small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">0.2.0</small>
 
 
     <button class="navbar-toggler" type="button" data-bs-toggle="collapse" data-bs-target="#navbar" aria-controls="navbar" aria-expanded="false" aria-label="Toggle navigation">
@@ -29,14 +29,14 @@
 </nav><div class="container template-reference-topic">
 <div class="row">
   <main id="main" class="col-md-9"><div class="page-header">
-      <img src="../logo.png" class="logo" alt=""><h1>biorecap: Retrieve and summarize bioRxiv preprints with a local LLM using ollama</h1>
-      <small class="dont-index">Source: <a href="https://github.com/stephenturner/biorecap/blob/v0.1.1/R/biorecap-package.R" class="external-link"><code>R/biorecap-package.R</code></a></small>
+      <img src="../logo.png" class="logo" alt=""><h1>biorecap: Retrieve and summarize bioRxiv and medRxiv preprints with a local LLM using ollama</h1>
+      <small class="dont-index">Source: <a href="https://github.com/stephenturner/biorecap/blob/main/R/biorecap-package.R" class="external-link"><code>R/biorecap-package.R</code></a></small>
       <div class="d-none name"><code>biorecap-package.Rd</code></div>
     </div>
 
     <div class="ref-description section level2">
     <p></p>
-<p>Retrieve and summarize bioRxiv preprints with a local LLM using ollama.</p>
+<p>Retrieve and summarize bioRxiv and medRxiv preprints with a local LLM using ollama.</p>
     </div>
 
 
@@ -60,7 +60,7 @@ <h2 id="author">Author<a class="anchor" aria-label="anchor" href="#author"></a><
 </div>
 
 <div class="pkgdown-footer-right">
-  <p>Site built with <a href="https://pkgdown.r-lib.org/" class="external-link">pkgdown</a> 2.1.0.</p>
+  <p>Site built with <a href="https://pkgdown.r-lib.org/" class="external-link">pkgdown</a> 2.1.1.</p>
 </div>
 
     </footer></div>
diff --git a/reference/biorecap_report.html b/reference/biorecap_report.html
index b48d385..a6f6f94 100644
--- a/reference/biorecap_report.html
+++ b/reference/biorecap_report.html
@@ -1,5 +1,5 @@
 <!DOCTYPE html>
-<!-- Generated by pkgdown: do not edit by hand --><html lang="en"><head><meta http-equiv="Content-Type" content="text/html; charset=UTF-8"><meta charset="utf-8"><meta http-equiv="X-UA-Compatible" content="IE=edge"><meta name="viewport" content="width=device-width, initial-scale=1, shrink-to-fit=no"><title>Create a report from bioRxiv preprints — biorecap_report • biorecap</title><!-- favicons --><link rel="icon" type="image/png" sizes="16x16" href="../favicon-16x16.png"><link rel="icon" type="image/png" sizes="32x32" href="../favicon-32x32.png"><link rel="apple-touch-icon" type="image/png" sizes="180x180" href="../apple-touch-icon.png"><link rel="apple-touch-icon" type="image/png" sizes="120x120" href="../apple-touch-icon-120x120.png"><link rel="apple-touch-icon" type="image/png" sizes="76x76" href="../apple-touch-icon-76x76.png"><link rel="apple-touch-icon" type="image/png" sizes="60x60" href="../apple-touch-icon-60x60.png"><script src="../deps/jquery-3.6.0/jquery-3.6.0.min.js"></script><meta name="viewport" content="width=device-width, initial-scale=1, shrink-to-fit=no"><link href="../deps/bootstrap-5.3.1/bootstrap.min.css" rel="stylesheet"><script src="../deps/bootstrap-5.3.1/bootstrap.bundle.min.js"></script><link href="../deps/font-awesome-6.4.2/css/all.min.css" rel="stylesheet"><link href="../deps/font-awesome-6.4.2/css/v4-shims.min.css" rel="stylesheet"><script src="../deps/headroom-0.11.0/headroom.min.js"></script><script src="../deps/headroom-0.11.0/jQuery.headroom.min.js"></script><script src="../deps/bootstrap-toc-1.0.1/bootstrap-toc.min.js"></script><script src="../deps/clipboard.js-2.0.11/clipboard.min.js"></script><script src="../deps/search-1.0.0/autocomplete.jquery.min.js"></script><script src="../deps/search-1.0.0/fuse.min.js"></script><script src="../deps/search-1.0.0/mark.min.js"></script><!-- pkgdown --><script src="../pkgdown.js"></script><meta property="og:title" content="Create a report from bioRxiv preprints — biorecap_report"><meta name="description" content="Create a report from bioRxiv preprints"><meta property="og:description" content="Create a report from bioRxiv preprints"><meta property="og:image" content="https://stephenturner.github.io/biorecap/logo.png"></head><body>
+<!-- Generated by pkgdown: do not edit by hand --><html lang="en"><head><meta http-equiv="Content-Type" content="text/html; charset=UTF-8"><meta charset="utf-8"><meta http-equiv="X-UA-Compatible" content="IE=edge"><meta name="viewport" content="width=device-width, initial-scale=1, shrink-to-fit=no"><title>Create a report from bioRxiv/medRxiv preprints — biorecap_report • biorecap</title><!-- favicons --><link rel="icon" type="image/png" sizes="16x16" href="../favicon-16x16.png"><link rel="icon" type="image/png" sizes="32x32" href="../favicon-32x32.png"><link rel="apple-touch-icon" type="image/png" sizes="180x180" href="../apple-touch-icon.png"><link rel="apple-touch-icon" type="image/png" sizes="120x120" href="../apple-touch-icon-120x120.png"><link rel="apple-touch-icon" type="image/png" sizes="76x76" href="../apple-touch-icon-76x76.png"><link rel="apple-touch-icon" type="image/png" sizes="60x60" href="../apple-touch-icon-60x60.png"><script src="../deps/jquery-3.6.0/jquery-3.6.0.min.js"></script><meta name="viewport" content="width=device-width, initial-scale=1, shrink-to-fit=no"><link href="../deps/bootstrap-5.3.1/bootstrap.min.css" rel="stylesheet"><script src="../deps/bootstrap-5.3.1/bootstrap.bundle.min.js"></script><link href="../deps/font-awesome-6.4.2/css/all.min.css" rel="stylesheet"><link href="../deps/font-awesome-6.4.2/css/v4-shims.min.css" rel="stylesheet"><script src="../deps/headroom-0.11.0/headroom.min.js"></script><script src="../deps/headroom-0.11.0/jQuery.headroom.min.js"></script><script src="../deps/bootstrap-toc-1.0.1/bootstrap-toc.min.js"></script><script src="../deps/clipboard.js-2.0.11/clipboard.min.js"></script><script src="../deps/search-1.0.0/autocomplete.jquery.min.js"></script><script src="../deps/search-1.0.0/fuse.min.js"></script><script src="../deps/search-1.0.0/mark.min.js"></script><!-- pkgdown --><script src="../pkgdown.js"></script><meta property="og:title" content="Create a report from bioRxiv/medRxiv preprints — biorecap_report"><meta name="description" content="Create a report from bioRxiv/medRxiv preprints"><meta property="og:description" content="Create a report from bioRxiv/medRxiv preprints"><meta property="og:image" content="https://stephenturner.github.io/biorecap/logo.png"></head><body>
     <a href="#main" class="visually-hidden-focusable">Skip to contents</a>
 
 
@@ -7,7 +7,7 @@
 
     <a class="navbar-brand me-2" href="../index.html">biorecap</a>
 
-    <small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">0.1.1</small>
+    <small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">0.2.0</small>
 
 
     <button class="navbar-toggler" type="button" data-bs-toggle="collapse" data-bs-target="#navbar" aria-controls="navbar" aria-expanded="false" aria-label="Toggle navigation">
@@ -27,13 +27,13 @@
 </nav><div class="container template-reference-topic">
 <div class="row">
   <main id="main" class="col-md-9"><div class="page-header">
-      <img src="../logo.png" class="logo" alt=""><h1>Create a report from bioRxiv preprints</h1>
-      <small class="dont-index">Source: <a href="https://github.com/stephenturner/biorecap/blob/v0.1.1/R/biorecap.R" class="external-link"><code>R/biorecap.R</code></a></small>
+      <img src="../logo.png" class="logo" alt=""><h1>Create a report from bioRxiv/medRxiv preprints</h1>
+      <small class="dont-index">Source: <a href="https://github.com/stephenturner/biorecap/blob/main/R/biorecap.R" class="external-link"><code>R/biorecap.R</code></a></small>
       <div class="d-none name"><code>biorecap_report.Rd</code></div>
     </div>
 
     <div class="ref-description section level2">
-    <p>Create a report from bioRxiv preprints</p>
+    <p>Create a report from bioRxiv/medRxiv preprints</p>
     </div>
 
     <div class="section level2">
@@ -69,7 +69,7 @@ <h2 id="arguments">Arguments<a class="anchor" aria-label="anchor" href="#argumen
 
 
 <dt id="arg-use-example-preprints">use_example_preprints<a class="anchor" aria-label="anchor" href="#arg-use-example-preprints"></a></dt>
-<dd><p>Use the example preprints data included with the package instead of fetching new data from bioRxiv. For diagnostic/testing purposes only.</p></dd>
+<dd><p>Use the example preprints data included with the package instead of fetching new data from bioRxiv/medRxiv. For diagnostic/testing purposes only.</p></dd>
 
 
 <dt id="arg--">...<a class="anchor" aria-label="anchor" href="#arg--"></a></dt>
@@ -100,7 +100,7 @@ <h2 id="ref-examples">Examples<a class="anchor" aria-label="anchor" href="#ref-e
 </div>
 
 <div class="pkgdown-footer-right">
-  <p>Site built with <a href="https://pkgdown.r-lib.org/" class="external-link">pkgdown</a> 2.1.0.</p>
+  <p>Site built with <a href="https://pkgdown.r-lib.org/" class="external-link">pkgdown</a> 2.1.1.</p>
 </div>
 
     </footer></div>
diff --git a/reference/build_prompt_preprint.html b/reference/build_prompt_preprint.html
index 98c8c82..4a712e3 100644
--- a/reference/build_prompt_preprint.html
+++ b/reference/build_prompt_preprint.html
@@ -7,7 +7,7 @@
 
     <a class="navbar-brand me-2" href="../index.html">biorecap</a>
 
-    <small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">0.1.1</small>
+    <small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">0.2.0</small>
 
 
     <button class="navbar-toggler" type="button" data-bs-toggle="collapse" data-bs-target="#navbar" aria-controls="navbar" aria-expanded="false" aria-label="Toggle navigation">
@@ -28,7 +28,7 @@
 <div class="row">
   <main id="main" class="col-md-9"><div class="page-header">
       <img src="../logo.png" class="logo" alt=""><h1>Construct a prompt to summarize a paper</h1>
-      <small class="dont-index">Source: <a href="https://github.com/stephenturner/biorecap/blob/v0.1.1/R/biorecap.R" class="external-link"><code>R/biorecap.R</code></a></small>
+      <small class="dont-index">Source: <a href="https://github.com/stephenturner/biorecap/blob/main/R/biorecap.R" class="external-link"><code>R/biorecap.R</code></a></small>
       <div class="d-none name"><code>build_prompt_preprint.Rd</code></div>
     </div>
 
@@ -90,7 +90,7 @@ <h2 id="ref-examples">Examples<a class="anchor" aria-label="anchor" href="#ref-e
 </div>
 
 <div class="pkgdown-footer-right">
-  <p>Site built with <a href="https://pkgdown.r-lib.org/" class="external-link">pkgdown</a> 2.1.0.</p>
+  <p>Site built with <a href="https://pkgdown.r-lib.org/" class="external-link">pkgdown</a> 2.1.1.</p>
 </div>
 
     </footer></div>
diff --git a/reference/build_prompt_subject.html b/reference/build_prompt_subject.html
index 5b271ef..6db5ad2 100644
--- a/reference/build_prompt_subject.html
+++ b/reference/build_prompt_subject.html
@@ -7,7 +7,7 @@
 
     <a class="navbar-brand me-2" href="../index.html">biorecap</a>
 
-    <small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">0.1.1</small>
+    <small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">0.2.0</small>
 
 
     <button class="navbar-toggler" type="button" data-bs-toggle="collapse" data-bs-target="#navbar" aria-controls="navbar" aria-expanded="false" aria-label="Toggle navigation">
@@ -28,7 +28,7 @@
 <div class="row">
   <main id="main" class="col-md-9"><div class="page-header">
       <img src="../logo.png" class="logo" alt=""><h1>Construct a prompt to summarize a set of papers from a subject</h1>
-      <small class="dont-index">Source: <a href="https://github.com/stephenturner/biorecap/blob/v0.1.1/R/biorecap.R" class="external-link"><code>R/biorecap.R</code></a></small>
+      <small class="dont-index">Source: <a href="https://github.com/stephenturner/biorecap/blob/main/R/biorecap.R" class="external-link"><code>R/biorecap.R</code></a></small>
       <div class="d-none name"><code>build_prompt_subject.Rd</code></div>
     </div>
 
