diff --git a/notebooks/Indra publication histogram.ipynb b/notebooks/Indra publication histogram.ipynb
new file mode 100644
index 000000000..8ae94ea7a
--- /dev/null
+++ b/notebooks/Indra publication histogram.ipynb	
@@ -0,0 +1,1012 @@
+{
+ "cells": [
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "id": "4aba301c",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "INFO: [2024-03-14 01:54:12] numexpr.utils - NumExpr defaulting to 8 threads.\n",
+      "INFO: [2024-03-14 01:54:14] indra_cogex.client.neo4j_client - Using configured URL for INDRA neo4j connection\n",
+      "INFO: [2024-03-14 01:54:14] indra_cogex.client.neo4j_client - Using configured credentials for INDRA neo4j connection\n"
+     ]
+    }
+   ],
+   "source": [
+    "from indra_cogex.client import Neo4jClient\n",
+    "import pandas as pd\n",
+    "import matplotlib.pyplot as plt\n",
+    "import numpy as np\n",
+    "import seaborn as sns\n",
+    "client = Neo4jClient()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 7,
+   "id": "d6475d03",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 640x480 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 640x480 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "There is an positive trend in the frequency of publications on  structural constituent of muscle\n"
+     ]
+    },
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
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+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>id</th>\n",
+       "      <th>name</th>\n",
+       "      <th>year</th>\n",
+       "      <th>count</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>go:0008307</td>\n",
+       "      <td>structural constituent of muscle</td>\n",
+       "      <td>2020.0</td>\n",
+       "      <td>114</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>go:0008307</td>\n",
+       "      <td>structural constituent of muscle</td>\n",
+       "      <td>2021.0</td>\n",
+       "      <td>49</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>go:0008307</td>\n",
+       "      <td>structural constituent of muscle</td>\n",
+       "      <td>2009.0</td>\n",
+       "      <td>86</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>go:0008307</td>\n",
+       "      <td>structural constituent of muscle</td>\n",
+       "      <td>2007.0</td>\n",
+       "      <td>41</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>go:0008307</td>\n",
+       "      <td>structural constituent of muscle</td>\n",
+       "      <td>2012.0</td>\n",
+       "      <td>55</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>5</th>\n",
+       "      <td>go:0008307</td>\n",
+       "      <td>structural constituent of muscle</td>\n",
+       "      <td>2011.0</td>\n",
+       "      <td>66</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>6</th>\n",
+       "      <td>go:0008307</td>\n",
+       "      <td>structural constituent of muscle</td>\n",
+       "      <td>2013.0</td>\n",
+       "      <td>64</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>7</th>\n",
+       "      <td>go:0008307</td>\n",
+       "      <td>structural constituent of muscle</td>\n",
+       "      <td>2016.0</td>\n",
+       "      <td>52</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>8</th>\n",
+       "      <td>go:0008307</td>\n",
+       "      <td>structural constituent of muscle</td>\n",
+       "      <td>2008.0</td>\n",
+       "      <td>25</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>9</th>\n",
+       "      <td>go:0008307</td>\n",
+       "      <td>structural constituent of muscle</td>\n",
+       "      <td>2015.0</td>\n",
+       "      <td>48</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>10</th>\n",
+       "      <td>go:0008307</td>\n",
+       "      <td>structural constituent of muscle</td>\n",
+       "      <td>2019.0</td>\n",
+       "      <td>49</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>11</th>\n",
+       "      <td>go:0008307</td>\n",
+       "      <td>structural constituent of muscle</td>\n",
+       "      <td>2003.0</td>\n",
+       "      <td>40</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>12</th>\n",
+       "      <td>go:0008307</td>\n",
+       "      <td>structural constituent of muscle</td>\n",
+       "      <td>1998.0</td>\n",
+       "      <td>55</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>13</th>\n",
+       "      <td>go:0008307</td>\n",