@@ -43,8 +43,7 @@ <h2 id="ref-usage">Usage<a class="anchor" aria-label="anchor" href="#ref-usage">
 <span>  <span class="va">title</span>,</span>
 <span>  <span class="va">summary</span>,</span>
 <span>  nsentences <span class="op">=</span> <span class="fl">5L</span>,</span>
-<span>  instructions <span class="op">=</span></span>
-<span>    <span class="fu"><a href="https://rdrr.io/r/base/c.html" class="external-link">c</a></span><span class="op">(</span><span class="st">"I am giving you information about preprints published in bioRxiv recently."</span>,</span>
+<span>  instructions <span class="op">=</span> <span class="fu"><a href="https://rdrr.io/r/base/c.html" class="external-link">c</a></span><span class="op">(</span><span class="st">"I am giving you information about recent bioRxiv/medRxiv preprints."</span>,</span>
 <span>    <span class="st">"I'll give you the subject, preprint titles, and short summary of each paper."</span>,</span>
 <span>    <span class="st">"Please provide a general summary new advances in this subject/field in general."</span>,</span>
 <span>    <span class="st">"Provide this summary of the field in as many sentences as I instruct."</span>,</span>
@@ -87,7 +86,7 @@ <h2 id="ref-examples">Examples<a class="anchor" aria-label="anchor" href="#ref-e
     <div class="sourceCode"><pre class="sourceCode r"><code><span class="r-in"><span><span class="va">title</span> <span class="op">&lt;-</span> <span class="va">example_preprints</span> <span class="op">|&gt;</span> <span class="fu">dplyr</span><span class="fu">::</span><span class="fu"><a href="https://dplyr.tidyverse.org/reference/filter.html" class="external-link">filter</a></span><span class="op">(</span><span class="va">subject</span><span class="op">==</span><span class="st">"bioinformatics"</span><span class="op">)</span> <span class="op">|&gt;</span> <span class="fu">dplyr</span><span class="fu">::</span><span class="fu"><a href="https://dplyr.tidyverse.org/reference/pull.html" class="external-link">pull</a></span><span class="op">(</span><span class="va">title</span><span class="op">)</span></span></span>
 <span class="r-in"><span><span class="va">summary</span> <span class="op">&lt;-</span> <span class="va">example_preprints</span> <span class="op">|&gt;</span> <span class="fu">dplyr</span><span class="fu">::</span><span class="fu"><a href="https://dplyr.tidyverse.org/reference/filter.html" class="external-link">filter</a></span><span class="op">(</span><span class="va">subject</span><span class="op">==</span><span class="st">"bioinformatics"</span><span class="op">)</span> <span class="op">|&gt;</span> <span class="fu">dplyr</span><span class="fu">::</span><span class="fu"><a href="https://dplyr.tidyverse.org/reference/pull.html" class="external-link">pull</a></span><span class="op">(</span><span class="va">summary</span><span class="op">)</span></span></span>
 <span class="r-in"><span><span class="fu">build_prompt_subject</span><span class="op">(</span>subject<span class="op">=</span><span class="st">"bioinformatics"</span>, title<span class="op">=</span><span class="va">title</span>, summary<span class="op">=</span><span class="va">summary</span><span class="op">)</span></span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> [1] "I am giving you information about preprints published in bioRxiv recently. I'll give you the subject, preprint titles, and short summary of each paper. Please provide a general summary new advances in this subject/field in general. Provide this summary of the field in as many sentences as I instruct. Do not include any preamble text to the summary just give me the summary with no preface or intro sentence.\n\nSubject: bioinformatics\nNumber of sentences in summary: 5\n\nHere are the titles and summaries:\n\nTitle: Integrity and miss grouping as support for clusters in agglomerative hierarchical methods: the R-package octopucs\nSummary: The proposed method assesses cluster support throughout hierarchical analyses by compiling a consensus topology and using ecological concepts of reciprocal complementarities to define cluster integrity and contamination. This approach allows for building support for groups even when there is partial membership match after resampling, and was implemented in the R package octopucs, which showed robust detection of changes in group memberships compared to other methods.\n\nTitle: Sainsc: a computational tool for segmentation-free analysis of in-situ capture\nSummary: Sainsc is a computational tool that enables segmentation-free analysis of spatially resolved transcriptomics data, allowing for accurate cell-type assignment at the subcellular level without requiring manual cell border delineation. The tool provides efficient processing of high-resolution spatial data and can generate maps of cell types with corresponding confidence scores, making it a valuable resource for biomedical researchers working with complex tissue samples.\n\nTitle: BRACE: A novel Bayesian-based imputation approach for dimension reduction analysis of alternative splicing at single-cell resolution\nSummary: Alternative splicing represents an additional layer of complexity in gene expression profiles, but analyzing it at single-cell resolution is challenging due to missing data. This paper introduces BRACE, a Bayesian-based imputation approach that improves upon existing methods and enables dimension reduction analysis of alternative splicing events at single-cell resolution.\n\nTitle: Topological embedding and directional feature importance in ensemble classifiers for multi-class classification\nSummary: Researchers developed a new metric called class-based direction feature importance (CLIFI) to provide interpretable insights into the decision-making process of ensemble classifiers for multi-class classification problems, specifically in the context of cancer biomarker identification. The CLIFI metric was incorporated into four algorithms and applied to The Cancer Genome Atlas proteomics data, resulting in high F1-scores and allowing for the visualization of model decision-making functions and the identification of heterogeneity in several proteins across different cancer types.\n\nTitle: SeuratExtend: Streamlining Single-Cell RNA-Seq Analysis Through an Integrated and Intuitive Framework\nSummary: SeuratExtend is an R package that integrates essential tools and databases for single-cell RNA sequencing (scRNA-seq) data analysis, streamlining the process through a user-friendly interface. The package offers various analyses, including functional enrichment and gene regulatory network reconstruction, and seamlessly integrates multiple databases and popular Python tools.\n\nTitle: An Evolutionary Statistics Toolkit for Simplified Sequence Analysis on Web with Client-Side Processing\nSummary: The \"Evolutionary Statistics Toolkit\" is a web-based platform that integrates multiple evolutionary statistics tools for simplified sequence analysis, including Tajima's D calculator and Shannon's Entropy. The open-source toolkit facilitates streamlined workflows for researchers in evolutionary biology and genomics, and also serves as an educational interactive website for beginners in evolutionary statistics.\n\nTitle: A map of integrated cis-regulatory elements enhances gene regulatory analysis in maize\nSummary: The authors integrated various methods for profiling cis-regulatory elements (CREs) in maize, resulting in maps of integrated CREs that show increased completeness and precision. These maps were used to infer drought-specific gene regulatory networks and identify candidate regulators of maize drought response, as well as to study the potential role of transposable elements in regulating gene expression.\n\nTitle: MOSTPLAS: A Self-correction Multi-label Learning Model for Plasmid Host Range Prediction\nSummary: Plasmid host range prediction tools are essential for understanding how plasmids promote bacterial evolution, but existing learning-based tools struggle due to limited well-annotated training samples. The proposed model, MOSTPLAS, addresses this issue with a self-correction multi-label learning approach that uses pseudo label learning and asymmetric loss to facilitate training with incomplete labels.\n\nTitle: Bootstrap Evaluation of Association Matrices (BEAM) for Integrating Multiple Omics Profiles with Multiple Outcomes\nSummary: The authors propose Bootstrap Evaluation of Association Matrices (BEAM), a new statistical method that integrates multiple omics profiles with multiple clinical endpoints to identify significant associations between them. BEAM outperformed other integrated analysis methods in simulations and identified biologically relevant genes in a pediatric leukemia application that were missed by univariate screens and other methods.\n\nTitle: Thermodynamic modeling of Csr/Rsm- RNA interactions capture novel, direct binding interactions across the Pseudomonas aeruginosa transcriptome\nSummary: Researchers developed a thermodynamic model to predict interactions between the post-transcriptional regulator RsmA and mRNAs in Pseudomonas aeruginosa, predicting 1043 direct binding interactions, including 457 novel targets. The predictions were validated through in vitro binding assays and in vivo translational reporters, revealing direct regulation of genes involved in quorum sensing and the Type IV Secretion system, expanding the known pool of RsmA target genes.\n\nTitle: Assessing the ability of ChatGPT to extract natural product bioactivity and biosynthesis data from publications\nSummary: ChatGPT was tested on its ability to extract data from publications on natural product bioactivity and biosynthesis, which is crucial for training models that predict natural product activity from biosynthetic gene clusters. The results showed that ChatGPT performed well in identifying papers describing natural product discovery and extracting information about the product's bioactivity, but struggled with extracting accession numbers for the biosynthetic gene cluster or producer's genome.\n\nTitle: Genome-Wide Analysis of TCP Family Genes and Their Constitutive Expression Pattern Analysis in the Melon (Cucumis melo)\nSummary: This study identified and characterized 29 putative TCP genes in melon, classifying them into two classes and analyzing their chromosomal location, gene structure, and expression patterns. The results suggest that some CmTCP genes may have similar functions to their homologs in other plant species, while others may have undergone functional diversification, providing a resource for future investigations into their roles in melon development.\n\nTitle: Single-cell differential expression analysis between conditions within nested settings\nSummary: Researchers compared various methods for differential expression analysis of single-cell transcriptomics data and found that methods designed specifically for single-cell data do not offer performance advantages over conventional pseudobulk methods like DESeq2 when applied to individual datasets. However, permutation-based methods excel in performance for atlas-level analysis, but require significantly longer run times, making DREAM a compromise between quality and runtime.\n\nTitle: CoMPHI: A Novel Composite Machine Learning Approach Utilizing Multiple FeatureRepresentation to Predict Hosts of Bacteriophages\nSummary: Here is a 2-sentence summary of the paper:  This study introduces CoMPHI, a novel composite machine learning approach that combines multiple feature representations to predict hosts of bacteriophages, with potential applications in phage therapy for treating bacterial infections. The model achieves high prediction accuracy, with an Area Under the ROC Curve (AUC) of up to 96.7% and accuracy of up to 95.1%, outperforming existing methods due to its inclusion of alignment scores and use of both nucleotide and protein sequences from phages and hosts.\n\nTitle: FourierMIL: Fourier filtering-based multiple instance learning for whole slide image analysis\nSummary: The paper presents FourierMIL, a multiple instance learning framework that uses the discrete Fourier transform to analyze whole-slide images (WSIs) in digital pathology. The method captures both global and local dependencies within WSIs and outperforms existing state-of-the-art methods in tumor classification tasks on gigapixel-resolution WSIs.\n\nTitle: Multiple Protein Structure Alignment at Scale with FoldMason\nSummary: Here is a 2-sentence summary of the paper:  FoldMason is a new method for multiple protein structure alignment that can handle hundreds of thousands of structures at scale with high speed and accuracy. It leverages the structural alphabet from Foldseek to compute confidence scores, provide interactive visualizations, and support large-scale protein structure analysis and phylogenetic studies.\n\nTitle: Deciphering octoploid strawberry evolution with serial LTR similarity matrices for subgenome partition\nSummary: A novel approach was developed to assign polyploid genome assemblies to subgenomes using long terminal repeat retrotransposons (LTR-RTs) and the Serial Similarity Matrix (SSM) method, which is particularly useful for genomes without known diploid ancestors. The SSM approach was validated using well-studied allopolyploidy genomes and then applied to the octoploid strawberry genome, revealing three allopolyploidization events in its evolutionary history.\n\nTitle: IDENTIFICATION OF IMMUNE RESPONSE AND RNA NETWORK OF RHEUMATOID ARTHRITIS AND MOLECULAR DOCKING OF CELASTRUS PANICULATUS AS POTENTIAL THERAPEUTIC AGENT\nSummary: This study used bioinformatics analysis to identify immune responses, microRNA-hub genes networks, and potential therapeutic agents for rheumatoid arthritis (RA), a complex autoimmune disease with an unknown pathogenesis. The researchers found several hub genes and miRNAs associated with RA, and identified oleic acid and zeylasterone as potential novel drug candidates against the disease through molecular docking analysis of Celastrus paniculatus phytochemical compounds.\n\nTitle: Imputing abundance of over 2500 surface proteins from single-cell transcriptomes with context-agnostic zero-shot deep ensembles\nSummary: SPIDER is a deep ensemble model that predicts the abundance of over 2500 surface proteins from single-cell transcriptomes with improved generalization across diverse contexts such as tissues or disease states. The model outperforms other state-of-the-art methods and has various applications including cell type annotation, biomarker/target identification, and cell-cell interaction analysis in cancer research.\n\nTitle: Modelling Protein-Glycan Interactions with HADDOCK\nSummary: Glycans play important roles in living organisms by interacting with proteins for information transfer and signalling purposes, making it essential to determine the three-dimensional structures of protein-glycan complexes. The molecular docking approach HADDOCK was used to predict protein-glycan complexes with a top 5 success rate of 70% for bound datasets and 40% for unbound datasets using a benchmark of 89 complexes.\n\nTitle: Machine Learning Reveals Key Glycoprotein Mutations and Rapidly Assigns Lassa Virus Lineages\nSummary: Machine learning and phylogenetic analysis of Lassa virus glycoprotein sequences revealed key mutations and genetic differences between Nigerian lineages and those from other West African countries. The study identified specific amino acid positions that are highly variable among the lineages, which may explain structural and phenotypical differences, and developed a machine learning-based tool for rapid lineage classification.\n\nTitle: RESP2: An uncertainty aware multi-target multi-property optimization AI pipeline for antibody discovery\nSummary: The RESP2 pipeline is an AI-powered tool designed to optimize the discovery of therapeutic antibodies against infectious disease pathogens, taking into account multiple targets and properties such as specificity, low immunogenicity, and high affinity. The pipeline uses a suite of methods to estimate uncertainty in predictions and has been successfully applied to discover a highly human antibody with broad binding to variants of the COVID-19 spike protein receptor binding domain.\n\nTitle: Extending the capabilities of deconvolution to provide cell type specific pathway analysis of bulk RNA-seq data for idiopathic pulmonary fibrosis\nSummary: A deconvolution method was applied to bulk RNA-seq data from idiopathic pulmonary fibrosis (IPF) samples to correct for changes in cell type proportions and provide cell-type specific pathway analysis. The results showed significant increases in fibroblasts and myofibroblasts, decreases in vascular endothelial capillary cells, and IPF-related changes in extracellular matrix organization and TGF-{beta} regulation, as well as the involvement of interferon signaling in ATII cells.\n\nTitle: A survey of ADP-ribosyltransferase families in the pathogenic Legionella\nSummary: A comprehensive bioinformatic survey of 41 Legionella species identified 63 proteins with significant sequence or structural similarity to known ADP-ribosyltransferases (ARTs), organized into 39 ART-like families, including 26 novel families. The study found that most members of the novel ART families are predicted effectors, presenting promising targets for understanding Legionella pathogenicity and developing therapeutic strategies.\n\nTitle: A replicable and modular benchmark for long-read transcript quantification methods\nSummary: Researchers have developed a replicable benchmark for evaluating long-read transcript quantification methods using synthetic RNA-seq datasets, which can be easily extended to include new tools or data sets. The study reveals discrepancies with previously published results and highlights the importance of high-quality simulated data in assessing the robustness of certain approaches.\n\nTitle: Logan: Planetary-Scale Genome Assembly Surveys Life's Diversity\nSummary: The NCBI Sequence Read Archive contains over 50 petabases of DNA sequencing data across 27 million datasets, but its size makes it impractical to search for specific genetic sequences within a reasonable time frame. To address this issue, the authors used cloud computing to perform genome assembly on each dataset and created the Logan assemblage, which is now freely available and enables faster querying of the data, with some queries completing in as little as 11 hours.\n\nTitle: Cell-type specific epigenetic clocks to quantify biological age at cell-type resolution\nSummary: Epigenetic clocks have been developed to estimate biological age, but most are based on heterogeneous bulk tissues and reflect both changes in cell-type composition and individual cell aging. This study created neuron- and hepatocyte-specific DNA methylation clocks that provide improved estimates of chronological age and detect accelerated biological aging in Alzheimer's disease and liver pathology.\n\nTitle: Genomic and transcriptomic analyses of Heteropoda venatoria reveal the expansion of P450 family for starvation resistance in spider\nSummary: The genome of Heteropoda venatoria was sequenced and comparative genomic analysis revealed significant expansions in gene families related to lipid metabolism, including cytochrome P450 and steroid hormone biosynthesis genes. The study found that during starvation, H. venatoria undergoes a series of physiological changes, including the activation of fatty acid metabolism and protein degradation pathways, and the expression of expanded P450 gene families, which help the spider maintain a low-energy metabolic state and endure longer periods of starvation.\n\nTitle: Annotation Vocabulary (Might Be) All You Need\nSummary: The authors introduce the \"Annotation Vocabulary\", a language of protein properties defined by structured ontologies that can be used to train transformer models without reference to amino acid sequences. They demonstrate the effectiveness of this approach in various experiments, achieving state-of-the-art results on several common datasets with competitive performance on others, and generating high-quality de novo protein sequences from annotation-only prompts.\n\nTitle: AncFlow: An Ancestral Sequence Reconstruction Approach for Determining Novel Protein Structural\nSummary: Here is the summary in 2 sentences:  AncFlow is an automated software pipeline that integrates phylogenetic analysis, subfamily identification, and ancestral sequence reconstruction (ASR) to generate ancestral protein sequences for structural prediction using state-of-the-art tools like AlphaFold. The pipeline was validated on two well-characterized protein families, providing insights into the evolutionary mechanisms underpinning functional diversification within these families and demonstrating its potential to guide protein engineering efforts."</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> [1] "I am giving you information about recent bioRxiv/medRxiv preprints. I'll give you the subject, preprint titles, and short summary of each paper. Please provide a general summary new advances in this subject/field in general. Provide this summary of the field in as many sentences as I instruct. Do not include any preamble text to the summary just give me the summary with no preface or intro sentence.\n\nSubject: bioinformatics\nNumber of sentences in summary: 5\n\nHere are the titles and summaries:\n\nTitle: MedGraphNet: Leveraging Multi-Relational Graph Neural Networks and Text Knowledge for Biomedical Predictions\nSummary: MedGraphNet leverages multi-relational Graph Neural Networks and text knowledge to improve biomedical predictions by initializing nodes using informative embeddings from existing text knowledge, allowing for robust integration of various data types and improved generalizability. The model demonstrates superior performance compared to traditional single-relation approaches in scenarios with isolated or sparsely connected nodes, particularly in identifying disease-gene associations and drug-phenotype relationships, and shows promising results in accurately inferring drug side effects without direct training on such data.\n\nTitle: High-throughput bacterial aggregation analysis in droplets\nSummary: The communal lifestyle of bacteria can contribute significantly to antimicrobial resistance by promoting biofilm formation. A key approach to addressing this issue is to develop novel techniques for analyzing bacterial behavior, such as those enabled by droplet-based platforms and image analysis methods.\n\nTitle: scParadise: Tunable highly accurate multi-task cell type annotation and surface protein abundance prediction\nSummary: scAdam outperforms existing methods in annotating rare cell types with high accuracy and consistency across diverse datasets. scEve enhances clustering and cell type separation through improved surface protein prediction, leading to better characterization of complex tissues.\n\nTitle: Camera Paths, Modeling, and Image Processing Tools for ArtiaX\nSummary: ArtiaX is a plugin that has been extended to improve the analysis and visualization of cryo-electron tomography data through advanced visualization techniques. The plugin allows for the generation of diverse models with putative particle positions and orientations, as well as a coarse grained algorithm to rectify overlaps in template matching, driving camera position and facilitating movie creation with fundamental image filtering options.\n\nTitle: dScaff - an automatic bioinformatics framework for scaffolding draft de novo assemblies based on reference genome data\nSummary: dScaff is an automatic bioinformatics framework designed for scaffolding draft de novo assemblies based on reference genome data. The tool uses a series of bash and R scripts to create a minimal complete scaffold from a genome assembly, with potential future features to be implemented, including using reference chromosomes or scaffolds.\n\nTitle: Jaeger: an accurate and fast deep-learning tool to detect bacteriophage sequences\nSummary: Jaeger's accuracy and speed in identifying bacteriophage sequences outperform existing deep-learning tools by consistently producing few false positives despite encountering diverse viral sequences. The novel method achieves an estimated 2-27% false discovery rate when applied to over 16,000 metagenomic assemblies, which is significantly lower than the benchmarking paper where deep-learning tools produced many false positives.\n\nTitle: AI-Augmented R-Group Exploration in Medicinal Chemistry\nSummary: The paper presents a novel approach to enhancing free-wing QSAR models by embedding R-groups with atom-centric pharmacophoric features, allowing for the distinction of regioisomers and improved predictivity across 12 public datasets. The proposed method is integrated into an open-source program, enabling its application in various scenarios, including classic free-Wilson analysis and exploration of uncharted chemical space facilitated by AI-generated building blocks.\n\nTitle: OPLS-based Multiclass Classification and Data-Driven Inter-Class Relationship Discovery\nSummary: OPLS-DA models are widely used in metabolomics for two-class comparisons due to their strong discrimination capabilities, but these models face challenges in multiclass settings. An extension of OPLS-DA called OPLS-HDA integrates Hierarchical Cluster Analysis with the OPLS-DA framework to create a decision tree that addresses multiclass classification challenges and provides intuitive visualization of inter-class relationships.\n\nTitle: STANCE: a unified statistical model to detect cell-type-specific spatially variable genes in spatial transcriptomics\nSummary: STANCE, a unified statistical model to detect cell-type-specific spatially variable genes in spatial transcriptomics, was developed to address the challenges posed by existing methods in detecting spatially variable genes (SVGs) and cell type-specific spatially variable genes (ctSVGs). The proposed method integrates gene expression, spatial location, and cell type composition through a linear mixed-effect model to identify both SVGs and ctSVGs in an initial stage, followed by a second stage test dedicated to ctSVG detection.\n\nTitle: AsaruSim: a single-cell and spatial RNA-Seq Nanopore long-reads simulation workflow\nSummary: AsaruSim simulates synthetic single-cell long-read Nanopore datasets that closely mimic real experimental data by employing a multi-step process. It includes the creation of a synthetic UMI count matrix, generation of perfect reads, optional PCR amplification, introduction of sequencing errors, and comprehensive quality control reporting.\n\nTitle: Building a literature knowledge base towards transparent biomedical AI\nSummary: LiteralGraph extracts biomedical terms and relationships from PubMed literature, establishing a comprehensive knowledge graph. The resulting Genomic Literature Knowledge Base consolidates over 263 million biomedical terms, 14 million relationships, and 10 million genomic events across multiple sources, including nine established repositories.\n\nTitle: Accurate non-invasive quantification of astaxanthin content using hyperspectral images and machine learning\nSummary: The authors investigated a method to accurately quantify astaxanthin content in Haematococcus pluvialis microalgae cultures using hyperspectral images and machine learning. They found that this approach, combining reflectance hyperspectral imaging with a 1-dimensional convolutional neural network, had low average prediction error across a range of astaxanthin contents, although it was unreliable at very low levels (&lt;0.6 micrograms mg-1).\n\nTitle: AlphaMut: a deep reinforcement learning model to suggest helix-disrupting mutations\nSummary: The authors propose a deep reinforcement learning model called AlphaMut to predict helix-disrupting mutations in proteins. AlphaMut identifies amino acids crucial for maintaining structural integrity and predicts key mutations that could alter protein function.\n\nTitle: Beyond Static Brain Atlases: AI-Powered Open Databasing and Dynamic Mining of Brain-Wide Neuron Morphometry\nSummary: NeuroXiv is a large-scale database that provides detailed 3D morphologies of individual neurons mapped to a standard brain atlas, allowing for dynamic, interactive neuroscience applications. The database offers a comprehensive collection of 175,149 atlas-oriented reconstructed morphologies of individual neurons from over 518 mouse brains, classified into 292 distinct types and mapped into the Common Coordinate Framework Version 3 (CCFv3).\n\nTitle: Metabolic modeling identifies determinants of thermal growth responses in Arabidopsis thaliana\nSummary: The paper developed an enzyme-constrained model of Arabidopsis thaliana's metabolism, which facilitates predictions of growth-related phenotypes at different temperatures and identifies genes affecting plant growth at suboptimal temperatures. This model was validated using mutant lines, demonstrating its potential in accurately predicting plant thermal responses and providing a template for developing climate-resilient crops.\n\nTitle: Decoding Protein Dynamics: ProFlex as a Linguistic Bridge in Normal Mode Analysis\nSummary: Artificial intelligence has revolutionized structural bioinformatics with AlphaFold being arguably the most impactful development to date. The structural atlases generated by these methods present significant opportunities for unraveling biological mysteries, but also pose challenges in leveraging such massive datasets effectively.\n\nTitle: Exploring midgut expression dynamics: longitudinal transcriptomic analysis of adult female Amblyomma americanum midgut and comparative insights with other hard tick species\nSummary: The study investigates the transcriptomic dynamics of the midgut in adult female Amblyomma americanum ticks during different feeding stages, revealing 15,599 putative DNA coding sequences and highlighting dynamic transcriptional changes as feeding progresses. The analysis also identified conserved transcripts across three hard tick species, providing insight into the physiological pathways relevant to the tick midgut and potential avenues for developing control methods targeting multiple tick species.\n\nTitle: Designing of thermostable proteins with a desired melting temperature\nSummary: We developed a regression method for predicting protein melting temperatures (Tm) using 17,312 non-redundant proteins and achieved the highest Pearson correlation of 0.80 with an R2 of 0.63 between predicted and actual Tm values. Our best model, fine-tuned on large language models such as ProtBert, achieved a maximum correlation of 0.89 with an R2 of 0.80, demonstrating improved performance in predicting protein stability at higher temperatures.\n\nTitle: Joint Modeling of Cellular Heterogeneity and Condition Effects with scPCA in Single-Cell RNA-Seq\nSummary: scRNA-seq in multi-condition experiments enables the systematic assessment of treatment effects by analyzing gene expression profiles.   scPCA is a flexible DR framework that jointly models cellular heterogeneity and conditioning variables, allowing for an integrated factor representation and revealing transcriptional changes across conditions and components.\n\nTitle: Identification of potential inhibitors against Inosine 5'-Monophosphate Dehydrogenase of Cryptosporidium parvum through an integrated in silico approach\nSummary: A total of 24 bioactive phytochemicals were screened virtually using molecular docking and ADMET analyses to identify potential inhibitors against Inosine 5'-Monophosphate Dehydrogenase (IMPDH) of Cryptosporidium parvum, with four lead compounds identified as Brevelin A, Vernodalin, Luteolin, and Pectolinarigenin. The lead compounds were found to possess favorable pharmacokinetic and pharmacodynamic properties, satisfactory toxicity analysis results, and no major side effects or violation of Lipinski's rules of five, indicating the possibility of oral bioavailability as potential drug candidates.\n\nTitle: Identification and Diagnostic Potential of Pyroptosis-Related Genes in Endometriosis: A Novel Bioinformatics Analysis\nSummary: Pyroptosis-related genes were identified through a bioinformatics analysis of endometriosis (EM) transcriptomic datasets, resulting in 26 differentially expressed genes that play a crucial role in the pathogenesis of EM.  A novel diagnostic model was constructed using LASSO regression based on pyroptosis scores, which included five key genes: KIF13B, BAG6, MYO5A, HEATR, and AK055981.\n\nTitle: Improving the accuracy of pose prediction by incorporating symmetry-related molecules\nSummary: The study aimed to improve the accuracy of pose prediction in molecular docking by incorporating symmetry-related molecules (SRMs). Redocking protein-ligand complexes with and without SRMs revealed that using SRMs significantly improved the prediction of biologically significant poses, as indicated by MM-GBSA calculations.\n\nTitle: Identification and study of Prolyl Oligopeptidases and related sequences in bacterial lineages\nSummary: The study examined ~32000 completely annotated bacterial genomes from the NCBI RefSeq Assembly database to identify annotated S9 family proteins, resulting in the discovery of ~53,000 bacterial S9 family proteins (referred to as POP homologues) which can be classified into distinct subfamilies through various machine-learning approaches and comprehensive analysis. These sequence homologues display distinct subclusters and class-specific motifs suggesting differences in substrate specificity in POP homologues.\n\nTitle: Learning-Augmented Sketching Offers Improved Performance for Privacy Preserving and Secure GWAS\nSummary: The introduction of trusted execution environments (TEEs) such as Intel SGX technology has enabled secure and privacy-preserving computation on the cloud, but stringent resource limitations pose a challenge for some TEEs. The SkSES method, which identifies significant SNPs in GWAS without disclosing sensitive genotype information, has been improved upon with a learning-augmented approach that achieves up to 40% accuracy gain compared to the original SkSES method.\n\nTitle: Liberality is More Explainable than PCA of Transcriptome for Vertebrate Embryo Development\nSummary: Liberality is a quantitative index of cellular differentiation and dedifferentiation that has been widely used for genome-scale data analysis, particularly in understanding vertebrate embryo development. The study analyzed a time course transcriptome dataset on vertebrate embryo development and found a trend that historically annotated embryo developmental stages matched changes in liberality, indicating the potential of liberality to analyze biological phenomena beyond just embryo development.\n\nTitle: Bacopa monnieri phytochemicals as promising BACE1 inhibitors for Alzheimers Disease Therapy\nSummary: Bacopa monnieri phytochemicals are investigated as potential BACE1 inhibitors for Alzheimer's Disease Therapy, with Bacopaside I showing superior binding affinity and interaction profile compared to established synthetic inhibitors. The study highlights the promising role of natural compounds in AD treatment, emphasizing their potential to overcome limitations faced in clinical settings, and advocates for a paradigm shift towards integrating traditional medicinal knowledge into contemporary drug discovery efforts.\n\nTitle: Accurate Multiple Sequence Alignment of Ultramassive Genome Sets\nSummary: The current state of multiple sequence alignment (MSA) is insufficient for handling ultramassive genome sets due to challenges in scalability and accuracy. The proposed algorithms, including directed acyclic graph construction, profile hidden Markov model training, and graph-based alignment, significantly improve accuracy and acceleration of MSA compared to widely used MAFFT for genome set sizes ranging from 40,000 to over 4 million.\n\nTitle: Machine Learning Driven Simulations of SARS-CoV-2 Fitness Landscape\nSummary: The SARS-CoV-2 infection is caused by interactions between the receptor binding domain of viral spike proteins and host cell ACE2 receptors, with mutations in the spike protein leading to neutralizing antibody escape and breakthrough infections. Machine learning-driven simulations combined with deep mutational scanning data predict variants of concern not seen in the training data and sample statistics of the fitness landscape, providing insight into the relationship between RBD sequence elements and emerging viral strains.\n\nTitle: Modelling dynamics of human NDPK hexamer structure, stability and interactions\nSummary: The precise assembly of the NDPK hexameric structure into homo- /hetero-oligomeric complexes is necessary for kinase activity but has been poorly understood due to high subunit homology, experimental challenges, and limited data on in vivo heterohexamer formation and subunit abundances across cellular compartments. A conserved Arg27 residue plays a key role in hexamer assembly, mediating inter- and intra-molecular monomeric interactions and ensuring similar hexameric assembly across subunits.\n\nTitle: GuaCAMOLE: GC-bias aware estimation improves the accuracy of metagenomic species abundances\nSummary: GuaCAMOLE is a novel computational method that detects and removes GC bias from metagenomic sequencing data, which affects the accuracy of quantifying microbial community compositions. The algorithm reports unbiased abundances and corrects the abundance of clinically relevant GC-poor species by up to a factor of two in gut microbiomes of colorectal cancer patients."</span>
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-  <p>Site built with <a href="https://pkgdown.r-lib.org/" class="external-link">pkgdown</a> 2.1.0.</p>
+  <p>Site built with <a href="https://pkgdown.r-lib.org/" class="external-link">pkgdown</a> 2.1.1.</p>
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     <a class="navbar-brand me-2" href="../index.html">biorecap</a>
 
-    <small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">0.1.1</small>
+    <small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">0.2.0</small>
 
 
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       <img src="../logo.png" class="logo" alt=""><h1>Example preprints with summaries</h1>
-      <small class="dont-index">Source: <a href="https://github.com/stephenturner/biorecap/blob/v0.1.1/R/data.R" class="external-link"><code>R/data.R</code></a></small>
+      <small class="dont-index">Source: <a href="https://github.com/stephenturner/biorecap/blob/main/R/data.R" class="external-link"><code>R/data.R</code></a></small>
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@@ -49,20 +49,20 @@ <h2 id="format">Format<a class="anchor" aria-label="anchor" href="#format"></a><
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     <h2 id="ref-examples">Examples<a class="anchor" aria-label="anchor" href="#ref-examples"></a></h2>
     <div class="sourceCode"><pre class="sourceCode r"><code><span class="r-in"><span><span class="va">example_preprints</span></span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="color: #949494;"># A tibble: 90 × 6</span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span>    subject        title                            url   abstract prompt summary</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span>    <span style="color: #949494; font-style: italic;">&lt;chr&gt;</span>          <span style="color: #949494; font-style: italic;">&lt;chr&gt;</span>                            <span style="color: #949494; font-style: italic;">&lt;chr&gt;</span> <span style="color: #949494; font-style: italic;">&lt;chr&gt;</span>    <span style="color: #949494; font-style: italic;">&lt;chr&gt;</span>  <span style="color: #949494; font-style: italic;">&lt;chr&gt;</span>  </span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="color: #BCBCBC;"> 1</span> bioinformatics Integrity and miss grouping as … http… <span style="color: #949494;">"</span>The hi… <span style="color: #949494;">"</span>I am… <span style="color: #949494;">"</span>The p…</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="color: #BCBCBC;"> 2</span> bioinformatics Sainsc: a computational tool fo… http… <span style="color: #949494;">"</span>Spatia… <span style="color: #949494;">"</span>I am… <span style="color: #949494;">"</span>Sains…</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="color: #BCBCBC;"> 3</span> bioinformatics BRACE: A novel Bayesian-based i… http… <span style="color: #949494;">"</span>Bayesi… <span style="color: #949494;">"</span>I am… <span style="color: #949494;">"</span>Alter…</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="color: #BCBCBC;"> 4</span> bioinformatics Topological embedding and direc… http… <span style="color: #949494;">"</span>Cancer… <span style="color: #949494;">"</span>I am… <span style="color: #949494;">"</span>Resea…</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="color: #BCBCBC;"> 5</span> bioinformatics SeuratExtend: Streamlining Sing… http… <span style="color: #949494;">"</span>Single… <span style="color: #949494;">"</span>I am… <span style="color: #949494;">"</span>Seura…</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="color: #BCBCBC;"> 6</span> bioinformatics An Evolutionary Statistics Tool… http… <span style="color: #949494;">"</span>We pre… <span style="color: #949494;">"</span>I am… <span style="color: #949494;">"</span>The \…</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="color: #BCBCBC;"> 7</span> bioinformatics A map of integrated cis-regulat… http… <span style="color: #949494;">"</span>Cis-re… <span style="color: #949494;">"</span>I am… <span style="color: #949494;">"</span>The a…</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="color: #BCBCBC;"> 8</span> bioinformatics MOSTPLAS: A Self-correction Mul… http… <span style="color: #949494;">"</span>Plasmi… <span style="color: #949494;">"</span>I am… <span style="color: #949494;">"</span>Plasm…</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="color: #BCBCBC;"> 9</span> bioinformatics Bootstrap Evaluation of Associa… http… <span style="color: #949494;">"</span>Motiva… <span style="color: #949494;">"</span>I am… <span style="color: #949494;">"</span>The a…</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="color: #BCBCBC;">10</span> bioinformatics Thermodynamic modeling of Csr/R… http… <span style="color: #949494;">"</span>Backgr… <span style="color: #949494;">"</span>I am… <span style="color: #949494;">"</span>Resea…</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="color: #949494;"># ℹ 80 more rows</span></span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="color: #949494;"># A tibble: 60 × 7</span></span>
+<span class="r-out co"><span class="r-pr">#&gt;</span>    source  subject        title                    url   abstract prompt summary</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span>    <span style="color: #949494; font-style: italic;">&lt;chr&gt;</span>   <span style="color: #949494; font-style: italic;">&lt;chr&gt;</span>          <span style="color: #949494; font-style: italic;">&lt;chr&gt;</span>                    <span style="color: #949494; font-style: italic;">&lt;chr&gt;</span> <span style="color: #949494; font-style: italic;">&lt;chr&gt;</span>    <span style="color: #949494; font-style: italic;">&lt;chr&gt;</span>  <span style="color: #949494; font-style: italic;">&lt;chr&gt;</span>  </span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="color: #BCBCBC;"> 1</span> bioRxiv bioinformatics MedGraphNet: Leveraging… http… Genetic… I am … MedGra…</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="color: #BCBCBC;"> 2</span> bioRxiv bioinformatics High-throughput bacteri… http… The com… I am … The co…</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="color: #BCBCBC;"> 3</span> bioRxiv bioinformatics scParadise: Tunable hig… http… scRNA-s… I am … scAdam…</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="color: #BCBCBC;"> 4</span> bioRxiv bioinformatics Camera Paths, Modeling,… http… The enh… I am … ArtiaX…</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="color: #BCBCBC;"> 5</span> bioRxiv bioinformatics dScaff - an automatic b… http… Rapid e… I am … dScaff…</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="color: #BCBCBC;"> 6</span> bioRxiv bioinformatics Jaeger: an accurate and… http… Abstrac… I am … Jaeger…</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="color: #BCBCBC;"> 7</span> bioRxiv bioinformatics AI-Augmented R-Group Ex… http… Efficie… I am … The pa…</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="color: #BCBCBC;"> 8</span> bioRxiv bioinformatics OPLS-based Multiclass C… http… Multicl… I am … OPLS-D…</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="color: #BCBCBC;"> 9</span> bioRxiv bioinformatics STANCE: a unified stati… http… A signi… I am … STANCE…</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="color: #BCBCBC;">10</span> bioRxiv bioinformatics AsaruSim: a single-cell… http… Motivat… I am … AsaruS…</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="color: #949494;"># ℹ 50 more rows</span></span>
 <span class="r-in"><span></span></span>
 </code></pre></div>
     </div>
@@ -75,7 +75,7 @@ <h2 id="ref-examples">Examples<a class="anchor" aria-label="anchor" href="#ref-e
 </div>
 
 <div class="pkgdown-footer-right">
-  <p>Site built with <a href="https://pkgdown.r-lib.org/" class="external-link">pkgdown</a> 2.1.0.</p>
+  <p>Site built with <a href="https://pkgdown.r-lib.org/" class="external-link">pkgdown</a> 2.1.1.</p>
 </div>
 
     </footer></div>
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diff --git a/reference/get_preprints.html b/reference/get_preprints.html
index 3608293..0fb3d23 100644
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@@ -1,5 +1,5 @@
 <!DOCTYPE html>
-<!-- Generated by pkgdown: do not edit by hand --><html lang="en"><head><meta http-equiv="Content-Type" content="text/html; charset=UTF-8"><meta charset="utf-8"><meta http-equiv="X-UA-Compatible" content="IE=edge"><meta name="viewport" content="width=device-width, initial-scale=1, shrink-to-fit=no"><title>Get bioRxiv preprints — get_preprints • biorecap</title><!-- favicons --><link rel="icon" type="image/png" sizes="16x16" href="../favicon-16x16.png"><link rel="icon" type="image/png" sizes="32x32" href="../favicon-32x32.png"><link rel="apple-touch-icon" type="image/png" sizes="180x180" href="../apple-touch-icon.png"><link rel="apple-touch-icon" type="image/png" sizes="120x120" href="../apple-touch-icon-120x120.png"><link rel="apple-touch-icon" type="image/png" sizes="76x76" href="../apple-touch-icon-76x76.png"><link rel="apple-touch-icon" type="image/png" sizes="60x60" href="../apple-touch-icon-60x60.png"><script src="../deps/jquery-3.6.0/jquery-3.6.0.min.js"></script><meta name="viewport" content="width=device-width, initial-scale=1, shrink-to-fit=no"><link href="../deps/bootstrap-5.3.1/bootstrap.min.css" rel="stylesheet"><script src="../deps/bootstrap-5.3.1/bootstrap.bundle.min.js"></script><link href="../deps/font-awesome-6.4.2/css/all.min.css" rel="stylesheet"><link href="../deps/font-awesome-6.4.2/css/v4-shims.min.css" rel="stylesheet"><script src="../deps/headroom-0.11.0/headroom.min.js"></script><script src="../deps/headroom-0.11.0/jQuery.headroom.min.js"></script><script src="../deps/bootstrap-toc-1.0.1/bootstrap-toc.min.js"></script><script src="../deps/clipboard.js-2.0.11/clipboard.min.js"></script><script src="../deps/search-1.0.0/autocomplete.jquery.min.js"></script><script src="../deps/search-1.0.0/fuse.min.js"></script><script src="../deps/search-1.0.0/mark.min.js"></script><!-- pkgdown --><script src="../pkgdown.js"></script><meta property="og:title" content="Get bioRxiv preprints — get_preprints"><meta name="description" content="Get bioRxiv preprints"><meta property="og:description" content="Get bioRxiv preprints"><meta property="og:image" content="https://stephenturner.github.io/biorecap/logo.png"></head><body>
+<!-- Generated by pkgdown: do not edit by hand --><html lang="en"><head><meta http-equiv="Content-Type" content="text/html; charset=UTF-8"><meta charset="utf-8"><meta http-equiv="X-UA-Compatible" content="IE=edge"><meta name="viewport" content="width=device-width, initial-scale=1, shrink-to-fit=no"><title>Get bioRxiv/medRxiv preprints — get_preprints • biorecap</title><!-- favicons --><link rel="icon" type="image/png" sizes="16x16" href="../favicon-16x16.png"><link rel="icon" type="image/png" sizes="32x32" href="../favicon-32x32.png"><link rel="apple-touch-icon" type="image/png" sizes="180x180" href="../apple-touch-icon.png"><link rel="apple-touch-icon" type="image/png" sizes="120x120" href="../apple-touch-icon-120x120.png"><link rel="apple-touch-icon" type="image/png" sizes="76x76" href="../apple-touch-icon-76x76.png"><link rel="apple-touch-icon" type="image/png" sizes="60x60" href="../apple-touch-icon-60x60.png"><script src="../deps/jquery-3.6.0/jquery-3.6.0.min.js"></script><meta name="viewport" content="width=device-width, initial-scale=1, shrink-to-fit=no"><link href="../deps/bootstrap-5.3.1/bootstrap.min.css" rel="stylesheet"><script src="../deps/bootstrap-5.3.1/bootstrap.bundle.min.js"></script><link href="../deps/font-awesome-6.4.2/css/all.min.css" rel="stylesheet"><link href="../deps/font-awesome-6.4.2/css/v4-shims.min.css" rel="stylesheet"><script src="../deps/headroom-0.11.0/headroom.min.js"></script><script src="../deps/headroom-0.11.0/jQuery.headroom.min.js"></script><script src="../deps/bootstrap-toc-1.0.1/bootstrap-toc.min.js"></script><script src="../deps/clipboard.js-2.0.11/clipboard.min.js"></script><script src="../deps/search-1.0.0/autocomplete.jquery.min.js"></script><script src="../deps/search-1.0.0/fuse.min.js"></script><script src="../deps/search-1.0.0/mark.min.js"></script><!-- pkgdown --><script src="../pkgdown.js"></script><meta property="og:title" content="Get bioRxiv/medRxiv preprints — get_preprints"><meta name="description" content="Get bioRxiv/medRxiv preprints"><meta property="og:description" content="Get bioRxiv/medRxiv preprints"><meta property="og:image" content="https://stephenturner.github.io/biorecap/logo.png"></head><body>
     <a href="#main" class="visually-hidden-focusable">Skip to contents</a>
 
 
@@ -7,7 +7,7 @@
 
     <a class="navbar-brand me-2" href="../index.html">biorecap</a>
 
-    <small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">0.1.1</small>
+    <small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">0.2.0</small>
 
 
     <button class="navbar-toggler" type="button" data-bs-toggle="collapse" data-bs-target="#navbar" aria-controls="navbar" aria-expanded="false" aria-label="Toggle navigation">
@@ -27,22 +27,18 @@
 </nav><div class="container template-reference-topic">
 <div class="row">
   <main id="main" class="col-md-9"><div class="page-header">
-      <img src="../logo.png" class="logo" alt=""><h1>Get bioRxiv preprints</h1>
-      <small class="dont-index">Source: <a href="https://github.com/stephenturner/biorecap/blob/v0.1.1/R/biorecap.R" class="external-link"><code>R/biorecap.R</code></a></small>
+      <img src="../logo.png" class="logo" alt=""><h1>Get bioRxiv/medRxiv preprints</h1>
+      <small class="dont-index">Source: <a href="https://github.com/stephenturner/biorecap/blob/main/R/biorecap.R" class="external-link"><code>R/biorecap.R</code></a></small>
       <div class="d-none name"><code>get_preprints.Rd</code></div>
     </div>
 
     <div class="ref-description section level2">
-    <p>Get bioRxiv preprints</p>
+    <p>Get bioRxiv/medRxiv preprints</p>
     </div>
 
     <div class="section level2">
     <h2 id="ref-usage">Usage<a class="anchor" aria-label="anchor" href="#ref-usage"></a></h2>
-    <div class="sourceCode"><pre class="sourceCode r"><code><span><span class="fu">get_preprints</span><span class="op">(</span></span>
-<span>  subject <span class="op">=</span> <span class="st">"all"</span>,</span>
-<span>  baseurl <span class="op">=</span> <span class="st">"https://connect.biorxiv.org/biorxiv_xml.php?subject="</span>,</span>
-<span>  clean <span class="op">=</span> <span class="cn">TRUE</span></span>
-<span><span class="op">)</span></span></code></pre></div>
+    <div class="sourceCode"><pre class="sourceCode r"><code><span><span class="fu">get_preprints</span><span class="op">(</span>subject <span class="op">=</span> <span class="st">"all"</span>, clean <span class="op">=</span> <span class="cn">TRUE</span><span class="op">)</span></span></code></pre></div>
     </div>
 
     <div class="section level2">
@@ -50,11 +46,7 @@ <h2 id="arguments">Arguments<a class="anchor" aria-label="anchor" href="#argumen
 
 
 <dl><dt id="arg-subject">subject<a class="anchor" aria-label="anchor" href="#arg-subject"></a></dt>
-<dd><p>A character vector of valid biorxiv subjects. See <a href="subjects.html">subjects</a>.</p></dd>
-
-
-<dt id="arg-baseurl">baseurl<a class="anchor" aria-label="anchor" href="#arg-baseurl"></a></dt>
-<dd><p>The base URL for the biorxiv RSS feed. Default is <code>https://connect.biorxiv.org/biorxiv_xml.php?subject=</code>. Do not change unless you know what you are doing.</p></dd>
+<dd><p>A character vector of valid bioRxiv and/or medRxiv subjects. See <a href="subjects.html">subjects</a>.</p></dd>
 
 
 <dt id="arg-clean">clean<a class="anchor" aria-label="anchor" href="#arg-clean"></a></dt>
@@ -63,26 +55,26 @@ <h2 id="arguments">Arguments<a class="anchor" aria-label="anchor" href="#argumen
 </dl></div>
     <div class="section level2">
     <h2 id="value">Value<a class="anchor" aria-label="anchor" href="#value"></a></h2>
-    <p>A data frame of bioRxiv preprints.</p>
+    <p>A data frame of preprints from bioRxiv and/or medRxiv.</p>
     </div>
 
     <div class="section level2">
     <h2 id="ref-examples">Examples<a class="anchor" aria-label="anchor" href="#ref-examples"></a></h2>
-    <div class="sourceCode"><pre class="sourceCode r"><code><span class="r-in"><span><span class="va">preprints</span> <span class="op">&lt;-</span> <span class="fu">get_preprints</span><span class="op">(</span>subject<span class="op">=</span><span class="fu"><a href="https://rdrr.io/r/base/c.html" class="external-link">c</a></span><span class="op">(</span><span class="st">"bioinformatics"</span>, <span class="st">"genomics"</span><span class="op">)</span><span class="op">)</span></span></span>
+    <div class="sourceCode"><pre class="sourceCode r"><code><span class="r-in"><span><span class="va">preprints</span> <span class="op">&lt;-</span> <span class="fu">get_preprints</span><span class="op">(</span>subject<span class="op">=</span><span class="fu"><a href="https://rdrr.io/r/base/c.html" class="external-link">c</a></span><span class="op">(</span><span class="st">"bioinformatics"</span>, <span class="st">"Public_and_Global_Health"</span><span class="op">)</span><span class="op">)</span></span></span>
 <span class="r-in"><span><span class="va">preprints</span></span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="color: #949494;"># A tibble: 60 × 4</span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span>    subject        title                                           url   abstract</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span>    <span style="color: #949494; font-style: italic;">&lt;chr&gt;</span>          <span style="color: #949494; font-style: italic;">&lt;chr&gt;</span>                                           <span style="color: #949494; font-style: italic;">&lt;chr&gt;</span> <span style="color: #949494; font-style: italic;">&lt;chr&gt;</span>   </span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="color: #BCBCBC;"> 1</span> bioinformatics <span style="color: #949494;">"</span>PHIStruct: Improving phage-host interaction p… http… Recent …</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="color: #BCBCBC;"> 2</span> bioinformatics <span style="color: #949494;">"</span>BGC Atlas: A Web Resource for Exploring the G… http… Seconda…</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="color: #BCBCBC;"> 3</span> bioinformatics <span style="color: #949494;">"</span>Enrichment analysis for spatial and single-ce… http… Imaging…</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="color: #BCBCBC;"> 4</span> bioinformatics <span style="color: #949494;">"</span>SpatialLeiden - Spatially-aware Leiden cluste… http… Cluster…</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="color: #BCBCBC;"> 5</span> bioinformatics <span style="color: #949494;">"</span>Protein sequence classification using natural… http… Protein…</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="color: #BCBCBC;"> 6</span> bioinformatics <span style="color: #949494;">"</span>A multi-omics approach to identify deleteriou… http… Crops l…</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="color: #BCBCBC;"> 7</span> bioinformatics <span style="color: #949494;">"</span>Automatic crystal identification for crystall… http… Crystal…</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="color: #BCBCBC;"> 8</span> bioinformatics <span style="color: #949494;">"</span>Solving the \"Blind men and the elephant prob… http… Biologi…</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="color: #BCBCBC;"> 9</span> bioinformatics <span style="color: #949494;">"</span>Evolutionary mismatch between nuclear and mit… http… Serrano…</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="color: #BCBCBC;">10</span> bioinformatics <span style="color: #949494;">"</span>Predicting small-molecule inhibition of prote… http… Protein…</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="color: #949494;"># A tibble: 60 × 5</span></span>
+<span class="r-out co"><span class="r-pr">#&gt;</span>    source  subject        title                                   url   abstract</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span>    <span style="color: #949494; font-style: italic;">&lt;chr&gt;</span>   <span style="color: #949494; font-style: italic;">&lt;chr&gt;</span>          <span style="color: #949494; font-style: italic;">&lt;chr&gt;</span>                                   <span style="color: #949494; font-style: italic;">&lt;chr&gt;</span> <span style="color: #949494; font-style: italic;">&lt;chr&gt;</span>   </span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="color: #BCBCBC;"> 1</span> bioRxiv bioinformatics SeaMoon: Prediction of molecular motio… http… How pro…</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="color: #BCBCBC;"> 2</span> bioRxiv bioinformatics Clustering individuals using INMTD: a … http… Motivat…</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="color: #BCBCBC;"> 3</span> bioRxiv bioinformatics INSPIRE: interpretable, flexible and s… http… Recent …</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="color: #BCBCBC;"> 4</span> bioRxiv bioinformatics MedGraphNet: Leveraging Multi-Relation… http… Genetic…</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="color: #BCBCBC;"> 5</span> bioRxiv bioinformatics High-throughput bacterial aggregation … http… The com…</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="color: #BCBCBC;"> 6</span> bioRxiv bioinformatics scParadise: Tunable highly accurate mu… http… scRNA-s…</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="color: #BCBCBC;"> 7</span> bioRxiv bioinformatics Camera Paths, Modeling, and Image Proc… http… The enh…</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="color: #BCBCBC;"> 8</span> bioRxiv bioinformatics dScaff - an automatic bioinformatics f… http… Rapid e…</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="color: #BCBCBC;"> 9</span> bioRxiv bioinformatics Jaeger: an accurate and fast deep-lear… http… Abstrac…</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="color: #BCBCBC;">10</span> bioRxiv bioinformatics AI-Augmented R-Group Exploration in Me… http… Efficie…</span>
 <span class="r-out co"><span class="r-pr">#&gt;</span> <span style="color: #949494;"># ℹ 50 more rows</span></span>
 <span class="r-in"><span></span></span>
 </code></pre></div>
@@ -96,7 +88,7 @@ <h2 id="ref-examples">Examples<a class="anchor" aria-label="anchor" href="#ref-e
 </div>
 
 <div class="pkgdown-footer-right">
-  <p>Site built with <a href="https://pkgdown.r-lib.org/" class="external-link">pkgdown</a> 2.1.0.</p>
+  <p>Site built with <a href="https://pkgdown.r-lib.org/" class="external-link">pkgdown</a> 2.1.1.</p>
 </div>
 
     </footer></div>
diff --git a/reference/index.html b/reference/index.html
index 1ffab13..84cea25 100644
--- a/reference/index.html
+++ b/reference/index.html
@@ -7,7 +7,7 @@
 
     <a class="navbar-brand me-2" href="../index.html">biorecap</a>
 
-    <small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">0.1.1</small>
+    <small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">0.2.0</small>
 
 
     <button class="navbar-toggler" type="button" data-bs-toggle="collapse" data-bs-target="#navbar" aria-controls="navbar" aria-expanded="false" aria-label="Toggle navigation">
@@ -64,7 +64,7 @@ <h2 id="all-functions">All functions<a class="anchor" aria-label="anchor" href="
           <code><a href="biorecap_report.html">biorecap_report()</a></code>
 
         </dt>
-        <dd>Create a report from bioRxiv preprints</dd>
+        <dd>Create a report from bioRxiv/medRxiv preprints</dd>
       </dl><dl><dt>
 
           <code><a href="build_prompt_preprint.html">build_prompt_preprint()</a></code>
@@ -88,7 +88,7 @@ <h2 id="all-functions">All functions<a class="anchor" aria-label="anchor" href="
           <code><a href="get_preprints.html">get_preprints()</a></code>
 
         </dt>
-        <dd>Get bioRxiv preprints</dd>
+        <dd>Get bioRxiv/medRxiv preprints</dd>
       </dl><dl><dt>
 
           <code><a href="subjects.html">subjects</a></code>
@@ -110,7 +110,7 @@ <h2 id="all-functions">All functions<a class="anchor" aria-label="anchor" href="
 </div>
 
 <div class="pkgdown-footer-right">
-  <p>Site built with <a href="https://pkgdown.r-lib.org/" class="external-link">pkgdown</a> 2.1.0.</p>
+  <p>Site built with <a href="https://pkgdown.r-lib.org/" class="external-link">pkgdown</a> 2.1.1.</p>
 </div>
 
     </footer></div>
diff --git a/reference/reexports.html b/reference/reexports.html
index 4aebdc5..90ec5c9 100644
--- a/reference/reexports.html
+++ b/reference/reexports.html
@@ -21,7 +21,7 @@
 
     <a class="navbar-brand me-2" href="../index.html">biorecap</a>
 
-    <small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">0.1.1</small>
+    <small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">0.2.0</small>
 
 
     <button class="navbar-toggler" type="button" data-bs-toggle="collapse" data-bs-target="#navbar" aria-controls="navbar" aria-expanded="false" aria-label="Toggle navigation">
@@ -42,7 +42,7 @@
 <div class="row">
   <main id="main" class="col-md-9"><div class="page-header">
       <img src="../logo.png" class="logo" alt=""><h1>Objects exported from other packages</h1>
-      <small class="dont-index">Source: <a href="https://github.com/stephenturner/biorecap/blob/v0.1.1/R/reexports.R" class="external-link"><code>R/reexports.R</code></a></small>
+      <small class="dont-index">Source: <a href="https://github.com/stephenturner/biorecap/blob/main/R/reexports.R" class="external-link"><code>R/reexports.R</code></a></small>
       <div class="d-none name"><code>reexports.Rd</code></div>
     </div>
 
@@ -65,7 +65,7 @@
 </div>
 
 <div class="pkgdown-footer-right">
-  <p>Site built with <a href="https://pkgdown.r-lib.org/" class="external-link">pkgdown</a> 2.1.0.</p>
+  <p>Site built with <a href="https://pkgdown.r-lib.org/" class="external-link">pkgdown</a> 2.1.1.</p>
 </div>
 
     </footer></div>
diff --git a/reference/subjects.html b/reference/subjects.html
index df3e857..9353a13 100644
--- a/reference/subjects.html
+++ b/reference/subjects.html
@@ -7,7 +7,7 @@
 
     <a class="navbar-brand me-2" href="../index.html">biorecap</a>
 
-    <small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">0.1.1</small>
+    <small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">0.2.0</small>
 
 
     <button class="navbar-toggler" type="button" data-bs-toggle="collapse" data-bs-target="#navbar" aria-controls="navbar" aria-expanded="false" aria-label="Toggle navigation">
@@ -28,7 +28,7 @@
 <div class="row">
   <main id="main" class="col-md-9"><div class="page-header">
       <img src="../logo.png" class="logo" alt=""><h1>bioRxiv subjects</h1>
-      <small class="dont-index">Source: <a href="https://github.com/stephenturner/biorecap/blob/v0.1.1/R/data.R" class="external-link"><code>R/data.R</code></a></small>
+      <small class="dont-index">Source: <a href="https://github.com/stephenturner/biorecap/blob/main/R/data.R" class="external-link"><code>R/data.R</code></a></small>
       <div class="d-none name"><code>subjects.Rd</code></div>
     </div>
 
@@ -43,7 +43,7 @@ <h2 id="ref-usage">Usage<a class="anchor" aria-label="anchor" href="#ref-usage">
 
     <div class="section level2">
     <h2 id="format">Format<a class="anchor" aria-label="anchor" href="#format"></a></h2>
-    <p>A character vector</p>
+    <p>A list of character vectors of subjects, one for bioRxiv, one for medRxiv.</p>
     </div>
     <div class="section level2">
     <h2 id="references">References<a class="anchor" aria-label="anchor" href="#references"></a></h2>
@@ -53,6 +53,7 @@ <h2 id="references">References<a class="anchor" aria-label="anchor" href="#refer
     <div class="section level2">
     <h2 id="ref-examples">Examples<a class="anchor" aria-label="anchor" href="#ref-examples"></a></h2>
     <div class="sourceCode"><pre class="sourceCode r"><code><span class="r-in"><span><span class="va">subjects</span></span></span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> $biorxiv</span>
 <span class="r-out co"><span class="r-pr">#&gt;</span>  [1] "all"                                   </span>
 <span class="r-out co"><span class="r-pr">#&gt;</span>  [2] "animal_behavior_and_cognition"         </span>
 <span class="r-out co"><span class="r-pr">#&gt;</span>  [3] "biochemistry"                          </span>
@@ -80,6 +81,63 @@ <h2 id="ref-examples">Examples<a class="anchor" aria-label="anchor" href="#ref-e
 <span class="r-out co"><span class="r-pr">#&gt;</span> [25] "synthetic_biology"                     </span>
 <span class="r-out co"><span class="r-pr">#&gt;</span> [26] "systems_biology"                       </span>
 <span class="r-out co"><span class="r-pr">#&gt;</span> [27] "zoology"                               </span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> </span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> $medrxiv</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span>  [1] "all"                                         </span>
+<span class="r-out co"><span class="r-pr">#&gt;</span>  [2] "addiction_medicine"                          </span>
+<span class="r-out co"><span class="r-pr">#&gt;</span>  [3] "allergy_and_immunology"                      </span>
+<span class="r-out co"><span class="r-pr">#&gt;</span>  [4] "anesthesia"                                  </span>
+<span class="r-out co"><span class="r-pr">#&gt;</span>  [5] "cardiovascular_medicine"                     </span>
+<span class="r-out co"><span class="r-pr">#&gt;</span>  [6] "dentistry_and_oral_medicine"                 </span>
+<span class="r-out co"><span class="r-pr">#&gt;</span>  [7] "dermatology"                                 </span>
+<span class="r-out co"><span class="r-pr">#&gt;</span>  [8] "dermatology"                                 </span>
+<span class="r-out co"><span class="r-pr">#&gt;</span>  [9] "endocrinology"                               </span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> [10] "epidemiology"                                </span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> [11] "ecology"                                     </span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> [12] "epidemiology"                                </span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> [13] "forensic_medicine"                           </span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> [14] "gastroenterology"                            </span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> [15] "genetic_and_genomic_medicine"                </span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> [16] "geriatric_medicine"                          </span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> [17] "health_economics"                            </span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> [18] "health_informatics"                          </span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> [19] "health_policy"                               </span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> [20] "health_systems_and_quality_improvement"      </span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> [21] "hematology"                                  </span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> [22] "hivaids"                                     </span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> [23] "infectious_diseases"                         </span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> [24] "intensive_care_and_critical_care_medicine"   </span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> [25] "medical_education"                           </span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> [26] "medical_ethics"                              </span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> [27] "nephrology"                                  </span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> [28] "neurology"                                   </span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> [29] "nursing"                                     </span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> [30] "nutrition"                                   </span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> [31] "obstetrics_and_gynecology"                   </span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> [32] "occupational_and_environmental_health"       </span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> [33] "oncology"                                    </span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> [34] "ophthalmology"                               </span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> [35] "orthopedics"                                 </span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> [36] "otolaryngology"                              </span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> [37] "pain_medicine"                               </span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> [38] "palliative_medicine"                         </span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> [39] "pathology"                                   </span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> [40] "pediatrics"                                  </span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> [41] "pharmacology_and_therapeutics"               </span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> [42] "primary_care_research"                       </span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> [43] "psychiatry_and_clinical_psychology"          </span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> [44] "public_and_global_health"                    </span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> [45] "radiology_and_imaging"                       </span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> [46] "rehabilitation_medicine_and_physical_therapy"</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> [47] "respiratory_medicine"                        </span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> [48] "rheumatology"                                </span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> [49] "sexual_and_reproductive_health"              </span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> [50] "sports_medicine"                             </span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> [51] "surgery"                                     </span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> [52] "toxicology"                                  </span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> [53] "transplantation"                             </span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> [54] "urology"                                     </span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> </span>
 <span class="r-in"><span></span></span>
 </code></pre></div>
     </div>
@@ -92,7 +150,7 @@ <h2 id="ref-examples">Examples<a class="anchor" aria-label="anchor" href="#ref-e
 </div>
 