+       "      <td>structural constituent of muscle</td>\n",
+       "      <td>1999.0</td>\n",
+       "      <td>23</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>14</th>\n",
+       "      <td>go:0008307</td>\n",
+       "      <td>structural constituent of muscle</td>\n",
+       "      <td>2000.0</td>\n",
+       "      <td>26</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>15</th>\n",
+       "      <td>go:0008307</td>\n",
+       "      <td>structural constituent of muscle</td>\n",
+       "      <td>2017.0</td>\n",
+       "      <td>32</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>16</th>\n",
+       "      <td>go:0008307</td>\n",
+       "      <td>structural constituent of muscle</td>\n",
+       "      <td>1996.0</td>\n",
+       "      <td>36</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>17</th>\n",
+       "      <td>go:0008307</td>\n",
+       "      <td>structural constituent of muscle</td>\n",
+       "      <td>2002.0</td>\n",
+       "      <td>56</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>18</th>\n",
+       "      <td>go:0008307</td>\n",
+       "      <td>structural constituent of muscle</td>\n",
+       "      <td>2010.0</td>\n",
+       "      <td>54</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>19</th>\n",
+       "      <td>go:0008307</td>\n",
+       "      <td>structural constituent of muscle</td>\n",
+       "      <td>2004.0</td>\n",
+       "      <td>20</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>20</th>\n",
+       "      <td>go:0008307</td>\n",
+       "      <td>structural constituent of muscle</td>\n",
+       "      <td>2014.0</td>\n",
+       "      <td>47</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>21</th>\n",
+       "      <td>go:0008307</td>\n",
+       "      <td>structural constituent of muscle</td>\n",
+       "      <td>2006.0</td>\n",
+       "      <td>35</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>22</th>\n",
+       "      <td>go:0008307</td>\n",
+       "      <td>structural constituent of muscle</td>\n",
+       "      <td>1997.0</td>\n",
+       "      <td>24</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>23</th>\n",
+       "      <td>go:0008307</td>\n",
+       "      <td>structural constituent of muscle</td>\n",
+       "      <td>2018.0</td>\n",
+       "      <td>14</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>24</th>\n",
+       "      <td>go:0008307</td>\n",
+       "      <td>structural constituent of muscle</td>\n",
+       "      <td>1995.0</td>\n",
+       "      <td>18</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>25</th>\n",
+       "      <td>go:0008307</td>\n",
+       "      <td>structural constituent of muscle</td>\n",
+       "      <td>2001.0</td>\n",
+       "      <td>40</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>26</th>\n",
+       "      <td>go:0008307</td>\n",
+       "      <td>structural constituent of muscle</td>\n",
+       "      <td>2005.0</td>\n",
+       "      <td>60</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>27</th>\n",
+       "      <td>go:0008307</td>\n",
+       "      <td>structural constituent of muscle</td>\n",
+       "      <td>2022.0</td>\n",
+       "      <td>10</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>29</th>\n",
+       "      <td>go:0008307</td>\n",
+       "      <td>structural constituent of muscle</td>\n",
+       "      <td>2023.0</td>\n",
+       "      <td>4</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>30</th>\n",
+       "      <td>go:0008307</td>\n",
+       "      <td>structural constituent of muscle</td>\n",
+       "      <td>1993.0</td>\n",
+       "      <td>1</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "            id                              name    year  count\n",
+       "0   go:0008307  structural constituent of muscle  2020.0    114\n",
+       "1   go:0008307  structural constituent of muscle  2021.0     49\n",
+       "2   go:0008307  structural constituent of muscle  2009.0     86\n",
+       "3   go:0008307  structural constituent of muscle  2007.0     41\n",
+       "4   go:0008307  structural constituent of muscle  2012.0     55\n",
+       "5   go:0008307  structural constituent of muscle  2011.0     66\n",
+       "6   go:0008307  structural constituent of muscle  2013.0     64\n",
+       "7   go:0008307  structural constituent of muscle  2016.0     52\n",