 <div class="pkgdown-footer-right">
-  <p>Site built with <a href="https://pkgdown.r-lib.org/" class="external-link">pkgdown</a> 2.1.0.</p>
+  <p>Site built with <a href="https://pkgdown.r-lib.org/" class="external-link">pkgdown</a> 2.1.1.</p>
 </div>
 
     </footer></div>
diff --git a/reference/tt_preprints.html b/reference/tt_preprints.html
index e7a44cc..a0e8097 100644
--- a/reference/tt_preprints.html
+++ b/reference/tt_preprints.html
@@ -7,7 +7,7 @@
 
     <a class="navbar-brand me-2" href="../index.html">biorecap</a>
 
-    <small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">0.1.1</small>
+    <small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">0.2.0</small>
 
 
     <button class="navbar-toggler" type="button" data-bs-toggle="collapse" data-bs-target="#navbar" aria-controls="navbar" aria-expanded="false" aria-label="Toggle navigation">
@@ -28,7 +28,7 @@
 <div class="row">
   <main id="main" class="col-md-9"><div class="page-header">
       <img src="../logo.png" class="logo" alt=""><h1>Create a markdown table from prepreprint summaries</h1>
-      <small class="dont-index">Source: <a href="https://github.com/stephenturner/biorecap/blob/v0.1.1/R/biorecap.R" class="external-link"><code>R/biorecap.R</code></a></small>
+      <small class="dont-index">Source: <a href="https://github.com/stephenturner/biorecap/blob/main/R/biorecap.R" class="external-link"><code>R/biorecap.R</code></a></small>
       <div class="d-none name"><code>tt_preprints.Rd</code></div>
     </div>
 
@@ -66,29 +66,29 @@ <h2 id="value">Value<a class="anchor" aria-label="anchor" href="#value"></a></h2
     <h2 id="ref-examples">Examples<a class="anchor" aria-label="anchor" href="#ref-examples"></a></h2>
     <div class="sourceCode"><pre class="sourceCode r"><code><span class="r-in"><span><span class="co"># Use built-in example data</span></span></span>
 <span class="r-in"><span><span class="va">example_preprints</span></span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="color: #949494;"># A tibble: 90 × 6</span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span>    subject        title                            url   abstract prompt summary</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span>    <span style="color: #949494; font-style: italic;">&lt;chr&gt;</span>          <span style="color: #949494; font-style: italic;">&lt;chr&gt;</span>                            <span style="color: #949494; font-style: italic;">&lt;chr&gt;</span> <span style="color: #949494; font-style: italic;">&lt;chr&gt;</span>    <span style="color: #949494; font-style: italic;">&lt;chr&gt;</span>  <span style="color: #949494; font-style: italic;">&lt;chr&gt;</span>  </span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="color: #BCBCBC;"> 1</span> bioinformatics Integrity and miss grouping as … http… <span style="color: #949494;">"</span>The hi… <span style="color: #949494;">"</span>I am… <span style="color: #949494;">"</span>The p…</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="color: #BCBCBC;"> 2</span> bioinformatics Sainsc: a computational tool fo… http… <span style="color: #949494;">"</span>Spatia… <span style="color: #949494;">"</span>I am… <span style="color: #949494;">"</span>Sains…</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="color: #BCBCBC;"> 3</span> bioinformatics BRACE: A novel Bayesian-based i… http… <span style="color: #949494;">"</span>Bayesi… <span style="color: #949494;">"</span>I am… <span style="color: #949494;">"</span>Alter…</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="color: #BCBCBC;"> 4</span> bioinformatics Topological embedding and direc… http… <span style="color: #949494;">"</span>Cancer… <span style="color: #949494;">"</span>I am… <span style="color: #949494;">"</span>Resea…</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="color: #BCBCBC;"> 5</span> bioinformatics SeuratExtend: Streamlining Sing… http… <span style="color: #949494;">"</span>Single… <span style="color: #949494;">"</span>I am… <span style="color: #949494;">"</span>Seura…</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="color: #BCBCBC;"> 6</span> bioinformatics An Evolutionary Statistics Tool… http… <span style="color: #949494;">"</span>We pre… <span style="color: #949494;">"</span>I am… <span style="color: #949494;">"</span>The \…</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="color: #BCBCBC;"> 7</span> bioinformatics A map of integrated cis-regulat… http… <span style="color: #949494;">"</span>Cis-re… <span style="color: #949494;">"</span>I am… <span style="color: #949494;">"</span>The a…</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="color: #BCBCBC;"> 8</span> bioinformatics MOSTPLAS: A Self-correction Mul… http… <span style="color: #949494;">"</span>Plasmi… <span style="color: #949494;">"</span>I am… <span style="color: #949494;">"</span>Plasm…</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="color: #BCBCBC;"> 9</span> bioinformatics Bootstrap Evaluation of Associa… http… <span style="color: #949494;">"</span>Motiva… <span style="color: #949494;">"</span>I am… <span style="color: #949494;">"</span>The a…</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="color: #BCBCBC;">10</span> bioinformatics Thermodynamic modeling of Csr/R… http… <span style="color: #949494;">"</span>Backgr… <span style="color: #949494;">"</span>I am… <span style="color: #949494;">"</span>Resea…</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="color: #949494;"># ℹ 80 more rows</span></span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="color: #949494;"># A tibble: 60 × 7</span></span>
+<span class="r-out co"><span class="r-pr">#&gt;</span>    source  subject        title                    url   abstract prompt summary</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span>    <span style="color: #949494; font-style: italic;">&lt;chr&gt;</span>   <span style="color: #949494; font-style: italic;">&lt;chr&gt;</span>          <span style="color: #949494; font-style: italic;">&lt;chr&gt;</span>                    <span style="color: #949494; font-style: italic;">&lt;chr&gt;</span> <span style="color: #949494; font-style: italic;">&lt;chr&gt;</span>    <span style="color: #949494; font-style: italic;">&lt;chr&gt;</span>  <span style="color: #949494; font-style: italic;">&lt;chr&gt;</span>  </span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="color: #BCBCBC;"> 1</span> bioRxiv bioinformatics MedGraphNet: Leveraging… http… Genetic… I am … MedGra…</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="color: #BCBCBC;"> 2</span> bioRxiv bioinformatics High-throughput bacteri… http… The com… I am … The co…</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="color: #BCBCBC;"> 3</span> bioRxiv bioinformatics scParadise: Tunable hig… http… scRNA-s… I am … scAdam…</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="color: #BCBCBC;"> 4</span> bioRxiv bioinformatics Camera Paths, Modeling,… http… The enh… I am … ArtiaX…</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="color: #BCBCBC;"> 5</span> bioRxiv bioinformatics dScaff - an automatic b… http… Rapid e… I am … dScaff…</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="color: #BCBCBC;"> 6</span> bioRxiv bioinformatics Jaeger: an accurate and… http… Abstrac… I am … Jaeger…</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="color: #BCBCBC;"> 7</span> bioRxiv bioinformatics AI-Augmented R-Group Ex… http… Efficie… I am … The pa…</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="color: #BCBCBC;"> 8</span> bioRxiv bioinformatics OPLS-based Multiclass C… http… Multicl… I am … OPLS-D…</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="color: #BCBCBC;"> 9</span> bioRxiv bioinformatics STANCE: a unified stati… http… A signi… I am … STANCE…</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="color: #BCBCBC;">10</span> bioRxiv bioinformatics AsaruSim: a single-cell… http… Motivat… I am … AsaruS…</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="color: #949494;"># ℹ 50 more rows</span></span>
 <span class="r-in"><span><span class="fu">tt_preprints</span><span class="op">(</span><span class="va">example_preprints</span><span class="op">[</span><span class="fl">1</span><span class="op">:</span><span class="fl">2</span>,<span class="op">]</span><span class="op">)</span></span></span>
 <span class="r-out co"><span class="r-pr">#&gt;</span> </span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> +-------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------+-----------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------+</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> | title                                                                                                                                                                               | summary                                                                                                                                                                                                                                                                                                                                                                                                                                                                                 |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> +=====================================================================================================================================================================================+=========================================================================================================================================================================================================================================================================================================================================================================================================================================================================================+</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> | [Integrity and miss grouping as support for clusters in agglomerative hierarchical methods: the R-package octopucs](http://biorxiv.org/cgi/content/short/2024.08.01.606070v1?rss=1) | The proposed method assesses cluster support throughout hierarchical analyses by compiling a consensus topology and using ecological concepts of reciprocal complementarities to define cluster integrity and contamination. This approach allows for building support for groups even when there is partial membership match after resampling, and was implemented in the R package octopucs, which showed robust detection of changes in group memberships compared to other methods. |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> +-------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------+-----------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------+</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> | [Sainsc: a computational tool for segmentation-free analysis of in-situ capture](http://biorxiv.org/cgi/content/short/2024.08.02.603879v1?rss=1)                                    | Sainsc is a computational tool that enables segmentation-free analysis of spatially resolved transcriptomics data, allowing for accurate cell-type assignment at the subcellular level without requiring manual cell border delineation. The tool provides efficient processing of high-resolution spatial data and can generate maps of cell types with corresponding confidence scores, making it a valuable resource for biomedical researchers working with complex tissue samples. |</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> +-------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------+-----------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------+ </span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> +--------------------------------------------------------------------------------------------------------------------------------------------------------------------------------+----------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------+</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> | title                                                                                                                                                                          | summary                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                            |</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> +================================================================================================================================================================================+====================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================+</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> | [MedGraphNet: Leveraging Multi-Relational Graph Neural Networks and Text Knowledge for Biomedical Predictions](http://biorxiv.org/cgi/content/short/2024.09.24.614782v1?rss=1) | MedGraphNet leverages multi-relational Graph Neural Networks and text knowledge to improve biomedical predictions by initializing nodes using informative embeddings from existing text knowledge, allowing for robust integration of various data types and improved generalizability. The model demonstrates superior performance compared to traditional single-relation approaches in scenarios with isolated or sparsely connected nodes, particularly in identifying disease-gene associations and drug-phenotype relationships, and shows promising results in accurately inferring drug side effects without direct training on such data. |</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> +--------------------------------------------------------------------------------------------------------------------------------------------------------------------------------+----------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------+</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> | [High-throughput bacterial aggregation analysis in droplets](http://biorxiv.org/cgi/content/short/2024.09.24.613170v1?rss=1)                                                   | The communal lifestyle of bacteria can contribute significantly to antimicrobial resistance by promoting biofilm formation. A key approach to addressing this issue is to develop novel techniques for analyzing bacterial behavior, such as those enabled by droplet-based platforms and image analysis methods.                                                                                                                                                                                                                                                                                                                                  |</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> +--------------------------------------------------------------------------------------------------------------------------------------------------------------------------------+----------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------+ </span>
 </code></pre></div>
     </div>
   </main><aside class="col-md-3"><nav id="toc" aria-label="Table of contents"><h2>On this page</h2>
@@ -100,7 +100,7 @@ <h2 id="ref-examples">Examples<a class="anchor" aria-label="anchor" href="#ref-e
 </div>
 
 <div class="pkgdown-footer-right">
-  <p>Site built with <a href="https://pkgdown.r-lib.org/" class="external-link">pkgdown</a> 2.1.0.</p>
+  <p>Site built with <a href="https://pkgdown.r-lib.org/" class="external-link">pkgdown</a> 2.1.1.</p>
 </div>
 