+       "8   go:0008307  structural constituent of muscle  2008.0     25\n",
+       "9   go:0008307  structural constituent of muscle  2015.0     48\n",
+       "10  go:0008307  structural constituent of muscle  2019.0     49\n",
+       "11  go:0008307  structural constituent of muscle  2003.0     40\n",
+       "12  go:0008307  structural constituent of muscle  1998.0     55\n",
+       "13  go:0008307  structural constituent of muscle  1999.0     23\n",
+       "14  go:0008307  structural constituent of muscle  2000.0     26\n",
+       "15  go:0008307  structural constituent of muscle  2017.0     32\n",
+       "16  go:0008307  structural constituent of muscle  1996.0     36\n",
+       "17  go:0008307  structural constituent of muscle  2002.0     56\n",
+       "18  go:0008307  structural constituent of muscle  2010.0     54\n",
+       "19  go:0008307  structural constituent of muscle  2004.0     20\n",
+       "20  go:0008307  structural constituent of muscle  2014.0     47\n",
+       "21  go:0008307  structural constituent of muscle  2006.0     35\n",
+       "22  go:0008307  structural constituent of muscle  1997.0     24\n",
+       "23  go:0008307  structural constituent of muscle  2018.0     14\n",
+       "24  go:0008307  structural constituent of muscle  1995.0     18\n",
+       "25  go:0008307  structural constituent of muscle  2001.0     40\n",
+       "26  go:0008307  structural constituent of muscle  2005.0     60\n",
+       "27  go:0008307  structural constituent of muscle  2022.0     10\n",
+       "29  go:0008307  structural constituent of muscle  2023.0      4\n",
+       "30  go:0008307  structural constituent of muscle  1993.0      1"
+      ]
+     },
+     "execution_count": 7,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "# Version 1 not including isa relationships \n",
+    "def plot_histogram(user):\n",
+    "    \"\"\"\n",
+    "    Parameters\n",
+    "    ----------\n",
+    "    user : string\n",
+    "        This is the id, name or other property the user chooses to enter\n",
+    "\n",
+    "    Returns\n",
+    "    -------\n",
+    "    df : This is the dataframe with the data of a biological process passed in by the user, \n",
+    "         containing the id, name, year and count\n",
+    "        \n",
+    "    \"\"\"\n",
+    "    # saving the cypher into a string\n",
+    "    # associated_with relationship should be extended to a path, to access the go terms that are\n",
+    "    # indirectly associated with it (use is_a relationship)\n",
+    "    cypher = \"\"\"MATCH p1=(process:BioEntity {type:'biological_process',\"\"\" + user + \"\"\"})<-[:associated_with]-(a) \n",
+    "    MATCH p2=(process)<-[:associated_with]-(b) \n",
+    "    MATCH p3=(a)-[r:indra_rel]->(b) \n",
+    "    MATCH p4=(e:Evidence)-[]->(pub:Publication) \n",
+    "    WHERE a.id <> b.id AND e.stmt_hash = r.stmt_hash \n",
+    "    RETURN process.id,process.name,pub.year,count(pub.year)\"\"\"\n",
+    "    \n",
+    "    # using query_tx to get the result\n",
+    "    results = client.query_tx(cypher)\n",
+    "    # creating a dataframe with the necessary data\n",
+    "    df = pd.DataFrame(results, columns=[\"id\", \"name\", \"year\", \"count\"])\n",
+    "    \n",
+    "    # plotting and labeling a bar chart\n",
+    "    # chose to do bar chart instead of a histogram because the frequency was another column \n",
+    "    plt.xlabel(\"Publication Year\")\n",
+    "    plt.ylabel(\"Frequency (# of times Published)\")\n",
+    "    plt.title(df[\"name\"].values[0])\n",
+    "    plt.bar(df[\"year\"],df[\"count\"])\n",
+    "    plt.show()\n",
+    "    \n",
+    "    # this block of code informs the popularity of research of the chosen biological processes \n",
+    "    # dropping null values\n",
+    "    df = df.dropna()\n",
+    "    x = df[\"year\"]\n",
+    "    y = df[\"count\"]\n",
+    "    plt.xlabel(\"Publication Year\")\n",
+    "    plt.ylabel(\"Frequency (# of times Published)\")\n",
+    "    plt.title(df[\"name\"].values[0])\n",
+    "    # plotting a scatterplot \n",
+    "    plt.scatter(x,y)\n",
+    "    # creating a line of best fit\n",
+    "    a, b = np.polyfit(x, y, 1)  \n",
+    "    plt.plot(x, a * x + b)  \n",
+    "    plt.show()\n",
+    "    \n",
+    "    \n",
+    "    # if the slope is greater than 0, there is a positive trend, and if the slope is 0 there is a negative trend\n",
+    "    # one challenge may be that in general as the years go on, the frequency of publications in general\n",
+    "    # increase, so this may be a slightly innaccurate way to go about determining popularity trends\n",
+    "    # to account for this normalize by amount of indra statments per year, \n",
+    "    if a > 0:\n",
+    "        print(\"There is an positive trend in the frequency of publications on \", df[\"name\"].values[0])\n",
+    "    else:\n",
+    "        print(\"There is an decreasing trend in the frequency of publications on \", df[\"name\"].values[0])\n",
+    "        \n",
+    "    return df\n",
+    "\n",
+    "# the user can input the id, name or whichever property they choose \n",
+    "# Three examples with a mix of names and ids being passed in \n",
+    "#user_input = \"id:'go:0008307'\"\n",
+    "user_input = \"name:'structural constituent of muscle'\"\n",
+    "plot_histogram(user_input)\n",
+    "\n",
+    "#user_input = \"name:'regulation of gastrulation'\"\n",
+    "#plot_histogram(user_input)\n",
+    "\n",
+    "#user_input = \"id:'go:0007411'\"\n",
+    "#plot_histogram(user_input)\n",
+    "\n",
+    "#user_input = \"name:'membrane protein ectodomain proteolysis'\"\n",
+    "#plot_histogram(user_input)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "6474233c",
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 21,
+   "id": "b00a9067",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 640x480 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 640x480 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "There is an positive trend in the frequency of publications on  structural constituent of muscle\n"
+     ]
+    },
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>id</th>\n",
+       "      <th>name</th>\n",
+       "      <th>year</th>\n",
+       "      <th>count</th>\n",
+       "      <th>parent_id</th>\n",
+       "      <th>parent_name</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>go:0008307</td>\n",
+       "      <td>structural constituent of muscle</td>\n",
+       "      <td>2009.0</td>\n",
+       "      <td>7998</td>\n",
+       "      <td>go:0005198</td>\n",
+       "      <td>structural molecule activity</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>go:0008307</td>\n",
+       "      <td>structural constituent of muscle</td>\n",
+       "      <td>2020.0</td>\n",
+       "      <td>10602</td>\n",
+       "      <td>go:0005198</td>\n",
+       "      <td>structural molecule activity</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>go:0008307</td>\n",
+       "      <td>structural constituent of muscle</td>\n",
+       "      <td>2021.0</td>\n",
+       "      <td>4557</td>\n",
+       "      <td>go:0005198</td>\n",
+       "      <td>structural molecule activity</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>go:0008307</td>\n",
+       "      <td>structural constituent of muscle</td>\n",
+       "      <td>2013.0</td>\n",
+       "      <td>5952</td>\n",
+       "      <td>go:0005198</td>\n",
+       "      <td>structural molecule activity</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>go:0008307</td>\n",
+       "      <td>structural constituent of muscle</td>\n",
+       "      <td>2012.0</td>\n",
+       "      <td>5115</td>\n",
+       "      <td>go:0005198</td>\n",
+       "      <td>structural molecule activity</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>5</th>\n",
+       "      <td>go:0008307</td>\n",
+       "      <td>structural constituent of muscle</td>\n",
+       "      <td>2014.0</td>\n",
+       "      <td>4371</td>\n",
+       "      <td>go:0005198</td>\n",
+       "      <td>structural molecule activity</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>6</th>\n",
+       "      <td>go:0008307</td>\n",
+       "      <td>structural constituent of muscle</td>\n",
+       "      <td>2002.0</td>\n",
+       "      <td>5208</td>\n",
+       "      <td>go:0005198</td>\n",
+       "      <td>structural molecule activity</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>7</th>\n",
+       "      <td>go:0008307</td>\n",
+       "      <td>structural constituent of muscle</td>\n",
+       "      <td>2006.0</td>\n",
+       "      <td>3255</td>\n",
+       "      <td>go:0005198</td>\n",
+       "      <td>structural molecule activity</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>8</th>\n",
+       "      <td>go:0008307</td>\n",
+       "      <td>structural constituent of muscle</td>\n",