     </footer></div>
diff --git a/search.json b/search.json
index b566256..db6b16b 100644
--- a/search.json
+++ b/search.json
@@ -1 +1 @@
-[{"path":"https://stephenturner.github.io/biorecap/CONTRIBUTING.html","id":null,"dir":"","previous_headings":"","what":"Contributing to biorecap","title":"Contributing to biorecap","text":"outlines propose change biorecap. detailed discussion contributing tidyverse packages, please see development contributing guide code review principles.","code":""},{"path":"https://stephenturner.github.io/biorecap/CONTRIBUTING.html","id":"fixing-typos","dir":"","previous_headings":"","what":"Fixing typos","title":"Contributing to biorecap","text":"can fix typos, spelling mistakes, grammatical errors documentation directly using GitHub web interface, long changes made source file. generally means ’ll need edit roxygen2 comments .R, .Rd file. can find .R file generates .Rd reading comment first line.","code":""},{"path":"https://stephenturner.github.io/biorecap/CONTRIBUTING.html","id":"bigger-changes","dir":"","previous_headings":"","what":"Bigger changes","title":"Contributing to biorecap","text":"want make bigger change, ’s good idea first file issue make sure someone team agrees ’s needed. ’ve found bug, please file issue illustrates bug minimal reprex (also help write unit test, needed). See guide create great issue advice.","code":""},{"path":"https://stephenturner.github.io/biorecap/CONTRIBUTING.html","id":"pull-request-process","dir":"","previous_headings":"Bigger changes","what":"Pull request process","title":"Contributing to biorecap","text":"Fork package clone onto computer. haven’t done , recommend using usethis::create_from_github(\"stephenturner/biorecap\", fork = TRUE). Install development dependencies devtools::install_dev_deps(), make sure package passes R CMD check running devtools::check(). R CMD check doesn’t pass cleanly, ’s good idea ask help continuing. Create Git branch pull request (PR). recommend using usethis::pr_init(\"brief-description--change\"). Make changes, commit git, create PR running usethis::pr_push(), following prompts browser. title PR briefly describe change. body PR contain Fixes #issue-number. user-facing changes, add bullet top NEWS.md (.e. just first header). Follow style described https://style.tidyverse.org/news.html.","code":""},{"path":"https://stephenturner.github.io/biorecap/CONTRIBUTING.html","id":"code-style","dir":"","previous_headings":"Bigger changes","what":"Code style","title":"Contributing to biorecap","text":"New code follow tidyverse style guide. can use styler package apply styles, please don’t restyle code nothing PR. use roxygen2, Markdown syntax, documentation. use testthat unit tests. Contributions test cases included easier accept.","code":""},{"path":"https://stephenturner.github.io/biorecap/CONTRIBUTING.html","id":"code-of-conduct","dir":"","previous_headings":"","what":"Code of Conduct","title":"Contributing to biorecap","text":"Please note biorecap project released Contributor Code Conduct. contributing project agree abide terms.","code":""},{"path":"https://stephenturner.github.io/biorecap/LICENSE.html","id":null,"dir":"","previous_headings":"","what":"MIT License","title":"MIT License","text":"Copyright (c) 2024 Stephen Turner Permission hereby granted, free charge, person obtaining copy software associated documentation files (“Software”), deal Software without restriction, including without limitation rights use, copy, modify, merge, publish, distribute, sublicense, /sell copies Software, permit persons Software furnished , subject following conditions: copyright notice permission notice shall included copies substantial portions Software. SOFTWARE PROVIDED “”, WITHOUT WARRANTY KIND, EXPRESS IMPLIED, INCLUDING LIMITED WARRANTIES MERCHANTABILITY, FITNESS PARTICULAR PURPOSE NONINFRINGEMENT. EVENT SHALL AUTHORS COPYRIGHT HOLDERS LIABLE CLAIM, DAMAGES LIABILITY, WHETHER ACTION CONTRACT, TORT OTHERWISE, ARISING , CONNECTION SOFTWARE USE DEALINGS SOFTWARE.","code":""},{"path":"https://stephenturner.github.io/biorecap/authors.html","id":null,"dir":"","previous_headings":"","what":"Authors","title":"Authors and Citation","text":"Stephen Turner. Author, maintainer.","code":""},{"path":"https://stephenturner.github.io/biorecap/authors.html","id":"citation","dir":"","previous_headings":"","what":"Citation","title":"Authors and Citation","text":"Turner S (2024). biorecap: Retrieve summarize bioRxiv preprints local LLM using ollama. R package version 0.1.1, https://stephenturner.github.io/biorecap/.","code":"@Manual{,   title = {biorecap: Retrieve and summarize bioRxiv preprints with a local LLM using ollama},   author = {Stephen Turner},   year = {2024},   note = {R package version 0.1.1},   url = {https://stephenturner.github.io/biorecap/}, }"},{"path":"https://stephenturner.github.io/biorecap/index.html","id":"biorecap-","dir":"","previous_headings":"","what":"Retrieve and summarize bioRxiv preprints with a local LLM using ollama","title":"Retrieve and summarize bioRxiv preprints with a local LLM using ollama","text":"Retrieve summarize bioRxiv preprints using local LLM Ollama via ollamar. Turner, S. D. (2024). biorecap: R package summarizing bioRxiv preprints local LLM. arXiv, 2408.11707. https://doi.org/10.48550/arXiv.2408.11707.","code":""},{"path":"https://stephenturner.github.io/biorecap/index.html","id":"installation","dir":"","previous_headings":"","what":"Installation","title":"Retrieve and summarize bioRxiv preprints with a local LLM using ollama","text":"Install biorecap GitHub (keep dependencies=TRUE get Suggests packages needed create HTML report):","code":"# install.packages(\"remotes\") remotes::install_github(\"stephenturner/biorecap\", dependencies=TRUE)"},{"path":[]},{"path":"https://stephenturner.github.io/biorecap/index.html","id":"quick-start","dir":"","previous_headings":"Usage","what":"Quick start","title":"Retrieve and summarize bioRxiv preprints with a local LLM using ollama","text":"First, load biorecap library. Let’s make sure Ollama running can talk R: Next can list available models: Write HTML report containing summaries recent preprints select subject areas current working directory. Example HTML report generated bioRxiv RSS feed August 6, 2024:","code":"library(biorecap) test_connection() #> Ollama local server running #> <httr2_response> #> GET http://localhost:11434/ #> Status: 200 OK #> Content-Type: text/plain #> Body: In memory (17 bytes) list_models() #> # A tibble: 3 × 4 #>   name            model           parameter_size quantization_level #>   <chr>           <chr>           <chr>          <chr>              #> 1 gemma2:latest   gemma2:latest   9.2B           Q4_0               #> 2 llama3.1:latest llama3.1:latest 8.0B           Q4_0               #> 3 llama3.1:70b    llama3.1:70b    70.6B          Q4_0 biorecap_report(output_dir=\".\",                  subject=c(\"bioinformatics\", \"genomics\", \"synthetic_biology\"),                  model=\"llama3.1\")"},{"path":"https://stephenturner.github.io/biorecap/index.html","id":"details","dir":"","previous_headings":"Usage","what":"Details","title":"Retrieve and summarize bioRxiv preprints with a local LLM using ollama","text":"get_preprints() function retrieves preprints bioRxiv’s RSS feeds. pass one subjects subject argument. add_prompt() function adds prompt preprint used prompt model. Let’s take look one prompts: giving paper’s title abstract. Summarize paper many sentences instruct. include preamble text. Just give summary. Number sentences summary: 2 Title: SeuratExtend: Streamlining Single-Cell RNA-Seq Analysis Integrated Intuitive Framework Abstract: Single-cell RNA sequencing (scRNA-seq) revolutionized study cellular heterogeneity, rapid expansion analytical tools proven blessing curse, presenting researchers significant challenges. , present SeuratExtend, comprehensive R package built upon widely adopted Seurat framework, streamlines scRNA-seq data analysis integrating essential tools databases. SeuratExtend offers user-friendly intuitive interface performing wide range analyses, including functional enrichment, trajectory inference, gene regulatory network reconstruction, denoising. package seamlessly integrates multiple databases, Gene Ontology Reactome, incorporates popular Python tools like scVelo, Palantir, SCENIC unified R interface. SeuratExtend enhances data visualization optimized plotting functions carefully curated color schemes, ensuring aesthetic appeal scientific rigor. demonstrate SeuratExtend’s performance case studies investigating tumor-associated high-endothelial venules autoinflammatory diseases, showcase novel applications pathway-Level analysis cluster annotation. SeuratExtend empowers researchers harness full potential scRNA-seq data, making complex analyses accessible wider audience. package, along comprehensive documentation tutorials, freely available GitHub, providing valuable resource single-cell genomics community. add_summary() function uses locally running LLM available Ollama summarize preprint. Let’s add summary. Notice can single pipeline. takes minutes! Let’s take look results: Let’s look one summaries. ’s summary SeuratExtend paper (abstract ): SeuratExtend R package integrates essential tools databases single-cell RNA sequencing (scRNA-seq) data analysis, streamlining process user-friendly interface. package offers various analyses, including functional enrichment gene regulatory network reconstruction, seamlessly integrates multiple databases popular Python tools. biorecap_report() function runs code RMarkdown template, writing resulting HTML CSV file results current working directory. built-subjects vector contains available bioRxiv subjects. create report subjects like (note, take time):","code":"pp <- get_preprints(subject=c(\"bioinformatics\",                                \"genomics\",                                \"synthetic_biology\")) pp #> # A tibble: 90 × 4 #>    subject        title                                           url   abstract #>    <chr>          <chr>                                           <chr> <chr>    #>  1 bioinformatics Integrity and miss grouping as support for clu… http… \"The hi… #>  2 bioinformatics Sainsc: a computational tool for segmentation-… http… \"Spatia… #>  3 bioinformatics BRACE: A novel Bayesian-based imputation appro… http… \"Bayesi… #>  4 bioinformatics Topological embedding and directional feature … http… \"Cancer… #>  5 bioinformatics SeuratExtend: Streamlining Single-Cell RNA-Seq… http… \"Single… #>  6 bioinformatics An Evolutionary Statistics Toolkit for Simplif… http… \"We pre… #>  7 bioinformatics A map of integrated cis-regulatory elements en… http… \"Cis-re… #>  8 bioinformatics MOSTPLAS: A Self-correction Multi-label Learni… http… \"Plasmi… #>  9 bioinformatics Bootstrap Evaluation of Association Matrices (… http… \"Motiva… #> 10 bioinformatics Thermodynamic modeling of Csr/Rsm- RNA interac… http… \"Backgr… #> # ℹ 80 more rows pp <- pp |> add_prompt() pp #> # A tibble: 90 × 5 #>    subject        title                                    url   abstract prompt #>    <chr>          <chr>                                    <chr> <chr>    <chr>  #>  1 bioinformatics Integrity and miss grouping as support … http… \"The hi… \"I am… #>  2 bioinformatics Sainsc: a computational tool for segmen… http… \"Spatia… \"I am… #>  3 bioinformatics BRACE: A novel Bayesian-based imputatio… http… \"Bayesi… \"I am… #>  4 bioinformatics Topological embedding and directional f… http… \"Cancer… \"I am… #>  5 bioinformatics SeuratExtend: Streamlining Single-Cell … http… \"Single… \"I am… #>  6 bioinformatics An Evolutionary Statistics Toolkit for … http… \"We pre… \"I am… #>  7 bioinformatics A map of integrated cis-regulatory elem… http… \"Cis-re… \"I am… #>  8 bioinformatics MOSTPLAS: A Self-correction Multi-label… http… \"Plasmi… \"I am… #>  9 bioinformatics Bootstrap Evaluation of Association Mat… http… \"Motiva… \"I am… #> 10 bioinformatics Thermodynamic modeling of Csr/Rsm- RNA … http… \"Backgr… \"I am… #> # ℹ 80 more rows pp <-    get_preprints(subject=c(\"bioinformatics\", \"genomics\", \"synthetic_biology\")) |>    add_prompt() |>    add_summary(model=\"llama3.1\") pp #> # A tibble: 90 × 6 #>    subject        title                            url   abstract prompt summary #>    <chr>          <chr>                            <chr> <chr>    <chr>  <chr>   #>  1 bioinformatics Integrity and miss grouping as … http… \"The hi… \"I am… \"The p… #>  2 bioinformatics Sainsc: a computational tool fo… http… \"Spatia… \"I am… \"Sains… #>  3 bioinformatics BRACE: A novel Bayesian-based i… http… \"Bayesi… \"I am… \"Alter… #>  4 bioinformatics Topological embedding and direc… http… \"Cancer… \"I am… \"Resea… #>  5 bioinformatics SeuratExtend: Streamlining Sing… http… \"Single… \"I am… \"Seura… #>  6 bioinformatics An Evolutionary Statistics Tool… http… \"We pre… \"I am… \"The \\… #>  7 bioinformatics A map of integrated cis-regulat… http… \"Cis-re… \"I am… \"The a… #>  8 bioinformatics MOSTPLAS: A Self-correction Mul… http… \"Plasmi… \"I am… \"Plasm… #>  9 bioinformatics Bootstrap Evaluation of Associa… http… \"Motiva… \"I am… \"The a… #> 10 bioinformatics Thermodynamic modeling of Csr/R… http… \"Backgr… \"I am… \"Resea… #> # ℹ 80 more rows biorecap_report(output_dir=\".\",                  subject=c(\"bioinformatics\", \"genomics\", \"synthetic_biology\"),                  model=\"llama3.1\") subjects #>  [1] \"all\"                                    #>  [2] \"animal_behavior_and_cognition\"          #>  [3] \"biochemistry\"                           #>  [4] \"bioengineering\"                         #>  [5] \"bioinformatics\"                         #>  [6] \"biophysics\"                             #>  [7] \"cancer_biology\"                         #>  [8] \"cell_biology\"                           #>  [9] \"clinical_trials\"                        #> [10] \"developmental_biology\"                  #> [11] \"ecology\"                                #> [12] \"epidemiology\"                           #> [13] \"evolutionary_biology\"                   #> [14] \"genetics\"                               #> [15] \"genomics\"                               #> [16] \"immunology\"                             #> [17] \"microbiology\"                           #> [18] \"molecular_biology\"                      #> [19] \"neuroscience\"                           #> [20] \"paleontology\"                           #> [21] \"pathology\"                              #> [22] \"pharmacology_and_toxicology\"            #> [23] \"plant_biology\"                          #> [24] \"scientific_communication_and_education\" #> [25] \"synthetic_biology\"                      #> [26] \"systems_biology\"                        #> [27] \"zoology\" biorecap_report(output_dir=\".\",                  subject=subjects,                  model=\"llama3.1\")"},{"path":"https://stephenturner.github.io/biorecap/reference/add_prompt.html","id":null,"dir":"Reference","previous_headings":"","what":"Add prompt to a data frame of preprints — add_prompt","title":"Add prompt to a data frame of preprints — add_prompt","text":"Add prompt data frame preprints","code":""},{"path":"https://stephenturner.github.io/biorecap/reference/add_prompt.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Add prompt to a data frame of preprints — add_prompt","text":"","code":"add_prompt(preprints, ...)"},{"path":"https://stephenturner.github.io/biorecap/reference/add_prompt.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Add prompt to a data frame of preprints — add_prompt","text":"preprints Result get_preprints(). ... Additional arguments build_prompt_preprint().","code":""},{"path":"https://stephenturner.github.io/biorecap/reference/add_prompt.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Add prompt to a data frame of preprints — add_prompt","text":"data frame bioRxiv preprints prompt added.","code":""},{"path":[]},{"path":"https://stephenturner.github.io/biorecap/reference/add_prompt.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Add prompt to a data frame of preprints — add_prompt","text":"","code":"preprints <- get_preprints(subject=c(\"bioinformatics\", \"genomics\")) preprints <- add_prompt(preprints) preprints #> # A tibble: 60 × 5 #>    subject        title                                    url   abstract prompt #>    <chr>          <chr>                                    <chr> <chr>    <chr>  #>  1 bioinformatics \"PHIStruct: Improving phage-host intera… http… Recent … \"I am… #>  2 bioinformatics \"BGC Atlas: A Web Resource for Explorin… http… Seconda… \"I am… #>  3 bioinformatics \"Enrichment analysis for spatial and si… http… Imaging… \"I am… #>  4 bioinformatics \"SpatialLeiden - Spatially-aware Leiden… http… Cluster… \"I am… #>  5 bioinformatics \"Protein sequence classification using … http… Protein… \"I am… #>  6 bioinformatics \"A multi-omics approach to identify del… http… Crops l… \"I am… #>  7 bioinformatics \"Automatic crystal identification for c… http… Crystal… \"I am… #>  8 bioinformatics \"Solving the \\\"Blind men and the elepha… http… Biologi… \"I am… #>  9 bioinformatics \"Evolutionary mismatch between nuclear … http… Serrano… \"I am… #> 10 bioinformatics \"Predicting small-molecule inhibition o… http… Protein… \"I am… #> # ℹ 50 more rows"},{"path":"https://stephenturner.github.io/biorecap/reference/add_prompt_subject.html","id":null,"dir":"Reference","previous_headings":"","what":"Add prompts for an entire subject — add_prompt_subject","title":"Add prompts for an entire subject — add_prompt_subject","text":"Add prompts entire subject","code":""},{"path":"https://stephenturner.github.io/biorecap/reference/add_prompt_subject.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Add prompts for an entire subject — add_prompt_subject","text":"","code":"add_prompt_subject(preprints, ...)"},{"path":"https://stephenturner.github.io/biorecap/reference/add_prompt_subject.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Add prompts for an entire subject — add_prompt_subject","text":"preprints Output get_preprints() followed add_prompt() followed add_summary(). ... Additional arguments build_prompt_subject().","code":""},{"path":"https://stephenturner.github.io/biorecap/reference/add_prompt_subject.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Add prompts for an entire subject — add_prompt_subject","text":"tibble subject prompt column.","code":""},{"path":"https://stephenturner.github.io/biorecap/reference/add_prompt_subject.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Add prompts for an entire subject — add_prompt_subject","text":"","code":"subjects <-   example_preprints |>   dplyr::group_by(subject) |>   add_prompt_subject() #> Warning: Expecting a tibble of class 'preprints_prompt' returned from get_preprints() |> add_prompt(). subjects #> # A tibble: 3 × 2 #>   subject           prompt                                                       #>   <chr>             <chr>                                                        #> 1 bioinformatics    \"I am giving you information about preprints published in b… #> 2 genomics          \"I am giving you information about preprints published in b… #> 3 synthetic_biology \"I am giving you information about preprints published in b…"},{"path":"https://stephenturner.github.io/biorecap/reference/add_summary.html","id":null,"dir":"Reference","previous_headings":"","what":"Generate a summary from a data frame of prompts — add_summary","title":"Generate a summary from a data frame of prompts — add_summary","text":"Generate summary data frame prompts","code":""},{"path":"https://stephenturner.github.io/biorecap/reference/add_summary.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Generate a summary from a data frame of prompts — add_summary","text":"","code":"add_summary(preprints, model = \"llama3.1\")"},{"path":"https://stephenturner.github.io/biorecap/reference/add_summary.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Generate a summary from a data frame of prompts — add_summary","text":"preprints Output get_preprints() followed add_prompt(). model model available Ollama (run ollamar::list_models()) see available.","code":""},{"path":"https://stephenturner.github.io/biorecap/reference/add_summary.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Generate a summary from a data frame of prompts — add_summary","text":"tibble, response column added.","code":""},{"path":"https://stephenturner.github.io/biorecap/reference/add_summary.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Generate a summary from a data frame of prompts — add_summary","text":"","code":"if (FALSE) { # \\dontrun{ # Individual papers preprints <-   get_preprints(c(\"genomics\", \"bioinformatics\")) |>   add_prompt() |>   add_summary() preprints } # }"},{"path":"https://stephenturner.github.io/biorecap/reference/biorecap-package.html","id":null,"dir":"Reference","previous_headings":"","what":"biorecap: Retrieve and summarize bioRxiv preprints with a local LLM using ollama — biorecap-package","title":"biorecap: Retrieve and summarize bioRxiv preprints with a local LLM using ollama — biorecap-package","text":"Retrieve summarize bioRxiv preprints local LLM using ollama.","code":""},{"path":[]},{"path":"https://stephenturner.github.io/biorecap/reference/biorecap-package.html","id":"author","dir":"Reference","previous_headings":"","what":"Author","title":"biorecap: Retrieve and summarize bioRxiv preprints with a local LLM using ollama — biorecap-package","text":"Maintainer: Stephen Turner vustephen@gmail.com (ORCID)","code":""},{"path":"https://stephenturner.github.io/biorecap/reference/biorecap_report.html","id":null,"dir":"Reference","previous_headings":"","what":"Create a report from bioRxiv preprints — biorecap_report","title":"Create a report from bioRxiv preprints — biorecap_report","text":"Create report bioRxiv preprints","code":""},{"path":"https://stephenturner.github.io/biorecap/reference/biorecap_report.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Create a report from bioRxiv preprints — biorecap_report","text":"","code":"biorecap_report(   output_dir = \".\",   subject = NULL,   nsentences = 2L,   model = \"llama3.1\",   use_example_preprints = FALSE,   ... )"},{"path":"https://stephenturner.github.io/biorecap/reference/biorecap_report.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Create a report from bioRxiv preprints — biorecap_report","text":"output_dir Directory save report. subject Character vector subjects include report. nsentences Number sentences summarize paper . model model use generating summaries. See ollamar::list_models(). use_example_preprints Use example preprints data included package instead fetching new data bioRxiv. diagnostic/testing purposes . ... arguments passed rmarkdown::render().","code":""},{"path":"https://stephenturner.github.io/biorecap/reference/biorecap_report.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Create a report from bioRxiv preprints — biorecap_report","text":"Nothing; called side effects produce report.","code":""},{"path":"https://stephenturner.github.io/biorecap/reference/biorecap_report.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Create a report from bioRxiv preprints — biorecap_report","text":"","code":"if (FALSE) { # \\dontrun{ output_dir <- tempdir() biorecap_report(use_example_preprints=TRUE, output_dir=output_dir) biorecap_report(subject=c(\"bioinformatics\", \"genomics\", \"synthetic_biology\"),                 output_dir=output_dir) } # }"},{"path":"https://stephenturner.github.io/biorecap/reference/build_prompt_preprint.html","id":null,"dir":"Reference","previous_headings":"","what":"Construct a prompt to summarize a paper — build_prompt_preprint","title":"Construct a prompt to summarize a paper — build_prompt_preprint","text":"Construct prompt summarize paper","code":""},{"path":"https://stephenturner.github.io/biorecap/reference/build_prompt_preprint.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Construct a prompt to summarize a paper — build_prompt_preprint","text":"","code":"build_prompt_preprint(   title,   abstract,   nsentences = 2L,   instructions = c(\"I am giving you a paper's title and abstract.\",     \"Summarize the paper in as many sentences as I instruct.\",     \"Do not include any preamble text to the summary\",     \"just give me the summary with no preface or intro sentence.\") )"},{"path":"https://stephenturner.github.io/biorecap/reference/build_prompt_preprint.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Construct a prompt to summarize a paper — build_prompt_preprint","text":"title title paper. abstract abstract paper. nsentences number sentences summarize paper . instructions Instructions prompt. can character vector gets collapsed single string.","code":""},{"path":"https://stephenturner.github.io/biorecap/reference/build_prompt_preprint.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Construct a prompt to summarize a paper — build_prompt_preprint","text":"string containing prompt.","code":""},{"path":"https://stephenturner.github.io/biorecap/reference/build_prompt_preprint.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Construct a prompt to summarize a paper — build_prompt_preprint","text":"","code":"build_prompt_preprint(title=\"A great paper\", abstract=\"This is the abstract.\") #> [1] \"I am giving you a paper's title and abstract. Summarize the paper in as many sentences as I instruct. Do not include any preamble text to the summary just give me the summary with no preface or intro sentence.\\nNumber of sentences in summary: 2\\nTitle: A great paper\\nAbstract: This is the abstract.\""},{"path":"https://stephenturner.github.io/biorecap/reference/build_prompt_subject.html","id":null,"dir":"Reference","previous_headings":"","what":"Construct a prompt to summarize a set of papers from a subject — build_prompt_subject","title":"Construct a prompt to summarize a set of papers from a subject — build_prompt_subject","text":"Construct prompt summarize set papers subject","code":""},{"path":"https://stephenturner.github.io/biorecap/reference/build_prompt_subject.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Construct a prompt to summarize a set of papers from a subject — build_prompt_subject","text":"","code":"build_prompt_subject(   subject,   title,   summary,   nsentences = 5L,   instructions =     c(\"I am giving you information about preprints published in bioRxiv recently.\",     \"I'll give you the subject, preprint titles, and short summary of each paper.\",     \"Please provide a general summary new advances in this subject/field in general.\",     \"Provide this summary of the field in as many sentences as I instruct.\",     \"Do not include any preamble text to the summary\",     \"just give me the summary with no preface or intro sentence.\") )"},{"path":"https://stephenturner.github.io/biorecap/reference/build_prompt_subject.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Construct a prompt to summarize a set of papers from a subject — build_prompt_subject","text":"subject name subject. title character vector titles subject summary character vector summaries paper provided get_preprints() followed add_prompt() followed add_summary(). nsentences number sentences summarize subject . instructions Instructions prompt. can character vector gets collapsed single string.","code":""},{"path":"https://stephenturner.github.io/biorecap/reference/build_prompt_subject.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Construct a prompt to summarize a set of papers from a subject — build_prompt_subject","text":"string containing prompt.","code":""},{"path":"https://stephenturner.github.io/biorecap/reference/build_prompt_subject.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Construct a prompt to summarize a set of papers from a subject — build_prompt_subject","text":"","code":"title <- example_preprints |> dplyr::filter(subject==\"bioinformatics\") |> dplyr::pull(title) summary <- example_preprints |> dplyr::filter(subject==\"bioinformatics\") |> dplyr::pull(summary) build_prompt_subject(subject=\"bioinformatics\", title=title, summary=summary) #> [1] \"I am giving you information about preprints published in bioRxiv recently. I'll give you the subject, preprint titles, and short summary of each paper. Please provide a general summary new advances in this subject/field in general. Provide this summary of the field in as many sentences as I instruct. Do not include any preamble text to the summary just give me the summary with no preface or intro sentence.\\n\\nSubject: bioinformatics\\nNumber of sentences in summary: 5\\n\\nHere are the titles and summaries:\\n\\nTitle: Integrity and miss grouping as support for clusters in agglomerative hierarchical methods: the R-package octopucs\\nSummary: The proposed method assesses cluster support throughout hierarchical analyses by compiling a consensus topology and using ecological concepts of reciprocal complementarities to define cluster integrity and contamination. This approach allows for building support for groups even when there is partial membership match after resampling, and was implemented in the R package octopucs, which showed robust detection of changes in group memberships compared to other methods.\\n\\nTitle: Sainsc: a computational tool for segmentation-free analysis of in-situ capture\\nSummary: Sainsc is a computational tool that enables segmentation-free analysis of spatially resolved transcriptomics data, allowing for accurate cell-type assignment at the subcellular level without requiring manual cell border delineation. The tool provides efficient processing of high-resolution spatial data and can generate maps of cell types with corresponding confidence scores, making it a valuable resource for biomedical researchers working with complex tissue samples.\\n\\nTitle: BRACE: A novel Bayesian-based imputation approach for dimension reduction analysis of alternative splicing at single-cell resolution\\nSummary: Alternative splicing represents an additional layer of complexity in gene expression profiles, but analyzing it at single-cell resolution is challenging due to missing data. This paper introduces BRACE, a Bayesian-based imputation approach that improves upon existing methods and enables dimension reduction analysis of alternative splicing events at single-cell resolution.\\n\\nTitle: Topological embedding and directional feature importance in ensemble classifiers for multi-class classification\\nSummary: Researchers developed a new metric called class-based direction feature importance (CLIFI) to provide interpretable insights into the decision-making process of ensemble classifiers for multi-class classification problems, specifically in the context of cancer biomarker identification. The CLIFI metric was incorporated into four algorithms and applied to The Cancer Genome Atlas proteomics data, resulting in high F1-scores and allowing for the visualization of model decision-making functions and the identification of heterogeneity in several proteins across different cancer types.