+       "      <td>2015.0</td>\n",
+       "      <td>4464</td>\n",
+       "      <td>go:0005198</td>\n",
+       "      <td>structural molecule activity</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>9</th>\n",
+       "      <td>go:0008307</td>\n",
+       "      <td>structural constituent of muscle</td>\n",
+       "      <td>2007.0</td>\n",
+       "      <td>3813</td>\n",
+       "      <td>go:0005198</td>\n",
+       "      <td>structural molecule activity</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>10</th>\n",
+       "      <td>go:0008307</td>\n",
+       "      <td>structural constituent of muscle</td>\n",
+       "      <td>2010.0</td>\n",
+       "      <td>5022</td>\n",
+       "      <td>go:0005198</td>\n",
+       "      <td>structural molecule activity</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>11</th>\n",
+       "      <td>go:0008307</td>\n",
+       "      <td>structural constituent of muscle</td>\n",
+       "      <td>2019.0</td>\n",
+       "      <td>4557</td>\n",
+       "      <td>go:0005198</td>\n",
+       "      <td>structural molecule activity</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>12</th>\n",
+       "      <td>go:0008307</td>\n",
+       "      <td>structural constituent of muscle</td>\n",
+       "      <td>2011.0</td>\n",
+       "      <td>6138</td>\n",
+       "      <td>go:0005198</td>\n",
+       "      <td>structural molecule activity</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>13</th>\n",
+       "      <td>go:0008307</td>\n",
+       "      <td>structural constituent of muscle</td>\n",
+       "      <td>2016.0</td>\n",
+       "      <td>4836</td>\n",
+       "      <td>go:0005198</td>\n",
+       "      <td>structural molecule activity</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>14</th>\n",
+       "      <td>go:0008307</td>\n",
+       "      <td>structural constituent of muscle</td>\n",
+       "      <td>2003.0</td>\n",
+       "      <td>3720</td>\n",
+       "      <td>go:0005198</td>\n",
+       "      <td>structural molecule activity</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>15</th>\n",
+       "      <td>go:0008307</td>\n",
+       "      <td>structural constituent of muscle</td>\n",
+       "      <td>1995.0</td>\n",
+       "      <td>1674</td>\n",
+       "      <td>go:0005198</td>\n",
+       "      <td>structural molecule activity</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>16</th>\n",
+       "      <td>go:0008307</td>\n",
+       "      <td>structural constituent of muscle</td>\n",
+       "      <td>1996.0</td>\n",
+       "      <td>3348</td>\n",
+       "      <td>go:0005198</td>\n",
+       "      <td>structural molecule activity</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>17</th>\n",
+       "      <td>go:0008307</td>\n",
+       "      <td>structural constituent of muscle</td>\n",
+       "      <td>2005.0</td>\n",
+       "      <td>5580</td>\n",
+       "      <td>go:0005198</td>\n",
+       "      <td>structural molecule activity</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>18</th>\n",
+       "      <td>go:0008307</td>\n",
+       "      <td>structural constituent of muscle</td>\n",
+       "      <td>2004.0</td>\n",
+       "      <td>1860</td>\n",
+       "      <td>go:0005198</td>\n",
+       "      <td>structural molecule activity</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>19</th>\n",
+       "      <td>go:0008307</td>\n",
+       "      <td>structural constituent of muscle</td>\n",
+       "      <td>2017.0</td>\n",
+       "      <td>2976</td>\n",
+       "      <td>go:0005198</td>\n",
+       "      <td>structural molecule activity</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>20</th>\n",
+       "      <td>go:0008307</td>\n",
+       "      <td>structural constituent of muscle</td>\n",
+       "      <td>2008.0</td>\n",
+       "      <td>2325</td>\n",
+       "      <td>go:0005198</td>\n",
+       "      <td>structural molecule activity</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>21</th>\n",
+       "      <td>go:0008307</td>\n",
+       "      <td>structural constituent of muscle</td>\n",
+       "      <td>2001.0</td>\n",
+       "      <td>3720</td>\n",
+       "      <td>go:0005198</td>\n",
+       "      <td>structural molecule activity</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>22</th>\n",
+       "      <td>go:0008307</td>\n",
+       "      <td>structural constituent of muscle</td>\n",
+       "      <td>1998.0</td>\n",
+       "      <td>5115</td>\n",
+       "      <td>go:0005198</td>\n",
+       "      <td>structural molecule activity</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>23</th>\n",
+       "      <td>go:0008307</td>\n",
+       "      <td>structural constituent of muscle</td>\n",
+       "      <td>1999.0</td>\n",
+       "      <td>2139</td>\n",
+       "      <td>go:0005198</td>\n",
+       "      <td>structural molecule activity</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>24</th>\n",
+       "      <td>go:0008307</td>\n",
+       "      <td>structural constituent of muscle</td>\n",
+       "      <td>2000.0</td>\n",
+       "      <td>2418</td>\n",
+       "      <td>go:0005198</td>\n",
+       "      <td>structural molecule activity</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>25</th>\n",
+       "      <td>go:0008307</td>\n",
+       "      <td>structural constituent of muscle</td>\n",
+       "      <td>1993.0</td>\n",
+       "      <td>93</td>\n",
+       "      <td>go:0005198</td>\n",
+       "      <td>structural molecule activity</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>26</th>\n",
+       "      <td>go:0008307</td>\n",
+       "      <td>structural constituent of muscle</td>\n",
+       "      <td>1997.0</td>\n",
+       "      <td>2232</td>\n",
+       "      <td>go:0005198</td>\n",
+       "      <td>structural molecule activity</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>27</th>\n",
+       "      <td>go:0008307</td>\n",
+       "      <td>structural constituent of muscle</td>\n",
+       "      <td>2018.0</td>\n",
+       "      <td>1302</td>\n",
+       "      <td>go:0005198</td>\n",
+       "      <td>structural molecule activity</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>28</th>\n",
+       "      <td>go:0008307</td>\n",
+       "      <td>structural constituent of muscle</td>\n",
+       "      <td>2022.0</td>\n",
+       "      <td>930</td>\n",
+       "      <td>go:0005198</td>\n",
+       "      <td>structural molecule activity</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>30</th>\n",
+       "      <td>go:0008307</td>\n",
+       "      <td>structural constituent of muscle</td>\n",
+       "      <td>2023.0</td>\n",
+       "      <td>372</td>\n",
+       "      <td>go:0005198</td>\n",
+       "      <td>structural molecule activity</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "            id                              name    year  count   parent_id  \\\n",
+       "0   go:0008307  structural constituent of muscle  2009.0   7998  go:0005198   \n",
+       "1   go:0008307  structural constituent of muscle  2020.0  10602  go:0005198   \n",
+       "2   go:0008307  structural constituent of muscle  2021.0   4557  go:0005198   \n",
+       "3   go:0008307  structural constituent of muscle  2013.0   5952  go:0005198   \n",
+       "4   go:0008307  structural constituent of muscle  2012.0   5115  go:0005198   \n",
+       "5   go:0008307  structural constituent of muscle  2014.0   4371  go:0005198   \n",
+       "6   go:0008307  structural constituent of muscle  2002.0   5208  go:0005198   \n",
+       "7   go:0008307  structural constituent of muscle  2006.0   3255  go:0005198   \n",
+       "8   go:0008307  structural constituent of muscle  2015.0   4464  go:0005198   \n",
+       "9   go:0008307  structural constituent of muscle  2007.0   3813  go:0005198   \n",
+       "10  go:0008307  structural constituent of muscle  2010.0   5022  go:0005198   \n",
+       "11  go:0008307  structural constituent of muscle  2019.0   4557  go:0005198   \n",
+       "12  go:0008307  structural constituent of muscle  2011.0   6138  go:0005198   \n",
+       "13  go:0008307  structural constituent of muscle  2016.0   4836  go:0005198   \n",
+       "14  go:0008307  structural constituent of muscle  2003.0   3720  go:0005198   \n",
+       "15  go:0008307  structural constituent of muscle  1995.0   1674  go:0005198   \n",
+       "16  go:0008307  structural constituent of muscle  1996.0   3348  go:0005198   \n",
+       "17  go:0008307  structural constituent of muscle  2005.0   5580  go:0005198   \n",
+       "18  go:0008307  structural constituent of muscle  2004.0   1860  go:0005198   \n",
+       "19  go:0008307  structural constituent of muscle  2017.0   2976  go:0005198   \n",