\\n\\nTitle: SeuratExtend: Streamlining Single-Cell RNA-Seq Analysis Through an Integrated and Intuitive Framework\\nSummary: SeuratExtend is an R package that integrates essential tools and databases for single-cell RNA sequencing (scRNA-seq) data analysis, streamlining the process through a user-friendly interface. The package offers various analyses, including functional enrichment and gene regulatory network reconstruction, and seamlessly integrates multiple databases and popular Python tools.\\n\\nTitle: An Evolutionary Statistics Toolkit for Simplified Sequence Analysis on Web with Client-Side Processing\\nSummary: The \\\"Evolutionary Statistics Toolkit\\\" is a web-based platform that integrates multiple evolutionary statistics tools for simplified sequence analysis, including Tajima's D calculator and Shannon's Entropy. The open-source toolkit facilitates streamlined workflows for researchers in evolutionary biology and genomics, and also serves as an educational interactive website for beginners in evolutionary statistics.\\n\\nTitle: A map of integrated cis-regulatory elements enhances gene regulatory analysis in maize\\nSummary: The authors integrated various methods for profiling cis-regulatory elements (CREs) in maize, resulting in maps of integrated CREs that show increased completeness and precision. These maps were used to infer drought-specific gene regulatory networks and identify candidate regulators of maize drought response, as well as to study the potential role of transposable elements in regulating gene expression.\\n\\nTitle: MOSTPLAS: A Self-correction Multi-label Learning Model for Plasmid Host Range Prediction\\nSummary: Plasmid host range prediction tools are essential for understanding how plasmids promote bacterial evolution, but existing learning-based tools struggle due to limited well-annotated training samples. The proposed model, MOSTPLAS, addresses this issue with a self-correction multi-label learning approach that uses pseudo label learning and asymmetric loss to facilitate training with incomplete labels.\\n\\nTitle: Bootstrap Evaluation of Association Matrices (BEAM) for Integrating Multiple Omics Profiles with Multiple Outcomes\\nSummary: The authors propose Bootstrap Evaluation of Association Matrices (BEAM), a new statistical method that integrates multiple omics profiles with multiple clinical endpoints to identify significant associations between them. BEAM outperformed other integrated analysis methods in simulations and identified biologically relevant genes in a pediatric leukemia application that were missed by univariate screens and other methods.\\n\\nTitle: Thermodynamic modeling of Csr/Rsm- RNA interactions capture novel, direct binding interactions across the Pseudomonas aeruginosa transcriptome\\nSummary: Researchers developed a thermodynamic model to predict interactions between the post-transcriptional regulator RsmA and mRNAs in Pseudomonas aeruginosa, predicting 1043 direct binding interactions, including 457 novel targets. The predictions were validated through in vitro binding assays and in vivo translational reporters, revealing direct regulation of genes involved in quorum sensing and the Type IV Secretion system, expanding the known pool of RsmA target genes.\\n\\nTitle: Assessing the ability of ChatGPT to extract natural product bioactivity and biosynthesis data from publications\\nSummary: ChatGPT was tested on its ability to extract data from publications on natural product bioactivity and biosynthesis, which is crucial for training models that predict natural product activity from biosynthetic gene clusters. The results showed that ChatGPT performed well in identifying papers describing natural product discovery and extracting information about the product's bioactivity, but struggled with extracting accession numbers for the biosynthetic gene cluster or producer's genome.\\n\\nTitle: Genome-Wide Analysis of TCP Family Genes and Their Constitutive Expression Pattern Analysis in the Melon (Cucumis melo)\\nSummary: This study identified and characterized 29 putative TCP genes in melon, classifying them into two classes and analyzing their chromosomal location, gene structure, and expression patterns. The results suggest that some CmTCP genes may have similar functions to their homologs in other plant species, while others may have undergone functional diversification, providing a resource for future investigations into their roles in melon development.\\n\\nTitle: Single-cell differential expression analysis between conditions within nested settings\\nSummary: Researchers compared various methods for differential expression analysis of single-cell transcriptomics data and found that methods designed specifically for single-cell data do not offer performance advantages over conventional pseudobulk methods like DESeq2 when applied to individual datasets. However, permutation-based methods excel in performance for atlas-level analysis, but require significantly longer run times, making DREAM a compromise between quality and runtime.\\n\\nTitle: CoMPHI: A Novel Composite Machine Learning Approach Utilizing Multiple FeatureRepresentation to Predict Hosts of Bacteriophages\\nSummary: Here is a 2-sentence summary of the paper:  This study introduces CoMPHI, a novel composite machine learning approach that combines multiple feature representations to predict hosts of bacteriophages, with potential applications in phage therapy for treating bacterial infections. The model achieves high prediction accuracy, with an Area Under the ROC Curve (AUC) of up to 96.7% and accuracy of up to 95.1%, outperforming existing methods due to its inclusion of alignment scores and use of both nucleotide and protein sequences from phages and hosts.\\n\\nTitle: FourierMIL: Fourier filtering-based multiple instance learning for whole slide image analysis\\nSummary: The paper presents FourierMIL, a multiple instance learning framework that uses the discrete Fourier transform to analyze whole-slide images (WSIs) in digital pathology. The method captures both global and local dependencies within WSIs and outperforms existing state-of-the-art methods in tumor classification tasks on gigapixel-resolution WSIs.\\n\\nTitle: Multiple Protein Structure Alignment at Scale with FoldMason\\nSummary: Here is a 2-sentence summary of the paper:  FoldMason is a new method for multiple protein structure alignment that can handle hundreds of thousands of structures at scale with high speed and accuracy. It leverages the structural alphabet from Foldseek to compute confidence scores, provide interactive visualizations, and support large-scale protein structure analysis and phylogenetic studies.\\n\\nTitle: Deciphering octoploid strawberry evolution with serial LTR similarity matrices for subgenome partition\\nSummary: A novel approach was developed to assign polyploid genome assemblies to subgenomes using long terminal repeat retrotransposons (LTR-RTs) and the Serial Similarity Matrix (SSM) method, which is particularly useful for genomes without known diploid ancestors. The SSM approach was validated using well-studied allopolyploidy genomes and then applied to the octoploid strawberry genome, revealing three allopolyploidization events in its evolutionary history.\\n\\nTitle: IDENTIFICATION OF IMMUNE RESPONSE AND RNA NETWORK OF RHEUMATOID ARTHRITIS AND MOLECULAR DOCKING OF CELASTRUS PANICULATUS AS POTENTIAL THERAPEUTIC AGENT\\nSummary: This study used bioinformatics analysis to identify immune responses, microRNA-hub genes networks, and potential therapeutic agents for rheumatoid arthritis (RA), a complex autoimmune disease with an unknown pathogenesis. The researchers found several hub genes and miRNAs associated with RA, and identified oleic acid and zeylasterone as potential novel drug candidates against the disease through molecular docking analysis of Celastrus paniculatus phytochemical compounds.\\n\\nTitle: Imputing abundance of over 2500 surface proteins from single-cell transcriptomes with context-agnostic zero-shot deep ensembles\\nSummary: SPIDER is a deep ensemble model that predicts the abundance of over 2500 surface proteins from single-cell transcriptomes with improved generalization across diverse contexts such as tissues or disease states. The model outperforms other state-of-the-art methods and has various applications including cell type annotation, biomarker/target identification, and cell-cell interaction analysis in cancer research.\\n\\nTitle: Modelling Protein-Glycan Interactions with HADDOCK\\nSummary: Glycans play important roles in living organisms by interacting with proteins for information transfer and signalling purposes, making it essential to determine the three-dimensional structures of protein-glycan complexes. The molecular docking approach HADDOCK was used to predict protein-glycan complexes with a top 5 success rate of 70% for bound datasets and 40% for unbound datasets using a benchmark of 89 complexes.\\n\\nTitle: Machine Learning Reveals Key Glycoprotein Mutations and Rapidly Assigns Lassa Virus Lineages\\nSummary: Machine learning and phylogenetic analysis of Lassa virus glycoprotein sequences revealed key mutations and genetic differences between Nigerian lineages and those from other West African countries. The study identified specific amino acid positions that are highly variable among the lineages, which may explain structural and phenotypical differences, and developed a machine learning-based tool for rapid lineage classification.\\n\\nTitle: RESP2: An uncertainty aware multi-target multi-property optimization AI pipeline for antibody discovery\\nSummary: The RESP2 pipeline is an AI-powered tool designed to optimize the discovery of therapeutic antibodies against infectious disease pathogens, taking into account multiple targets and properties such as specificity, low immunogenicity, and high affinity. The pipeline uses a suite of methods to estimate uncertainty in predictions and has been successfully applied to discover a highly human antibody with broad binding to variants of the COVID-19 spike protein receptor binding domain.\\n\\nTitle: Extending the capabilities of deconvolution to provide cell type specific pathway analysis of bulk RNA-seq data for idiopathic pulmonary fibrosis\\nSummary: A deconvolution method was applied to bulk RNA-seq data from idiopathic pulmonary fibrosis (IPF) samples to correct for changes in cell type proportions and provide cell-type specific pathway analysis. The results showed significant increases in fibroblasts and myofibroblasts, decreases in vascular endothelial capillary cells, and IPF-related changes in extracellular matrix organization and TGF-{beta} regulation, as well as the involvement of interferon signaling in ATII cells.\\n\\nTitle: A survey of ADP-ribosyltransferase families in the pathogenic Legionella\\nSummary: A comprehensive bioinformatic survey of 41 Legionella species identified 63 proteins with significant sequence or structural similarity to known ADP-ribosyltransferases (ARTs), organized into 39 ART-like families, including 26 novel families. The study found that most members of the novel ART families are predicted effectors, presenting promising targets for understanding Legionella pathogenicity and developing therapeutic strategies.\\n\\nTitle: A replicable and modular benchmark for long-read transcript quantification methods\\nSummary: Researchers have developed a replicable benchmark for evaluating long-read transcript quantification methods using synthetic RNA-seq datasets, which can be easily extended to include new tools or data sets. The study reveals discrepancies with previously published results and highlights the importance of high-quality simulated data in assessing the robustness of certain approaches.\\n\\nTitle: Logan: Planetary-Scale Genome Assembly Surveys Life's Diversity\\nSummary: The NCBI Sequence Read Archive contains over 50 petabases of DNA sequencing data across 27 million datasets, but its size makes it impractical to search for specific genetic sequences within a reasonable time frame. To address this issue, the authors used cloud computing to perform genome assembly on each dataset and created the Logan assemblage, which is now freely available and enables faster querying of the data, with some queries completing in as little as 11 hours.\\n\\nTitle: Cell-type specific epigenetic clocks to quantify biological age at cell-type resolution\\nSummary: Epigenetic clocks have been developed to estimate biological age, but most are based on heterogeneous bulk tissues and reflect both changes in cell-type composition and individual cell aging. This study created neuron- and hepatocyte-specific DNA methylation clocks that provide improved estimates of chronological age and detect accelerated biological aging in Alzheimer's disease and liver pathology.\\n\\nTitle: Genomic and transcriptomic analyses of Heteropoda venatoria reveal the expansion of P450 family for starvation resistance in spider\\nSummary: The genome of Heteropoda venatoria was sequenced and comparative genomic analysis revealed significant expansions in gene families related to lipid metabolism, including cytochrome P450 and steroid hormone biosynthesis genes. The study found that during starvation, H. venatoria undergoes a series of physiological changes, including the activation of fatty acid metabolism and protein degradation pathways, and the expression of expanded P450 gene families, which help the spider maintain a low-energy metabolic state and endure longer periods of starvation.\\n\\nTitle: Annotation Vocabulary (Might Be) All You Need\\nSummary: The authors introduce the \\\"Annotation Vocabulary\\\", a language of protein properties defined by structured ontologies that can be used to train transformer models without reference to amino acid sequences. They demonstrate the effectiveness of this approach in various experiments, achieving state-of-the-art results on several common datasets with competitive performance on others, and generating high-quality de novo protein sequences from annotation-only prompts.\\n\\nTitle: AncFlow: An Ancestral Sequence Reconstruction Approach for Determining Novel Protein Structural\\nSummary: Here is the summary in 2 sentences:  AncFlow is an automated software pipeline that integrates phylogenetic analysis, subfamily identification, and ancestral sequence reconstruction (ASR) to generate ancestral protein sequences for structural prediction using state-of-the-art tools like AlphaFold. The pipeline was validated on two well-characterized protein families, providing insights into the evolutionary mechanisms underpinning functional diversification within these families and demonstrating its potential to guide protein engineering efforts.\""},{"path":"https://stephenturner.github.io/biorecap/reference/example_preprints.html","id":null,"dir":"Reference","previous_headings":"","what":"Example preprints with summaries — example_preprints","title":"Example preprints with summaries — example_preprints","text":"Example preprints summaries August 6, 2024.","code":""},{"path":"https://stephenturner.github.io/biorecap/reference/example_preprints.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Example preprints with summaries — example_preprints","text":"","code":"example_preprints"},{"path":"https://stephenturner.github.io/biorecap/reference/example_preprints.html","id":"format","dir":"Reference","previous_headings":"","what":"Format","title":"Example preprints with summaries — example_preprints","text":"tibble returned get_preprints() followed add_prompt() followed add_summary().","code":""},{"path":"https://stephenturner.github.io/biorecap/reference/example_preprints.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Example preprints with summaries — example_preprints","text":"","code":"example_preprints #> # A tibble: 90 × 6 #>    subject        title                            url   abstract prompt summary #>    <chr>          <chr>                            <chr> <chr>    <chr>  <chr>   #>  1 bioinformatics Integrity and miss grouping as … http… \"The hi… \"I am… \"The p… #>  2 bioinformatics Sainsc: a computational tool fo… http… \"Spatia… \"I am… \"Sains… #>  3 bioinformatics BRACE: A novel Bayesian-based i… http… \"Bayesi… \"I am… \"Alter… #>  4 bioinformatics Topological embedding and direc… http… \"Cancer… \"I am… \"Resea… #>  5 bioinformatics SeuratExtend: Streamlining Sing… http… \"Single… \"I am… \"Seura… #>  6 bioinformatics An Evolutionary Statistics Tool… http… \"We pre… \"I am… \"The \\… #>  7 bioinformatics A map of integrated cis-regulat… http… \"Cis-re… \"I am… \"The a… #>  8 bioinformatics MOSTPLAS: A Self-correction Mul… http… \"Plasmi… \"I am… \"Plasm… #>  9 bioinformatics Bootstrap Evaluation of Associa… http… \"Motiva… \"I am… \"The a… #> 10 bioinformatics Thermodynamic modeling of Csr/R… http… \"Backgr… \"I am… \"Resea… #> # ℹ 80 more rows"},{"path":"https://stephenturner.github.io/biorecap/reference/get_preprints.html","id":null,"dir":"Reference","previous_headings":"","what":"Get bioRxiv preprints — get_preprints","title":"Get bioRxiv preprints — get_preprints","text":"Get bioRxiv preprints","code":""},{"path":"https://stephenturner.github.io/biorecap/reference/get_preprints.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Get bioRxiv preprints — get_preprints","text":"","code":"get_preprints(   subject = \"all\",   baseurl = \"https://connect.biorxiv.org/biorxiv_xml.php?subject=\",   clean = TRUE )"},{"path":"https://stephenturner.github.io/biorecap/reference/get_preprints.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Get bioRxiv preprints — get_preprints","text":"subject character vector valid biorxiv subjects. See subjects. baseurl base URL biorxiv RSS feed. Default https://connect.biorxiv.org/biorxiv_xml.php?subject=. change unless know . clean Logical; try strip graphical abstract information? TRUE, strips away text O_FIG C_FIG, words graphical abstract abstract text RSS feed.","code":""},{"path":"https://stephenturner.github.io/biorecap/reference/get_preprints.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Get bioRxiv preprints — get_preprints","text":"data frame bioRxiv preprints.","code":""},{"path":"https://stephenturner.github.io/biorecap/reference/get_preprints.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Get bioRxiv preprints — get_preprints","text":"","code":"preprints <- get_preprints(subject=c(\"bioinformatics\", \"genomics\")) preprints #> # A tibble: 60 × 4 #>    subject        title                                           url   abstract #>    <chr>          <chr>                                           <chr> <chr>    #>  1 bioinformatics \"PHIStruct: Improving phage-host interaction p… http… Recent … #>  2 bioinformatics \"BGC Atlas: A Web Resource for Exploring the G… http… Seconda… #>  3 bioinformatics \"Enrichment analysis for spatial and single-ce… http… Imaging… #>  4 bioinformatics \"SpatialLeiden - Spatially-aware Leiden cluste… http… Cluster… #>  5 bioinformatics \"Protein sequence classification using natural… http… Protein… #>  6 bioinformatics \"A multi-omics approach to identify deleteriou… http… Crops l… #>  7 bioinformatics \"Automatic crystal identification for crystall… http… Crystal… #>  8 bioinformatics \"Solving the \\\"Blind men and the elephant prob… http… Biologi… #>  9 bioinformatics \"Evolutionary mismatch between nuclear and mit… http… Serrano… #> 10 bioinformatics \"Predicting small-molecule inhibition of prote… http… Protein… #> # ℹ 50 more rows"},{"path":"https://stephenturner.github.io/biorecap/reference/reexports.html","id":null,"dir":"Reference","previous_headings":"","what":"Objects exported from other packages — reexports","title":"Objects exported from other packages — reexports","text":"objects imported packages. Follow links see documentation. ollamar list_models, test_connection","code":""},{"path":"https://stephenturner.github.io/biorecap/reference/subjects.html","id":null,"dir":"Reference","previous_headings":"","what":"bioRxiv subjects — subjects","title":"bioRxiv subjects — subjects","text":"Names subjects RSS feeds biorXiv","code":""},{"path":"https://stephenturner.github.io/biorecap/reference/subjects.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"bioRxiv subjects — subjects","text":"","code":"subjects"},{"path":"https://stephenturner.github.io/biorecap/reference/subjects.html","id":"format","dir":"Reference","previous_headings":"","what":"Format","title":"bioRxiv subjects — subjects","text":"character vector","code":""},{"path":"https://stephenturner.github.io/biorecap/reference/subjects.html","id":"references","dir":"Reference","previous_headings":"","what":"References","title":"bioRxiv subjects — subjects","text":"https://www.biorxiv.org/alertsrss","code":""},{"path":"https://stephenturner.github.io/biorecap/reference/subjects.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"bioRxiv subjects — subjects","text":"","code":"subjects #>  [1] \"all\"                                    #>  [2] \"animal_behavior_and_cognition\"          #>  [3] \"biochemistry\"                           #>  [4] \"bioengineering\"                         #>  [5] \"bioinformatics\"                         #>  [6] \"biophysics\"                             #>  [7] \"cancer_biology\"                         #>  [8] \"cell_biology\"                           #>  [9] \"clinical_trials\"                        #> [10] \"developmental_biology\"                  #> [11] \"ecology\"                                #> [12] \"epidemiology\"                           #> [13] \"evolutionary_biology\"                   #> [14] \"genetics\"                               #> [15] \"genomics\"                               #> [16] \"immunology\"                             #> [17] \"microbiology\"                           #> [18] \"molecular_biology\"                      #> [19] \"neuroscience\"                           #> [20] \"paleontology\"                           #> [21] \"pathology\"                              #> [22] \"pharmacology_and_toxicology\"            #> [23] \"plant_biology\"                          #> [24] \"scientific_communication_and_education\" #> [25] \"synthetic_biology\"                      #> [26] \"systems_biology\"                        #> [27] \"zoology\""},{"path":"https://stephenturner.github.io/biorecap/reference/tt_preprints.html","id":null,"dir":"Reference","previous_headings":"","what":"Create a markdown table from prepreprint summaries — tt_preprints","title":"Create a markdown table from prepreprint summaries — tt_preprints","text":"Create markdown table prepreprint summaries","code":""},{"path":"https://stephenturner.github.io/biorecap/reference/tt_preprints.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Create a markdown table from prepreprint summaries — tt_preprints","text":"","code":"tt_preprints(preprints, cols = c(\"title\", \"summary\"), width = c(1, 3))"},{"path":"https://stephenturner.github.io/biorecap/reference/tt_preprints.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Create a markdown table from prepreprint summaries — tt_preprints","text":"preprints Output get_preprints() followed add_prompt() followed add_summary(). cols Columns display resulting markdown table. width Vector relative widths equal length(cols).","code":""},{"path":"https://stephenturner.github.io/biorecap/reference/tt_preprints.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Create a markdown table from prepreprint summaries — tt_preprints","text":"tinytable table.","code":""},{"path":"https://stephenturner.github.io/biorecap/reference/tt_preprints.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Create a markdown table from prepreprint summaries — tt_preprints","text":"","code":"# Use built-in example data example_preprints #> # A tibble: 90 × 6 #>    subject        title                            url   abstract prompt summary #>    <chr>          <chr>                            <chr> <chr>    <chr>  <chr>   #>  1 bioinformatics Integrity and miss grouping as … http… \"The hi… \"I am… \"The p… #>  2 bioinformatics Sainsc: a computational tool fo… http… \"Spatia… \"I am… \"Sains… #>  3 bioinformatics BRACE: A novel Bayesian-based i… http… \"Bayesi… \"I am… \"Alter… #>  4 bioinformatics Topological embedding and direc… http… \"Cancer… \"I am… \"Resea… #>  5 bioinformatics SeuratExtend: Streamlining Sing… http… \"Single… \"I am… \"Seura… #>  6 bioinformatics An Evolutionary Statistics Tool… http… \"We pre… \"I am… \"The \\… #>  7 bioinformatics A map of integrated cis-regulat… http… \"Cis-re… \"I am… \"The a… #>  8 bioinformatics MOSTPLAS: A Self-correction Mul… http… \"Plasmi… \"I am… \"Plasm… #>  9 bioinformatics Bootstrap Evaluation of Associa… http… \"Motiva… \"I am… \"The a… #> 10 bioinformatics Thermodynamic modeling of Csr/R… http… \"Backgr… \"I am… \"Resea… #> # ℹ 80 more rows tt_preprints(example_preprints[1:2,]) #>  #> +-------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------+-----------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------+ #> | title                                                                                                                                                                               | summary                                                                                                                                                                                                                                                                                                                                                                                                                                                                                 | #> +=====================================================================================================================================================================================+=========================================================================================================================================================================================================================================================================================================================================================================================================================================================================================+ #> | [Integrity and miss grouping as support for clusters in agglomerative hierarchical methods: the R-package octopucs](http://biorxiv.org/cgi/content/short/2024.08.01.606070v1?rss=1) | The proposed method assesses cluster support throughout hierarchical analyses by compiling a consensus topology and using ecological concepts of reciprocal complementarities to define cluster integrity and contamination. This approach allows for building support for groups even when there is partial membership match after resampling, and was implemented in the R package octopucs, which showed robust detection of changes in group memberships compared to other methods. | #> +-------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------+-----------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------+ #> | [Sainsc: a computational tool for segmentation-free analysis of in-situ capture](http://biorxiv.org/cgi/content/short/2024.08.02.603879v1?rss=1)                                    | Sainsc is a computational tool that enables segmentation-free analysis of spatially resolved transcriptomics data, allowing for accurate cell-type assignment at the subcellular level without requiring manual cell border delineation. The tool provides efficient processing of high-resolution spatial data and can generate maps of cell types with corresponding confidence scores, making it a valuable resource for biomedical researchers working with complex tissue samples. | #> +-------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------+-----------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------+"},{"path":"https://stephenturner.github.io/biorecap/news/index.html","id":"biorecap-011","dir":"Changelog","previous_headings":"","what":"biorecap 0.1.1","title":"biorecap 0.1.1","text":"Fix bug add_summary() caused upstream changes ollamar (fixes #1). Bumped minimum required version ollamar 1.2.1.","code":""},{"path":"https://stephenturner.github.io/biorecap/news/index.html","id":"biorecap-010","dir":"Changelog","previous_headings":"","what":"biorecap 0.1.0","title":"biorecap 0.1.0","text":"Initial release.","code":""}]