+       "20  go:0008307  structural constituent of muscle  2008.0   2325  go:0005198   \n",
+       "21  go:0008307  structural constituent of muscle  2001.0   3720  go:0005198   \n",
+       "22  go:0008307  structural constituent of muscle  1998.0   5115  go:0005198   \n",
+       "23  go:0008307  structural constituent of muscle  1999.0   2139  go:0005198   \n",
+       "24  go:0008307  structural constituent of muscle  2000.0   2418  go:0005198   \n",
+       "25  go:0008307  structural constituent of muscle  1993.0     93  go:0005198   \n",
+       "26  go:0008307  structural constituent of muscle  1997.0   2232  go:0005198   \n",
+       "27  go:0008307  structural constituent of muscle  2018.0   1302  go:0005198   \n",
+       "28  go:0008307  structural constituent of muscle  2022.0    930  go:0005198   \n",
+       "30  go:0008307  structural constituent of muscle  2023.0    372  go:0005198   \n",
+       "\n",
+       "                     parent_name  \n",
+       "0   structural molecule activity  \n",
+       "1   structural molecule activity  \n",
+       "2   structural molecule activity  \n",
+       "3   structural molecule activity  \n",
+       "4   structural molecule activity  \n",
+       "5   structural molecule activity  \n",
+       "6   structural molecule activity  \n",
+       "7   structural molecule activity  \n",
+       "8   structural molecule activity  \n",
+       "9   structural molecule activity  \n",
+       "10  structural molecule activity  \n",
+       "11  structural molecule activity  \n",
+       "12  structural molecule activity  \n",
+       "13  structural molecule activity  \n",
+       "14  structural molecule activity  \n",
+       "15  structural molecule activity  \n",
+       "16  structural molecule activity  \n",
+       "17  structural molecule activity  \n",
+       "18  structural molecule activity  \n",
+       "19  structural molecule activity  \n",
+       "20  structural molecule activity  \n",
+       "21  structural molecule activity  \n",
+       "22  structural molecule activity  \n",
+       "23  structural molecule activity  \n",
+       "24  structural molecule activity  \n",
+       "25  structural molecule activity  \n",
+       "26  structural molecule activity  \n",
+       "27  structural molecule activity  \n",
+       "28  structural molecule activity  \n",
+       "30  structural molecule activity  "
+      ]
+     },
+     "execution_count": 21,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "# version 2 including isa relationships \n",
+    "def new_histogram(user):\n",
+    "    \"\"\"\n",
+    "    Parameters\n",
+    "    ----------\n",
+    "    user : string\n",
+    "        This is the id, name or other property the user chooses to enter\n",
+    "\n",
+    "    Returns\n",
+    "    -------\n",
+    "    df : This is the dataframe with the data of a biological process passed in by the user, \n",
+    "         containing the id, name, year and count\n",
+    "        \n",
+    "    \"\"\"\n",
+    "    # saving the cypher into a string\n",
+    "    # associated_with relationship should be extended to a path, to access the go terms that are\n",
+    "    # indirectly associated with it (use is_a relationship)\n",
+    "    cypher = \"\"\"MATCH p1=(process:BioEntity {type:'biological_process', id:'go:0008307'})-[:isa]->(a)\n",
+    "    MATCH p2 = (process)<-[:associated_with]-(c)\n",
+    "    MATCH p3 = (process)<-[:associated_with]-(d)\n",
+    "    MATCH p4=(a)<-[:associated_with]-(b)\n",
+    "    MATCH p5=(a)<-[:associated_with]-(f)\n",
+    "    MATCH p6=(c)-[r:indra_rel]->(d)\n",
+    "    MATCH p7=(b)-[:indra_rel]->(f)\n",
+    "    MATCH p8=(e:Evidence)-[]->(pub:Publication)\n",
+    "    WHERE c.id <> d.id AND b.id<> f.id AND e.stmt_hash = r.stmt_hash\n",
+    "    RETURN process.id,process.name,pub.year,count(pub.year), a.id, a.name\"\"\"\n",
+    "   \n",
+    "    # using query_tx to get the result\n",
+    "    results = client.query_tx(cypher)\n",
+    "    # creating a dataframe with the necessary data\n",
+    "    # im not going to know how many isa relationships there will be, this example happened to have 1 other relationship \n",
+    "    # so I was able to add it in \n",
+    "    df = pd.DataFrame(results, columns=[\"id\", \"name\", \"year\", \"count\", \"parent_id\", \"parent_name\"])\n",
+    "    \n",
+    "    # plotting and labeling a bar chart\n",