+[{"path":"https://stephenturner.github.io/biorecap/CONTRIBUTING.html","id":null,"dir":"","previous_headings":"","what":"Contributing to biorecap","title":"Contributing to biorecap","text":"outlines propose change biorecap. detailed discussion contributing tidyverse packages, please see development contributing guide code review principles.","code":""},{"path":"https://stephenturner.github.io/biorecap/CONTRIBUTING.html","id":"fixing-typos","dir":"","previous_headings":"","what":"Fixing typos","title":"Contributing to biorecap","text":"can fix typos, spelling mistakes, grammatical errors documentation directly using GitHub web interface, long changes made source file. generally means ’ll need edit roxygen2 comments .R, .Rd file. can find .R file generates .Rd reading comment first line.","code":""},{"path":"https://stephenturner.github.io/biorecap/CONTRIBUTING.html","id":"bigger-changes","dir":"","previous_headings":"","what":"Bigger changes","title":"Contributing to biorecap","text":"want make bigger change, ’s good idea first file issue make sure someone team agrees ’s needed. ’ve found bug, please file issue illustrates bug minimal reprex (also help write unit test, needed). See guide create great issue advice.","code":""},{"path":"https://stephenturner.github.io/biorecap/CONTRIBUTING.html","id":"pull-request-process","dir":"","previous_headings":"Bigger changes","what":"Pull request process","title":"Contributing to biorecap","text":"Fork package clone onto computer. haven’t done , recommend using usethis::create_from_github(\"stephenturner/biorecap\", fork = TRUE). Install development dependencies devtools::install_dev_deps(), make sure package passes R CMD check running devtools::check(). R CMD check doesn’t pass cleanly, ’s good idea ask help continuing. Create Git branch pull request (PR). recommend using usethis::pr_init(\"brief-description--change\"). Make changes, commit git, create PR running usethis::pr_push(), following prompts browser. title PR briefly describe change. body PR contain Fixes #issue-number. user-facing changes, add bullet top NEWS.md (.e. just first header). Follow style described https://style.tidyverse.org/news.html.","code":""},{"path":"https://stephenturner.github.io/biorecap/CONTRIBUTING.html","id":"code-style","dir":"","previous_headings":"Bigger changes","what":"Code style","title":"Contributing to biorecap","text":"New code follow tidyverse style guide. can use styler package apply styles, please don’t restyle code nothing PR. use roxygen2, Markdown syntax, documentation. use testthat unit tests. Contributions test cases included easier accept.","code":""},{"path":"https://stephenturner.github.io/biorecap/CONTRIBUTING.html","id":"code-of-conduct","dir":"","previous_headings":"","what":"Code of Conduct","title":"Contributing to biorecap","text":"Please note biorecap project released Contributor Code Conduct. contributing project agree abide terms.","code":""},{"path":"https://stephenturner.github.io/biorecap/LICENSE.html","id":null,"dir":"","previous_headings":"","what":"MIT License","title":"MIT License","text":"Copyright (c) 2024 Stephen Turner Permission hereby granted, free charge, person obtaining copy software associated documentation files (“Software”), deal Software without restriction, including without limitation rights use, copy, modify, merge, publish, distribute, sublicense, /sell copies Software, permit persons Software furnished , subject following conditions: copyright notice permission notice shall included copies substantial portions Software. SOFTWARE PROVIDED “”, WITHOUT WARRANTY KIND, EXPRESS IMPLIED, INCLUDING LIMITED WARRANTIES MERCHANTABILITY, FITNESS PARTICULAR PURPOSE NONINFRINGEMENT. EVENT SHALL AUTHORS COPYRIGHT HOLDERS LIABLE CLAIM, DAMAGES LIABILITY, WHETHER ACTION CONTRACT, TORT OTHERWISE, ARISING , CONNECTION SOFTWARE USE DEALINGS SOFTWARE.","code":""},{"path":"https://stephenturner.github.io/biorecap/authors.html","id":null,"dir":"","previous_headings":"","what":"Authors","title":"Authors and Citation","text":"Stephen Turner. Author, maintainer.","code":""},{"path":"https://stephenturner.github.io/biorecap/authors.html","id":"citation","dir":"","previous_headings":"","what":"Citation","title":"Authors and Citation","text":"Turner S (2024). biorecap: Retrieve summarize bioRxiv medRxiv preprints local LLM using ollama. R package version 0.2.0, https://stephenturner.github.io/biorecap/.","code":"@Manual{,   title = {biorecap: Retrieve and summarize bioRxiv and medRxiv preprints with a local LLM using ollama},   author = {Stephen Turner},   year = {2024},   note = {R package version 0.2.0},   url = {https://stephenturner.github.io/biorecap/}, }"},{"path":"https://stephenturner.github.io/biorecap/index.html","id":"biorecap-","dir":"","previous_headings":"","what":"Retrieve and summarize bioRxiv and medRxiv preprints with a local LLM using ollama","title":"Retrieve and summarize bioRxiv and medRxiv preprints with a local LLM using ollama","text":"Retrieve summarize bioRxiv medRxiv preprints using local LLM Ollama via ollamar. Turner, S. D. (2024). biorecap: R package summarizing bioRxiv preprints local LLM. arXiv, 2408.11707. https://doi.org/10.48550/arXiv.2408.11707.","code":""},{"path":"https://stephenturner.github.io/biorecap/index.html","id":"installation","dir":"","previous_headings":"","what":"Installation","title":"Retrieve and summarize bioRxiv and medRxiv preprints with a local LLM using ollama","text":"Install biorecap GitHub (keep dependencies=TRUE get Suggests packages needed create HTML report):","code":"# install.packages(\"remotes\") remotes::install_github(\"stephenturner/biorecap\", dependencies=TRUE)"},{"path":[]},{"path":"https://stephenturner.github.io/biorecap/index.html","id":"quick-start","dir":"","previous_headings":"Usage","what":"Quick start","title":"Retrieve and summarize bioRxiv and medRxiv preprints with a local LLM using ollama","text":"First, load biorecap library. Let’s make sure Ollama running can talk R: Next can list available models: Write HTML report containing summaries recent preprints select subject areas current working directory. can include bioRxiv medRxiv subjects, biorecap know RSS feed use. Example HTML report generated bioRxiv (bioinformatics) infectious diseases (medRxiv) subjects September 25, 2024:","code":"library(biorecap) test_connection() #> Ollama local server running #> <httr2_response> #> GET http://localhost:11434/ #> Status: 200 OK #> Content-Type: text/plain #> Body: In memory (17 bytes) list_models() name   size parameter_size quantization_level            modified 1   gemma2:latest 5.4 GB           9.2B               Q4_0 2024-08-07T07:35:15 3    llama3.1:70b  40 GB          70.6B               Q4_0 2024-07-24T10:57:08 4 llama3.1:latest 4.7 GB           8.0B               Q4_0 2024-07-31T09:38:38 5 llama3.2:latest   2 GB           3.2B             Q4_K_M 2024-09-25T14:54:23 6     phi3:latest 2.2 GB           3.8B               Q4_0 2024-08-28T04:37:58 biorecap_report(output_dir=\".\",                  subject=c(\"bioinformatics\", \"infectious_diseases\"),                  model=\"llama3.1\")"},{"path":"https://stephenturner.github.io/biorecap/index.html","id":"details","dir":"","previous_headings":"Usage","what":"Details","title":"Retrieve and summarize bioRxiv and medRxiv preprints with a local LLM using ollama","text":"get_preprints() function retrieves preprints RSS feed either bioRxiv medRxiv, based subject provided. pass one subjects subject argument. add_prompt() function adds prompt preprint used prompt model. Let’s take look one prompts: giving paper’s title abstract. Summarize paper many sentences instruct. include preamble text. Just give summary. Number sentences summary: 2 Title: SeuratExtend: Streamlining Single-Cell RNA-Seq Analysis Integrated Intuitive Framework Abstract: Single-cell RNA sequencing (scRNA-seq) revolutionized study cellular heterogeneity, rapid expansion analytical tools proven blessing curse, presenting researchers significant challenges. , present SeuratExtend, comprehensive R package built upon widely adopted Seurat framework, streamlines scRNA-seq data analysis integrating essential tools databases. SeuratExtend offers user-friendly intuitive interface performing wide range analyses, including functional enrichment, trajectory inference, gene regulatory network reconstruction, denoising. package seamlessly integrates multiple databases, Gene Ontology Reactome, incorporates popular Python tools like scVelo, Palantir, SCENIC unified R interface. SeuratExtend enhances data visualization optimized plotting functions carefully curated color schemes, ensuring aesthetic appeal scientific rigor. demonstrate SeuratExtend’s performance case studies investigating tumor-associated high-endothelial venules autoinflammatory diseases, showcase novel applications pathway-Level analysis cluster annotation. SeuratExtend empowers researchers harness full potential scRNA-seq data, making complex analyses accessible wider audience. package, along comprehensive documentation tutorials, freely available GitHub, providing valuable resource single-cell genomics community. add_summary() function uses locally running LLM available Ollama summarize preprint. Let’s add summary. Notice can single pipeline. takes minutes! Let’s take look results: Let’s look one summaries. ’s summary SeuratExtend paper (abstract ): SeuratExtend R package integrates essential tools databases single-cell RNA sequencing (scRNA-seq) data analysis, streamlining process user-friendly interface. package offers various analyses, including functional enrichment gene regulatory network reconstruction, seamlessly integrates multiple databases popular Python tools. biorecap_report() function runs code RMarkdown template, writing resulting HTML CSV file results current working directory. built-subjects list vectors containing available bioRxiv medRxiv subjects. create report subjects like (note, take time):","code":"pp <- get_preprints(subject=c(\"bioinformatics\",                                \"infectious_diseases\")) head(pp) tail(pp) #> # A tibble: 6 × 5 #>   source  subject        title                                    url   abstract #>   <chr>   <chr>          <chr>                                    <chr> <chr>    #> 1 bioRxiv bioinformatics MedGraphNet: Leveraging Multi-Relationa… http… Genetic… #> 2 bioRxiv bioinformatics High-throughput bacterial aggregation a… http… The com… #> 3 bioRxiv bioinformatics scParadise: Tunable highly accurate mul… http… scRNA-s… #> 4 bioRxiv bioinformatics Camera Paths, Modeling, and Image Proce… http… The enh… #> 5 bioRxiv bioinformatics dScaff - an automatic bioinformatics fr… http… Rapid e… #> 6 bioRxiv bioinformatics Jaeger: an accurate and fast deep-learn… http… Abstrac… #> # A tibble: 6 × 5 #>   source  subject             title                               url   abstract #>   <chr>   <chr>               <chr>                               <chr> <chr>    #> 1 medRxiv infectious_diseases Reactogenicity and immunogenicity … http… \"The re… #> 2 medRxiv infectious_diseases A next generation CRISPR diagnosti… http… \"The WH… #> 3 medRxiv infectious_diseases Hospital-onset bacteraemia and fun… http… \"Backgr… #> 4 medRxiv infectious_diseases Co-circulating pathogens of humans… http… \"Histor… #> 5 medRxiv infectious_diseases Integration of Group A Streptococc… http… \"The Ca… #> 6 medRxiv infectious_diseases Deep Learning Models for Predictin… http… \"The Nu… pp <- pp |> add_prompt() pp #> # A tibble: 60 × 6 #>    source  subject        title                            url   abstract prompt #>    <chr>   <chr>          <chr>                            <chr> <chr>    <chr>  #>  1 bioRxiv bioinformatics MedGraphNet: Leveraging Multi-R… http… Genetic… I am … #>  2 bioRxiv bioinformatics High-throughput bacterial aggre… http… The com… I am … #>  3 bioRxiv bioinformatics scParadise: Tunable highly accu… http… scRNA-s… I am … #>  4 bioRxiv bioinformatics Camera Paths, Modeling, and Ima… http… The enh… I am … #>  5 bioRxiv bioinformatics dScaff - an automatic bioinform… http… Rapid e… I am … #>  6 bioRxiv bioinformatics Jaeger: an accurate and fast de… http… Abstrac… I am … #>  7 bioRxiv bioinformatics AI-Augmented R-Group Exploratio… http… Efficie… I am … #>  8 bioRxiv bioinformatics OPLS-based Multiclass Classific… http… Multicl… I am … #>  9 bioRxiv bioinformatics STANCE: a unified statistical m… http… A signi… I am … #> 10 bioRxiv bioinformatics AsaruSim: a single-cell and spa… http… Motivat… I am … #> # ℹ 50 more rows pp <-    get_preprints(subject=c(\"bioinformatics\", \"infectious_diseases\")) |>    add_prompt() |>    add_summary(model=\"llama3.2\") pp #> # A tibble: 60 × 7 #>    source  subject        title                    url   abstract prompt summary #>    <chr>   <chr>          <chr>                    <chr> <chr>    <chr>  <chr>   #>  1 bioRxiv bioinformatics MedGraphNet: Leveraging… http… Genetic… I am … MedGra… #>  2 bioRxiv bioinformatics High-throughput bacteri… http… The com… I am … The co… #>  3 bioRxiv bioinformatics scParadise: Tunable hig… http… scRNA-s… I am … scAdam… #>  4 bioRxiv bioinformatics Camera Paths, Modeling,… http… The enh… I am … ArtiaX… #>  5 bioRxiv bioinformatics dScaff - an automatic b… http… Rapid e… I am … dScaff… #>  6 bioRxiv bioinformatics Jaeger: an accurate and… http… Abstrac… I am … Jaeger… #>  7 bioRxiv bioinformatics AI-Augmented R-Group Ex… http… Efficie… I am … The pa… #>  8 bioRxiv bioinformatics OPLS-based Multiclass C… http… Multicl… I am … OPLS-D… #>  9 bioRxiv bioinformatics STANCE: a unified stati… http… A signi… I am … STANCE… #> 10 bioRxiv bioinformatics AsaruSim: a single-cell… http… Motivat… I am … AsaruS… #> # ℹ 50 more rows biorecap_report(output_dir=\".\",                  subject=c(\"bioinformatics\", \"infectious_diseases\"),                  model=\"llama3.2\") subjects$biorxiv #>  [1] \"all\"                                    #>  [2] \"animal_behavior_and_cognition\"          #>  [3] \"biochemistry\"                           #>  [4] \"bioengineering\"                         #>  [5] \"bioinformatics\"                         #>  [6] \"biophysics\"                             #>  [7] \"cancer_biology\"                         #>  [8] \"cell_biology\"                           #>  [9] \"clinical_trials\"                        #> [10] \"developmental_biology\"                  #> [11] \"ecology\"                                #> [12] \"epidemiology\"                           #> [13] \"evolutionary_biology\"                   #> [14] \"genetics\"                               #> [15] \"genomics\"                               #> [16] \"immunology\"                             #> [17] \"microbiology\"                           #> [18] \"molecular_biology\"                      #> [19] \"neuroscience\"                           #> [20] \"paleontology\"                           #> [21] \"pathology\"                              #> [22] \"pharmacology_and_toxicology\"            #> [23] \"plant_biology\"                          #> [24] \"scientific_communication_and_education\" #> [25] \"synthetic_biology\"                      #> [26] \"systems_biology\"                        #> [27] \"zoology\" subjects$medrxiv #>  [1] \"all\"                                          #>  [2] \"addiction_medicine\"                           #>  [3] \"allergy_and_immunology\"                       #>  [4] \"anesthesia\"                                   #>  [5] \"cardiovascular_medicine\"                      #>  [6] \"dentistry_and_oral_medicine\"                  #>  [7] \"dermatology\"                                  #>  [8] \"dermatology\"                                  #>  [9] \"endocrinology\"                                #> [10] \"epidemiology\"                                 #> [11] \"ecology\"                                      #> [12] \"epidemiology\"                                 #> [13] \"forensic_medicine\"                            #> [14] \"gastroenterology\"                             #> [15] \"genetic_and_genomic_medicine\"                 #> [16] \"geriatric_medicine\"                           #> [17] \"health_economics\"                             #> [18] \"health_informatics\"                           #> [19] \"health_policy\"                                #> [20] \"health_systems_and_quality_improvement\"       #> [21] \"hematology\"                                   #> [22] \"hivaids\"                                      #> [23] \"infectious_diseases\"                          #> [24] \"intensive_care_and_critical_care_medicine\"    #> [25] \"medical_education\"                            #> [26] \"medical_ethics\"                               #> [27] \"nephrology\"                                   #> [28] \"neurology\"                                    #> [29] \"nursing\"                                      #> [30] \"nutrition\"                                    #> [31] \"obstetrics_and_gynecology\"                    #> [32] \"occupational_and_environmental_health\"        #> [33] \"oncology\"                                     #> [34] \"ophthalmology\"                                #> [35] \"orthopedics\"                                  #> [36] \"otolaryngology\"                               #> [37] \"pain_medicine\"                                #> [38] \"palliative_medicine\"                          #> [39] \"pathology\"                                    #> [40] \"pediatrics\"                                   #> [41] \"pharmacology_and_therapeutics\"                #> [42] \"primary_care_research\"                        #> [43] \"psychiatry_and_clinical_psychology\"           #> [44] \"public_and_global_health\"                     #> [45] \"radiology_and_imaging\"                        #> [46] \"rehabilitation_medicine_and_physical_therapy\" #> [47] \"respiratory_medicine\"                         #> [48] \"rheumatology\"                                 #> [49] \"sexual_and_reproductive_health\"               #> [50] \"sports_medicine\"                              #> [51] \"surgery\"                                      #> [52] \"toxicology\"                                   #> [53] \"transplantation\"                              #> [54] \"urology\" biorecap_report(output_dir=\".\",                  subject=subjects,                  model=\"llama3.2\")"},{"path":"https://stephenturner.github.io/biorecap/reference/add_prompt.html","id":null,"dir":"Reference","previous_headings":"","what":"Add prompt to a data frame of preprints — add_prompt","title":"Add prompt to a data frame of preprints — add_prompt","text":"Add prompt data frame preprints","code":""},{"path":"https://stephenturner.github.io/biorecap/reference/add_prompt.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Add prompt to a data frame of preprints — add_prompt","text":"","code":"add_prompt(preprints, ...)"},{"path":"https://stephenturner.github.io/biorecap/reference/add_prompt.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Add prompt to a data frame of preprints — add_prompt","text":"preprints Result get_preprints(). ... Additional arguments build_prompt_preprint().","code":""},{"path":"https://stephenturner.github.io/biorecap/reference/add_prompt.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Add prompt to a data frame of preprints — add_prompt","text":"data frame preprints prompt added.","code":""},{"path":[]},{"path":"https://stephenturner.github.io/biorecap/reference/add_prompt.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Add prompt to a data frame of preprints — add_prompt","text":"","code":"preprints <- get_preprints(subject=c(\"bioinformatics\", \"genomics\")) preprints <- add_prompt(preprints) preprints #> # A tibble: 60 × 6 #>    source  subject        title                            url   abstract prompt #>    <chr>   <chr>          <chr>                            <chr> <chr>    <chr>  #>  1 bioRxiv bioinformatics SeaMoon: Prediction of molecula… http… How pro… \"I am… #>  2 bioRxiv bioinformatics Clustering individuals using IN… http… Motivat… \"I am… #>  3 bioRxiv bioinformatics INSPIRE: interpretable, flexibl… http… Recent … \"I am… #>  4 bioRxiv bioinformatics MedGraphNet: Leveraging Multi-R… http… Genetic… \"I am… #>  5 bioRxiv bioinformatics High-throughput bacterial aggre… http… The com… \"I am… #>  6 bioRxiv bioinformatics scParadise: Tunable highly accu… http… scRNA-s… \"I am… #>  7 bioRxiv bioinformatics Camera Paths, Modeling, and Ima… http… The enh… \"I am… #>  8 bioRxiv bioinformatics dScaff - an automatic bioinform… http… Rapid e… \"I am… #>  9 bioRxiv bioinformatics Jaeger: an accurate and fast de… http… Abstrac… \"I am… #> 10 bioRxiv bioinformatics AI-Augmented R-Group Exploratio… http… Efficie… \"I am… #> # ℹ 50 more rows"},{"path":"https://stephenturner.github.io/biorecap/reference/add_prompt_subject.html","id":null,"dir":"Reference","previous_headings":"","what":"Add prompts for an entire subject — add_prompt_subject","title":"Add prompts for an entire subject — add_prompt_subject","text":"Add prompts entire subject","code":""},{"path":"https://stephenturner.github.io/biorecap/reference/add_prompt_subject.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Add prompts for an entire subject — add_prompt_subject","text":"","code":"add_prompt_subject(preprints, ...)"},{"path":"https://stephenturner.github.io/biorecap/reference/add_prompt_subject.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Add prompts for an entire subject — add_prompt_subject","text":"preprints Output get_preprints() followed add_prompt() followed add_summary(). ... Additional arguments build_prompt_subject().","code":""},{"path":"https://stephenturner.github.io/biorecap/reference/add_prompt_subject.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Add prompts for an entire subject — add_prompt_subject","text":"tibble subject prompt column.","code":""},{"path":"https://stephenturner.github.io/biorecap/reference/add_prompt_subject.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Add prompts for an entire subject — add_prompt_subject","text":"","code":"subjects <-   example_preprints |>   dplyr::group_by(subject) |>   add_prompt_subject() #> Warning: Expecting a tibble of class 'preprints_prompt' returned from get_preprints() |> add_prompt(). subjects #> # A tibble: 2 × 2 #>   subject             prompt                                                     #>   <chr>               <chr>                                                      #> 1 bioinformatics      \"I am giving you information about recent bioRxiv/medRxiv… #> 2 infectious_diseases \"I am giving you information about recent bioRxiv/medRxiv…"},{"path":"https://stephenturner.github.io/biorecap/reference/add_summary.html","id":null,"dir":"Reference","previous_headings":"","what":"Generate a summary from a data frame of prompts — add_summary","title":"Generate a summary from a data frame of prompts — add_summary","text":"Generate summary data frame prompts","code":""},{"path":"https://stephenturner.github.io/biorecap/reference/add_summary.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Generate a summary from a data frame of prompts — add_summary","text":"","code":"add_summary(preprints, model = \"llama3.2\")"},{"path":"https://stephenturner.github.io/biorecap/reference/add_summary.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Generate a summary from a data frame of prompts — add_summary","text":"preprints Output get_preprints() followed add_prompt(). model model available Ollama (run ollamar::list_models()) see available.","code":""},{"path":"https://stephenturner.github.io/biorecap/reference/add_summary.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Generate a summary from a data frame of prompts — add_summary","text":"tibble, response column added.","code":""},{"path":"https://stephenturner.github.io/biorecap/reference/add_summary.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Generate a summary from a data frame of prompts — add_summary","text":"","code":"if (FALSE) { # \\dontrun{ # Individual papers preprints <-   get_preprints(c(\"genomics\", \"bioinformatics\")) |>   add_prompt() |>   add_summary() preprints } # }"},{"path":"https://stephenturner.github.io/biorecap/reference/biorecap-package.html","id":null,"dir":"Reference","previous_headings":"","what":"biorecap: Retrieve and summarize bioRxiv and medRxiv preprints with a local LLM using ollama — biorecap-package","title":"biorecap: Retrieve and summarize bioRxiv and medRxiv preprints with a local LLM using ollama — biorecap-package","text":"Retrieve summarize bioRxiv medRxiv preprints local LLM using ollama.","code":""},{"path":[]},{"path":"https://stephenturner.github.io/biorecap/reference/biorecap-package.html","id":"author","dir":"Reference","previous_headings":"","what":"Author","title":"biorecap: Retrieve and summarize bioRxiv and medRxiv preprints with a local LLM using ollama — biorecap-package","text":"Maintainer: Stephen Turner vustephen@gmail.com (ORCID)","code":""},{"path":"https://stephenturner.github.io/biorecap/reference/biorecap_report.html","id":null,"dir":"Reference","previous_headings":"","what":"Create a report from bioRxiv/medRxiv preprints — biorecap_report","title":"Create a report from bioRxiv/medRxiv preprints — biorecap_report","text":"Create report bioRxiv/medRxiv preprints","code":""},{"path":"https://stephenturner.github.io/biorecap/reference/biorecap_report.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Create a report from bioRxiv/medRxiv preprints — biorecap_report","text":"","code":"biorecap_report(   output_dir = \".\",   subject = NULL,   nsentences = 2L,   model = \"llama3.1\",   use_example_preprints = FALSE,   ... )"},{"path":"https://stephenturner.github.io/biorecap/reference/biorecap_report.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Create a report from bioRxiv/medRxiv preprints — biorecap_report","text":"output_dir Directory save report. subject Character vector subjects include report. nsentences Number sentences summarize paper . model model use generating summaries. See ollamar::list_models(). use_example_preprints Use example preprints data included package instead fetching new data bioRxiv/medRxiv. diagnostic/testing purposes . ... arguments passed rmarkdown::render().","code":""},{"path":"https://stephenturner.github.io/biorecap/reference/biorecap_report.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Create a report from bioRxiv/medRxiv preprints — biorecap_report","text":"Nothing; called side effects produce report.","code":""},{"path":"https://stephenturner.github.io/biorecap/reference/biorecap_report.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Create a report from bioRxiv/medRxiv preprints — biorecap_report","text":"","code":"if (FALSE) { # \\dontrun{ output_dir <- tempdir() biorecap_report(use_example_preprints=TRUE, output_dir=output_dir) biorecap_report(subject=c(\"bioinformatics\", \"genomics\", \"synthetic_biology\"),                 output_dir=output_dir) } # }"},{"path":"https://stephenturner.github.io/biorecap/reference/build_prompt_preprint.html","id":null,"dir":"Reference","previous_headings":"","what":"Construct a prompt to summarize a paper — build_prompt_preprint","title":"Construct a prompt to summarize a paper — build_prompt_preprint","text":"Construct prompt summarize paper","code":""},{"path":"https://stephenturner.github.io/biorecap/reference/build_prompt_preprint.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Construct a prompt to summarize a paper — build_prompt_preprint","text":"","code":"build_prompt_preprint(   title,   abstract,   nsentences = 2L,   instructions = c(\"I am giving you a paper's title and abstract.\",     \"Summarize the paper in as many sentences as I instruct.\",     \"Do not include any preamble text to the summary\",     \"just give me the summary with no preface or intro sentence.\") )"},{"path":"https://stephenturner.github.io/biorecap/reference/build_prompt_preprint.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Construct a prompt to summarize a paper — build_prompt_preprint","text":"title title paper. abstract abstract paper. nsentences number sentences summarize paper . instructions Instructions prompt. can character vector gets collapsed single string.","code":""},{"path":"https://stephenturner.github.io/biorecap/reference/build_prompt_preprint.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Construct a prompt to summarize a paper — build_prompt_preprint","text":"string containing prompt.","code":""},{"path":"https://stephenturner.github.io/biorecap/reference/build_prompt_preprint.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Construct a prompt to summarize a paper — build_prompt_preprint","text":"","code":"build_prompt_preprint(title=\"A great paper\", abstract=\"This is the abstract.\") #> [1] \"I am giving you a paper's title and abstract. Summarize the paper in as many sentences as I instruct. Do not include any preamble text to the summary just give me the summary with no preface or intro sentence.\\nNumber of sentences in summary: 2\\nTitle: A great paper\\nAbstract: This is the abstract.\""},{"path":"https://stephenturner.github.io/biorecap/reference/build_prompt_subject.html","id":null,"dir":"Reference","previous_headings":"","what":"Construct a prompt to summarize a set of papers from a subject — build_prompt_subject","title":"Construct a prompt to summarize a set of papers from a subject — build_prompt_subject","text":"Construct prompt summarize set papers subject","code":""},{"path":"https://stephenturner.github.io/biorecap/reference/build_prompt_subject.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Construct a prompt to summarize a set of papers from a subject — build_prompt_subject","text":"","code":"build_prompt_subject(   subject,   title,   summary,   nsentences = 5L,   instructions = c(\"I am giving you information about recent bioRxiv/medRxiv preprints.\",     \"I'll give you the subject, preprint titles, and short summary of each paper.\",     \"Please provide a general summary new advances in this subject/field in general.\",     \"Provide this summary of the field in as many sentences as I instruct.\",     \"Do not include any preamble text to the summary\",     \"just give me the summary with no preface or intro sentence.\") )"},{"path":"https://stephenturner.github.io/biorecap/reference/build_prompt_subject.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Construct a prompt to summarize a set of papers from a subject — build_prompt_subject","text":"subject name subject. title character vector titles subject summary character vector summaries paper provided get_preprints() followed add_prompt() followed add_summary(). nsentences number sentences summarize subject . instructions Instructions prompt. can character vector gets collapsed single string.","code":""},{"path":"https://stephenturner.github.io/biorecap/reference/build_prompt_subject.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Construct a prompt to summarize a set of papers from a subject — build_prompt_subject","text":"string containing prompt.","code":""},{"path":"https://stephenturner.github.io/biorecap/reference/build_prompt_subject.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Construct a prompt to summarize a set of papers from a subject — build_prompt_subject","text":"","code":"title <- example_preprints |> dplyr::filter(subject==\"bioinformatics\") |> dplyr::pull(title) summary <- example_preprints |> dplyr::filter(subject==\"bioinformatics\") |> dplyr::pull(summary) build_prompt_subject(subject=\"bioinformatics\", title=title, summary=summary) #> [1] \"I am giving you information about recent bioRxiv/medRxiv preprints. I'll give you the subject, preprint titles, and short summary of each paper. Please provide a general summary new advances in this subject/field in general. Provide this summary of the field in as many sentences as I instruct. Do not include any preamble text to the summary just give me the summary with no preface or intro sentence.\\n\\nSubject: bioinformatics\\nNumber of sentences in summary: 5\\n\\nHere are the titles and summaries:\\n\\nTitle: MedGraphNet: Leveraging Multi-Relational Graph Neural Networks and Text Knowledge for Biomedical Predictions\\nSummary: MedGraphNet leverages multi-relational Graph Neural Networks and text knowledge to improve biomedical predictions by initializing nodes using informative embeddings from existing text knowledge, allowing for robust integration of various data types and improved generalizability. The model demonstrates superior performance compared to traditional single-relation approaches in scenarios with isolated or sparsely connected nodes, particularly in identifying disease-gene associations and drug-phenotype relationships, and shows promising results in accurately inferring drug side effects without direct training on such data.\\n\\nTitle: High-throughput bacterial aggregation analysis in droplets\\nSummary: The communal lifestyle of bacteria can contribute significantly to antimicrobial resistance by promoting biofilm formation. A key approach to addressing this issue is to develop novel techniques for analyzing bacterial behavior, such as those enabled by droplet-based platforms and image analysis methods.\\n\\nTitle: scParadise: Tunable highly accurate multi-task cell type annotation and surface protein abundance prediction\\nSummary: scAdam outperforms existing methods in annotating rare cell types with high accuracy and consistency across diverse datasets. scEve enhances clustering and cell type separation through improved surface protein prediction, leading to better characterization of complex tissues.\\n\\nTitle: Camera Paths, Modeling, and Image Processing Tools for ArtiaX\\nSummary: ArtiaX is a plugin that has been extended to improve the analysis and visualization of cryo-electron tomography data through advanced visualization techniques. The plugin allows for the generation of diverse models with putative particle positions and orientations, as well as a coarse grained algorithm to rectify overlaps in template matching, driving camera position and facilitating movie creation with fundamental image filtering options.\\n\\nTitle: dScaff - an automatic bioinformatics framework for scaffolding draft de novo assemblies based on reference genome data\\nSummary: dScaff is an automatic bioinformatics framework designed for scaffolding draft de novo assemblies based on reference genome data. The tool uses a series of bash and R scripts to create a minimal complete scaffold from a genome assembly, with potential future features to be implemented, including using reference chromosomes or scaffolds.\\n\\nTitle: Jaeger: an accurate and fast deep-learning tool to detect bacteriophage sequences\\nSummary: Jaeger's accuracy and speed in identifying bacteriophage sequences outperform existing deep-learning tools by consistently producing few false positives despite encountering diverse viral sequences. The novel method achieves an estimated 2-27% false discovery rate when applied to over 16,000 metagenomic assemblies, which is significantly lower than the benchmarking paper where deep-learning tools produced many false positives.\\n\\nTitle: AI-Augmented R-Group Exploration in Medicinal Chemistry\\nSummary: The paper presents a novel approach to enhancing free-wing QSAR models by embedding R-groups with atom-centric pharmacophoric features, allowing for the distinction of regioisomers and improved predictivity across 12 public datasets. The proposed method is integrated into an open-source program, enabling its application in various scenarios, including classic free-Wilson analysis and exploration of uncharted chemical space facilitated by AI-generated building blocks.\\n\\nTitle: OPLS-based Multiclass Classification and Data-Driven Inter-Class Relationship Discovery\\nSummary: OPLS-DA models are widely used in metabolomics for two-class comparisons due to their strong discrimination capabilities, but these models face challenges in multiclass settings. An extension of OPLS-DA called OPLS-HDA integrates Hierarchical Cluster Analysis with the OPLS-DA framework to create a decision tree that addresses multiclass classification challenges and provides intuitive visualization of inter-class relationships.\\n\\nTitle: STANCE: a unified statistical model to detect cell-type-specific spatially variable genes in spatial transcriptomics\\nSummary: STANCE, a unified statistical model to detect cell-type-specific spatially variable genes in spatial transcriptomics, was developed to address the challenges posed by existing methods in detecting spatially variable genes (SVGs) and cell type-specific spatially variable genes (ctSVGs). The proposed method integrates gene expression, spatial location, and cell type composition through a linear mixed-effect model to identify both SVGs and ctSVGs in an initial stage, followed by a second stage test dedicated to ctSVG detection.\\n\\nTitle: AsaruSim: a single-cell and spatial RNA-Seq Nanopore long-reads simulation workflow\\nSummary: AsaruSim simulates synthetic single-cell long-read Nanopore datasets that closely mimic real experimental data by employing a multi-step process. It includes the creation of a synthetic UMI count matrix, generation of perfect reads, optional PCR amplification, introduction of sequencing errors, and comprehensive quality control reporting.\\n\\nTitle: Building a literature knowledge base towards transparent biomedical AI\\nSummary: LiteralGraph extracts biomedical terms and relationships from PubMed literature, establishing a comprehensive knowledge graph. The resulting Genomic Literature Knowledge Base consolidates over 263 million biomedical terms, 14 million relationships, and 10 million genomic events across multiple sources, including nine established repositories.\\n\\nTitle: Accurate non-invasive quantification of astaxanthin content using hyperspectral images and machine learning\\nSummary: The authors investigated a method to accurately quantify astaxanthin content in Haematococcus pluvialis microalgae cultures using hyperspectral images and machine learning. They found that this approach, combining reflectance hyperspectral imaging with a 1-dimensional convolutional neural network, had low average prediction error across a range of astaxanthin contents, although it was unreliable at very low levels (<0.6 micrograms mg-1).\\n\\nTitle: AlphaMut: a deep reinforcement learning model to suggest helix-disrupting mutations\\nSummary: The authors propose a deep reinforcement learning model called AlphaMut to predict helix-disrupting mutations in proteins. AlphaMut identifies amino acids crucial for maintaining structural integrity and predicts key mutations that could alter protein function.\\n\\nTitle: Beyond Static Brain Atlases: AI-Powered Open Databasing and Dynamic Mining of Brain-Wide Neuron Morphometry\\nSummary: NeuroXiv is a large-scale database that provides detailed 3D morphologies of individual neurons mapped to a standard brain atlas, allowing for dynamic, interactive neuroscience applications. The database offers a comprehensive collection of 175,149 atlas-oriented reconstructed morphologies of individual neurons from over 518 mouse brains, classified into 292 distinct types and mapped into the Common Coordinate Framework Version 3 (CCFv3).\\n\\nTitle: Metabolic modeling identifies determinants of thermal growth responses in Arabidopsis thaliana\\nSummary: The paper developed an enzyme-constrained model of Arabidopsis thaliana's metabolism, which facilitates predictions of growth-related phenotypes at different temperatures and identifies genes affecting plant growth at suboptimal temperatures. This model was validated using mutant lines, demonstrating its potential in accurately predicting plant thermal responses and providing a template for developing climate-resilient crops.\\n\\nTitle: Decoding Protein Dynamics: ProFlex as a Linguistic Bridge in Normal Mode Analysis\\nSummary: Artificial intelligence has revolutionized structural bioinformatics with AlphaFold being arguably the most impactful development to date. The structural atlases generated by these methods present significant opportunities for unraveling biological mysteries, but also pose challenges in leveraging such massive datasets effectively.\\n\\nTitle: Exploring midgut expression dynamics: longitudinal transcriptomic analysis of adult female Amblyomma americanum midgut and comparative insights with other hard tick species\\nSummary: The study investigates the transcriptomic dynamics of the midgut in adult female Amblyomma americanum ticks during different feeding stages, revealing 15,599 putative DNA coding sequences and highlighting dynamic transcriptional changes as feeding progresses. The analysis also identified conserved transcripts across three hard tick species, providing insight into the physiological pathways relevant to the tick midgut and potential avenues for developing control methods targeting multiple tick species.\\n\\nTitle: Designing of thermostable proteins with a desired melting temperature\\nSummary: We developed a regression method for predicting protein melting temperatures (Tm) using 17,312 non-redundant proteins and achieved the highest Pearson correlation of 0.80 with an R2 of 0.63 between predicted and actual Tm values. Our best model, fine-tuned on large language models such as ProtBert, achieved a maximum correlation of 0.89 with an R2 of 0.80, demonstrating improved performance in predicting protein stability at higher temperatures.\\n\\nTitle: Joint Modeling of Cellular Heterogeneity and Condition Effects with scPCA in Single-Cell RNA-Seq\\nSummary: scRNA-seq in multi-condition experiments enables the systematic assessment of treatment effects by analyzing gene expression profiles.   scPCA is a flexible DR framework that jointly models cellular heterogeneity and conditioning variables, allowing for an integrated factor representation and revealing transcriptional changes across conditions and components.\\n\\nTitle: Identification of potential inhibitors against Inosine 5'-Monophosphate Dehydrogenase of Cryptosporidium parvum through an integrated in silico approach\\nSummary: A total of 24 bioactive phytochemicals were screened virtually using molecular docking and ADMET analyses to identify potential inhibitors against Inosine 5'-Monophosphate Dehydrogenase (IMPDH) of Cryptosporidium parvum, with four lead compounds identified as Brevelin A, Vernodalin, Luteolin, and Pectolinarigenin. The lead compounds were found to possess favorable pharmacokinetic and pharmacodynamic properties, satisfactory toxicity analysis results, and no major side effects or violation of Lipinski's rules of five, indicating the possibility of oral bioavailability as potential drug candidates.\\n\\nTitle: Identification and Diagnostic Potential of Pyroptosis-Related Genes in Endometriosis: A Novel Bioinformatics Analysis\\nSummary: Pyroptosis-related genes were identified through a bioinformatics analysis of endometriosis (EM) transcriptomic datasets, resulting in 26 differentially expressed genes that play a crucial role in the pathogenesis of EM.  A novel diagnostic model was constructed using LASSO regression based on pyroptosis scores, which included five key genes: KIF13B, BAG6, MYO5A, HEATR, and AK055981.\\n\\nTitle: Improving the accuracy of pose prediction by incorporating symmetry-related molecules\\nSummary: The study aimed to improve the accuracy of pose prediction in molecular docking by incorporating symmetry-related molecules (SRMs). Redocking protein-ligand complexes with and without SRMs revealed that using SRMs significantly improved the prediction of biologically significant poses, as indicated by MM-GBSA calculations.\\n\\nTitle: Identification and study of Prolyl Oligopeptidases and related sequences in bacterial lineages\\nSummary: The study examined ~32000 completely annotated bacterial genomes from the NCBI RefSeq Assembly database to identify annotated S9 family proteins, resulting in the discovery of ~53,000 bacterial S9 family proteins (referred to as POP homologues) which can be classified into distinct subfamilies through various machine-learning approaches and comprehensive analysis. These sequence homologues display distinct subclusters and class-specific motifs suggesting differences in substrate specificity in POP homologues.\\n\\nTitle: Learning-Augmented Sketching Offers Improved Performance for Privacy Preserving and Secure GWAS\\nSummary: The introduction of trusted execution environments (TEEs) such as Intel SGX technology has enabled secure and privacy-preserving computation on the cloud, but stringent resource limitations pose a challenge for some TEEs. The SkSES method, which identifies significant SNPs in GWAS without disclosing sensitive genotype information, has been improved upon with a learning-augmented approach that achieves up to 40% accuracy gain compared to the original SkSES method.\\n\\nTitle: Liberality is More Explainable than PCA of Transcriptome for Vertebrate Embryo Development\\nSummary: Liberality is a quantitative index of cellular differentiation and dedifferentiation that has been widely used for genome-scale data analysis, particularly in understanding vertebrate embryo development. The study analyzed a time course transcriptome dataset on vertebrate embryo development and found a trend that historically annotated embryo developmental stages matched changes in liberality, indicating the potential of liberality to analyze biological phenomena beyond just embryo development.\\n\\nTitle: Bacopa monnieri phytochemicals as promising BACE1 inhibitors for Alzheimers Disease Therapy\\nSummary: Bacopa monnieri phytochemicals are investigated as potential BACE1 inhibitors for Alzheimer's Disease Therapy, with Bacopaside I showing superior binding affinity and interaction profile compared to established synthetic inhibitors. The study highlights the promising role of natural compounds in AD treatment, emphasizing their potential to overcome limitations faced in clinical settings, and advocates for a paradigm shift towards integrating traditional medicinal knowledge into contemporary drug discovery efforts.\\n\\nTitle: Accurate Multiple Sequence Alignment of Ultramassive Genome Sets\\nSummary: The current state of multiple sequence alignment (MSA) is insufficient for handling ultramassive genome sets due to challenges in scalability and accuracy. The proposed algorithms, including directed acyclic graph construction, profile hidden Markov model training, and graph-based alignment, significantly improve accuracy and acceleration of MSA compared to widely used MAFFT for genome set sizes ranging from 40,000 to over 4 million.\\n\\nTitle: Machine Learning Driven Simulations of SARS-CoV-2 Fitness Landscape\\nSummary: The SARS-CoV-2 infection is caused by interactions between the receptor binding domain of viral spike proteins and host cell ACE2 receptors, with mutations in the spike protein leading to neutralizing antibody escape and breakthrough infections. Machine learning-driven simulations combined with deep mutational scanning data predict variants of concern not seen in the training data and sample statistics of the fitness landscape, providing insight into the relationship between RBD sequence elements and emerging viral strains.\\n\\nTitle: Modelling dynamics of human NDPK hexamer structure, stability and interactions\\nSummary: The precise assembly of the NDPK hexameric structure into homo- /hetero-oligomeric complexes is necessary for kinase activity but has been poorly understood due to high subunit homology, experimental challenges, and limited data on in vivo heterohexamer formation and subunit abundances across cellular compartments. A conserved Arg27 residue plays a key role in hexamer assembly, mediating inter- and intra-molecular monomeric interactions and ensuring similar hexameric assembly across subunits.\\n\\nTitle: GuaCAMOLE: GC-bias aware estimation improves the accuracy of metagenomic species abundances\\nSummary: GuaCAMOLE is a novel computational method that detects and removes GC bias from metagenomic sequencing data, which affects the accuracy of quantifying microbial community compositions. The algorithm reports unbiased abundances and corrects the abundance of clinically relevant GC-poor species by up to a factor of two in gut microbiomes of colorectal cancer patients.\""},{"path":"https://stephenturner.github.io/biorecap/reference/example_preprints.html","id":null,"dir":"Reference","previous_headings":"","what":"Example preprints with summaries — example_preprints","title":"Example preprints with summaries — example_preprints","text":"Example preprints summaries August 6, 2024.","code":""},{"path":"https://stephenturner.github.io/biorecap/reference/example_preprints.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Example preprints with summaries — example_preprints","text":"","code":"example_preprints"},{"path":"https://stephenturner.github.io/biorecap/reference/example_preprints.html","id":"format","dir":"Reference","previous_headings":"","what":"Format","title":"Example preprints with summaries — example_preprints","text":"tibble returned get_preprints() followed add_prompt() followed add_summary().","code":""},{"path":"https://stephenturner.github.io/biorecap/reference/example_preprints.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Example preprints with summaries — example_preprints","text":"","code":"example_preprints #> # A tibble: 60 × 7 #>    source  subject        title                    url   abstract prompt summary #>    <chr>   <chr>          <chr>                    <chr> <chr>    <chr>  <chr>   #>  1 bioRxiv bioinformatics MedGraphNet: Leveraging… http… Genetic… I am … MedGra… #>  2 bioRxiv bioinformatics High-throughput bacteri… http… The com… I am … The co… #>  3 bioRxiv bioinformatics scParadise: Tunable hig… http… scRNA-s… I am … scAdam… #>  4 bioRxiv bioinformatics Camera Paths, Modeling,… http… The enh… I am … ArtiaX… #>  5 bioRxiv bioinformatics dScaff - an automatic b… http… Rapid e… I am … dScaff… #>  6 bioRxiv bioinformatics Jaeger: an accurate and… http… Abstrac… I am … Jaeger… #>  7 bioRxiv bioinformatics AI-Augmented R-Group Ex… http… Efficie… I am … The pa… #>  8 bioRxiv bioinformatics OPLS-based Multiclass C… http… Multicl… I am … OPLS-D… #>  9 bioRxiv bioinformatics STANCE: a unified stati… http… A signi… I am … STANCE… #> 10 bioRxiv bioinformatics AsaruSim: a single-cell… http… Motivat… I am … AsaruS… #> # ℹ 50 more rows"},{"path":"https://stephenturner.github.io/biorecap/reference/get_preprints.html","id":null,"dir":"Reference","previous_headings":"","what":"Get bioRxiv/medRxiv preprints — get_preprints","title":"Get bioRxiv/medRxiv preprints — get_preprints","text":"Get bioRxiv/medRxiv preprints","code":""},{"path":"https://stephenturner.github.io/biorecap/reference/get_preprints.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Get bioRxiv/medRxiv preprints — get_preprints","text":"","code":"get_preprints(subject = \"all\", clean = TRUE)"},{"path":"https://stephenturner.github.io/biorecap/reference/get_preprints.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Get bioRxiv/medRxiv preprints — get_preprints","text":"subject character vector valid bioRxiv /medRxiv subjects. See subjects. clean Logical; try strip graphical abstract information? TRUE, strips away text O_FIG C_FIG, words graphical abstract abstract text RSS feed.","code":""},{"path":"https://stephenturner.github.io/biorecap/reference/get_preprints.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Get bioRxiv/medRxiv preprints — get_preprints","text":"data frame preprints bioRxiv /medRxiv.","code":""},{"path":"https://stephenturner.github.io/biorecap/reference/get_preprints.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Get bioRxiv/medRxiv preprints — get_preprints","text":"","code":"preprints <- get_preprints(subject=c(\"bioinformatics\", \"Public_and_Global_Health\")) preprints #> # A tibble: 60 × 5 #>    source  subject        title                                   url   abstract #>    <chr>   <chr>          <chr>                                   <chr> <chr>    #>  1 bioRxiv bioinformatics SeaMoon: Prediction of molecular motio… http… How pro… #>  2 bioRxiv bioinformatics Clustering individuals using INMTD: a … http… Motivat… #>  3 bioRxiv bioinformatics INSPIRE: interpretable, flexible and s… http… Recent … #>  4 bioRxiv bioinformatics MedGraphNet: Leveraging Multi-Relation… http… Genetic… #>  5 bioRxiv bioinformatics High-throughput bacterial aggregation … http… The com… #>  6 bioRxiv bioinformatics scParadise: Tunable highly accurate mu… http… scRNA-s… #>  7 bioRxiv bioinformatics Camera Paths, Modeling, and Image Proc… http… The enh… #>  8 bioRxiv bioinformatics dScaff - an automatic bioinformatics f… http… Rapid e… #>  9 bioRxiv bioinformatics Jaeger: an accurate and fast deep-lear… http… Abstrac… #> 10 bioRxiv bioinformatics AI-Augmented R-Group Exploration in Me… http… Efficie… #> # ℹ 50 more rows"},{"path":"https://stephenturner.github.io/biorecap/reference/reexports.html","id":null,"dir":"Reference","previous_headings":"","what":"Objects exported from other packages — reexports","title":"Objects exported from other packages — reexports","text":"objects imported packages. Follow links see documentation. ollamar list_models, test_connection","code":""},{"path":"https://stephenturner.github.io/biorecap/reference/subjects.html","id":null,"dir":"Reference","previous_headings":"","what":"bioRxiv subjects — subjects","title":"bioRxiv subjects — subjects","text":"Names subjects RSS feeds biorXiv","code":""},{"path":"https://stephenturner.github.io/biorecap/reference/subjects.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"bioRxiv subjects — subjects","text":"","code":"subjects"},{"path":"https://stephenturner.github.io/biorecap/reference/subjects.html","id":"format","dir":"Reference","previous_headings":"","what":"Format","title":"bioRxiv subjects — subjects","text":"list character vectors subjects, one bioRxiv, one medRxiv.","code":""},{"path":"https://stephenturner.github.io/biorecap/reference/subjects.html","id":"references","dir":"Reference","previous_headings":"","what":"References","title":"bioRxiv subjects — subjects","text":"https://www.biorxiv.org/alertsrss","code":""},{"path":"https://stephenturner.github.io/biorecap/reference/subjects.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"bioRxiv subjects — subjects","text":"","code":"subjects #> $biorxiv #>  [1] \"all\"                                    #>  [2] \"animal_behavior_and_cognition\"          #>  [3] \"biochemistry\"                           #>  [4] \"bioengineering\"                         #>  [5] \"bioinformatics\"                         #>  [6] \"biophysics\"                             #>  [7] \"cancer_biology\"                         #>  [8] \"cell_biology\"                           #>  [9] \"clinical_trials\"                        #> [10] \"developmental_biology\"                  #> [11] \"ecology\"                                #> [12] \"epidemiology\"                           #> [13] \"evolutionary_biology\"                   #> [14] \"genetics\"                               #> [15] \"genomics\"                               #> [16] \"immunology\"                             #> [17] \"microbiology\"                           #> [18] \"molecular_biology\"                      #> [19] \"neuroscience\"                           #> [20] \"paleontology\"                           #> [21] \"pathology\"                              #> [22] \"pharmacology_and_toxicology\"            #> [23] \"plant_biology\"                          #> [24] \"scientific_communication_and_education\" #> [25] \"synthetic_biology\"                      #> [26] \"systems_biology\"                        #> [27] \"zoology\"                                #>  #> $medrxiv #>  [1] \"all\"                                          #>  [2] \"addiction_medicine\"                           #>  [3] \"allergy_and_immunology\"                       #>  [4] \"anesthesia\"                                   #>  [5] \"cardiovascular_medicine\"                      #>  [6] \"dentistry_and_oral_medicine\"                  #>  [7] \"dermatology\"                                  #>  [8] \"dermatology\"                                  #>  [9] \"endocrinology\"                                #> [10] \"epidemiology\"                                 #> [11] \"ecology\"                                      #> [12] \"epidemiology\"                                 #> [13] \"forensic_medicine\"                            #> [14] \"gastroenterology\"                             #> [15] \"genetic_and_genomic_medicine\"                 #> [16] \"geriatric_medicine\"                           #> [17] \"health_economics\"                             #> [18] \"health_informatics\"                           #> [19] \"health_policy\"                                #> [20] \"health_systems_and_quality_improvement\"       #> [21] \"hematology\"                                   #> [22] \"hivaids\"                                      #> [23] \"infectious_diseases\"                          #> [24] \"intensive_care_and_critical_care_medicine\"    #> [25] \"medical_education\"                            #> [26] \"medical_ethics\"                               #> [27] \"nephrology\"                                   #> [28] \"neurology\"                                    #> [29] \"nursing\"                                      #> [30] \"nutrition\"                                    #> [31] \"obstetrics_and_gynecology\"                    #> [32] \"occupational_and_environmental_health\"        #> [33] \"oncology\"                                     #> [34] \"ophthalmology\"                                #> [35] \"orthopedics\"                                  #> [36] \"otolaryngology\"                               #> [37] \"pain_medicine\"                                #> [38] \"palliative_medicine\"                          #> [39] \"pathology\"                                    #> [40] \"pediatrics\"                                   #> [41] \"pharmacology_and_therapeutics\"                #> [42] \"primary_care_research\"                        #> [43] \"psychiatry_and_clinical_psychology\"           #> [44] \"public_and_global_health\"                     #> [45] \"radiology_and_imaging\"                        #> [46] \"rehabilitation_medicine_and_physical_therapy\" #> [47] \"respiratory_medicine\"                         #> [48] \"rheumatology\"                                 #> [49] \"sexual_and_reproductive_health\"               #> [50] \"sports_medicine\"                              #> [51] \"surgery\"                                      #> [52] \"toxicology\"                                   #> [53] \"transplantation\"                              #> [54] \"urology\"                                      #>"},{"path":"https://stephenturner.github.io/biorecap/reference/tt_preprints.html","id":null,"dir":"Reference","previous_headings":"","what":"Create a markdown table from prepreprint summaries — tt_preprints","title":"Create a markdown table from prepreprint summaries — tt_preprints","text":"Create markdown table prepreprint summaries","code":""},{"path":"https://stephenturner.github.io/biorecap/reference/tt_preprints.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Create a markdown table from prepreprint summaries — tt_preprints","text":"","code":"tt_preprints(preprints, cols = c(\"title\", \"summary\"), width = c(1, 3))"},{"path":"https://stephenturner.github.io/biorecap/reference/tt_preprints.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Create a markdown table from prepreprint summaries — tt_preprints","text":"preprints Output get_preprints() followed add_prompt() followed add_summary(). cols Columns display resulting markdown table. width Vector relative widths equal length(cols).","code":""},{"path":"https://stephenturner.github.io/biorecap/reference/tt_preprints.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Create a markdown table from prepreprint summaries — tt_preprints","text":"tinytable table.","code":""},{"path":"https://stephenturner.github.io/biorecap/reference/tt_preprints.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Create a markdown table from prepreprint summaries — tt_preprints","text":"","code":"# Use built-in example data example_preprints #> # A tibble: 60 × 7 #>    source  subject        title                    url   abstract prompt summary #>    <chr>   <chr>          <chr>                    <chr> <chr>    <chr>  <chr>   #>  1 bioRxiv bioinformatics MedGraphNet: Leveraging… http… Genetic… I am … MedGra… #>  2 bioRxiv bioinformatics High-throughput bacteri… http… The com… I am … The co… #>  3 bioRxiv bioinformatics scParadise: Tunable hig… http… scRNA-s… I am … scAdam… #>  4 bioRxiv bioinformatics Camera Paths, Modeling,… http… The enh… I am … ArtiaX… #>  5 bioRxiv bioinformatics dScaff - an automatic b… http… Rapid e… I am … dScaff… #>  6 bioRxiv bioinformatics Jaeger: an accurate and… http… Abstrac… I am … Jaeger… #>  7 bioRxiv bioinformatics AI-Augmented R-Group Ex… http… Efficie… I am … The pa… #>  8 bioRxiv bioinformatics OPLS-based Multiclass C… http… Multicl… I am … OPLS-D… #>  9 bioRxiv bioinformatics STANCE: a unified stati… http… A signi… I am … STANCE… #> 10 bioRxiv bioinformatics AsaruSim: a single-cell… http… Motivat… I am … AsaruS… #> # ℹ 50 more rows tt_preprints(example_preprints[1:2,]) #>  #> +--------------------------------------------------------------------------------------------------------------------------------------------------------------------------------+----------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------+ #> | title                                                                                                                                                                          | summary                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                            | #> +================================================================================================================================================================================+====================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================+ #> | [MedGraphNet: Leveraging Multi-Relational Graph Neural Networks and Text Knowledge for Biomedical Predictions](http://biorxiv.org/cgi/content/short/2024.09.24.614782v1?rss=1) | MedGraphNet leverages multi-relational Graph Neural Networks and text knowledge to improve biomedical predictions by initializing nodes using informative embeddings from existing text knowledge, allowing for robust integration of various data types and improved generalizability. The model demonstrates superior performance compared to traditional single-relation approaches in scenarios with isolated or sparsely connected nodes, particularly in identifying disease-gene associations and drug-phenotype relationships, and shows promising results in accurately inferring drug side effects without direct training on such data. | #> +--------------------------------------------------------------------------------------------------------------------------------------------------------------------------------+----------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------+ #> | [High-throughput bacterial aggregation analysis in droplets](http://biorxiv.org/cgi/content/short/2024.09.24.613170v1?rss=1)                                                   | The communal lifestyle of bacteria can contribute significantly to antimicrobial resistance by promoting biofilm formation. A key approach to addressing this issue is to develop novel techniques for analyzing bacterial behavior, such as those enabled by droplet-based platforms and image analysis methods.                                                                                                                                                                                                                                                                                                                                  | #> +--------------------------------------------------------------------------------------------------------------------------------------------------------------------------------+----------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------+"},{"path":"https://stephenturner.github.io/biorecap/news/index.html","id":"biorecap-020","dir":"Changelog","previous_headings":"","what":"biorecap 0.2.0","title":"biorecap 0.2.0","text":"Added medRxiv support. get_preprints() function now pull either bioRxiv medRxiv RSS feed depending subject passed . downstream functions reporting updated reflect change (fixes #5). Changed default model llama 3.2 3B. Added new source column returned preprints indicating whether preprint came bioRxiv medRxiv. Updated tests.","code":""},{"path":"https://stephenturner.github.io/biorecap/news/index.html","id":"biorecap-011","dir":"Changelog","previous_headings":"","what":"biorecap 0.1.1","title":"biorecap 0.1.1","text":"Fix bug add_summary() caused upstream changes ollamar (fixes #1). Bumped minimum required version ollamar 1.2.1.","code":""},{"path":"https://stephenturner.github.io/biorecap/news/index.html","id":"biorecap-010","dir":"Changelog","previous_headings":"","what":"biorecap 0.1.0","title":"biorecap 0.1.0","text":"Initial release.","code":""}]