+    "    # chose to do bar chart instead of a histogram because the frequency was another column \n",
+    "    plt.xlabel(\"Publication Year\")\n",
+    "    plt.ylabel(\"Frequency (# of times Published)\")\n",
+    "    plt.title(df[\"name\"].values[0] + \" and \" + df[\"parent_name\"].values[0])\n",
+    "    plt.bar(df[\"year\"],df[\"count\"])\n",
+    "    plt.show()\n",
+    "    \n",
+    "    # this block of code informs the popularity of research of the chosen biological processes \n",
+    "    # dropping null values\n",
+    "    df = df.dropna()\n",
+    "    x = df[\"year\"]\n",
+    "    y = df[\"count\"]\n",
+    "    plt.xlabel(\"Publication Year\")\n",
+    "    plt.ylabel(\"Frequency (# of times Published)\")\n",
+    "    plt.title(df[\"name\"].values[0] + \" \" + df[\"parent_name\"].values[0])\n",
+    "    # plotting a scatterplot \n",
+    "    plt.scatter(x,y)\n",
+    "    # creating a line of best fit\n",
+    "    a, b = np.polyfit(x, y, 1)  \n",
+    "    plt.plot(x, a * x + b)  \n",
+    "    plt.show()\n",
+    "    \n",
+    "    \n",
+    "    # if the slope is greater than 0, there is a positive trend, and if the slope is 0 there is a negative trend\n",
+    "    # one challenge may be that in general as the years go on, the frequency of publications in general\n",
+    "    # increase, so this may be a slightly innaccurate way to go about determining popularity trends\n",
+    "    # to account for this normalize by amount of indra statments per year, \n",
+    "    if a > 0:\n",
+    "        print(\"There is an positive trend in the frequency of publications on \", df[\"name\"].values[0])\n",
+    "    else:\n",
+    "        print(\"There is an decreasing trend in the frequency of publications on \", df[\"name\"].values[0])\n",
+    "        \n",
+    "    return df\n",
+    "\n",
+    "# the user can input the id, name or whichever property they choose \n",
+    "# Three examples with a mix of names and ids being passed in \n",
+    "#user_input = \"id:'go:0008307'\"\n",
+    "user_input = \"name:'structural constituent of muscle'\"\n",
+    "new_histogram(user_input)\n",
+    "\n",
+    "#user_input = \"name:'regulation of gastrulation'\"\n",
+    "#plot_histogram(user_input)\n",
+    "\n",
+    "#user_input = \"id:'go:0007411'\"\n",
+    "#plot_histogram(user_input)\n",
+    "\n",
+    "#user_input = \"name:'membrane protein ectodomain proteolysis'\"\n",
+    "#plot_histogram(user_input)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "id": "c307e715",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 640x480 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "# This function attempts to plot a histogram instead of a bar chart using seaborn \n",
+    "# by using the frequency column as the actual frequency \n",
+    "def plot_histogram(user):\n",
+    "    \"\"\"\n",
+    "    Parameters\n",
+    "    ----------\n",
+    "    user : string\n",
+    "        This is the id, name or other property the user chooses to enter\n",
+    "\n",
+    "    Returns\n",
+    "    -------\n",
+    "    none\n",
+    "        \n",
+    "    \"\"\"\n",
+    "    cypher = \"MATCH p1=(process:BioEntity {type:'biological_process',\" + user + \"})<-[:associated_with]-(a) MATCH p2=(process)<-[:associated_with]-(b) MATCH p3=(a)-[r:indra_rel]->(b) MATCH p4=(e:Evidence)-[]->(pub:Publication) WHERE a.id <> b.id AND e.stmt_hash = r.stmt_hash RETURN process.id,process.name,pub.year,count(pub.year)\"\n",
+    "    results = client.query_tx(cypher)\n",
+    "    df = pd.DataFrame(results, columns=[\"id\", \"name\", \"year\", \"count\"])\n",
+    "    sns.histplot(x = df[\"year\"],y = df[\"count\"])\n",
+    "    plt.xlabel(\"Publication Year\")\n",
+    "    plt.ylabel(\"Frequency (# of times Published)\")\n",
+    "    plt.title(df[\"name\"].values[0])\n",
+    "    plt.show()\n",
+    "    \n",
+    "#user_input = \"id:'go:0008307'\"\n",
+    "user_input = \"name:'structural constituent of muscle'\"\n",
+    "plot_histogram(user_input)\n",
+    "\n",
+    "# Histogram looks odd as it is not continuous \n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "91ceae88",
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "096a9397-31cd-4ae1-a7eb-1ae22ef1f623",
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3 (ipykernel)",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.10.13"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 5
+}