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+{
+ "cells": [
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "id": "6a60ccd0-ba9e-4406-a0e2-370d930af671",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "INFO: [2024-06-06 14:59:24] numexpr.utils - NumExpr defaulting to 8 threads.\n",
+      "INFO: [2024-06-06 14:59:25] indra_cogex.client.neo4j_client - Using configured URL for INDRA neo4j connection\n",
+      "INFO: [2024-06-06 14:59:25] 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",
+    "client = Neo4jClient()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "ee760a68-b717-4d4d-9068-88bc056793de",
+   "metadata": {},
+   "source": [
+    "## **Create a Histogram to Find the Top 20 Journals Researching Cancer**"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "22561c44-0e63-4ba7-8992-c8db7f7dea1b",
+   "metadata": {},
+   "source": [
+    "This code finds the top 20 journals researching lung cancer by finding the ratio of publications mentioning lung cancer to total publications for each journal, and creating a histogram to visualize it. I selected the second journal because it overall had more publications as compared to the first result despite having a slightly lower ratio."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "id": "913c9451-b3e4-4a93-a98c-de939529573e",
+   "metadata": {},
+   "outputs": [
+    {
+     "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>name</th>\n",
+       "      <th>id</th>\n",
+       "      <th>Total Pub</th>\n",
+       "      <th>Lung Pub</th>\n",
+       "      <th>Ratio</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>Clinical Lung Cancer</td>\n",
+       "      <td>nlm:100893225</td>\n",
+       "      <td>2169</td>\n",
+       "      <td>2043</td>\n",
+       "      <td>94</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>Lung Cancer</td>\n",
+       "      <td>nlm:8800805</td>\n",
+       "      <td>6772</td>\n",
+       "      <td>6302</td>\n",
+       "      <td>93</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>Journal of Thoracic Oncology</td>\n",
+       "      <td>nlm:101274235</td>\n",
+       "      <td>5522</td>\n",
+       "      <td>4843</td>\n",
+       "      <td>87</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>Zhongguo fei ai za zhi = Chinese journal of lu...</td>\n",
+       "      <td>nlm:101126433</td>\n",
+       "      <td>1995</td>\n",
+       "      <td>1707</td>\n",
+       "      <td>85</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>Thoracic Cancer</td>\n",
+       "      <td>nlm:101531441</td>\n",
+       "      <td>2486</td>\n",
+       "      <td>1686</td>\n",
+       "      <td>67</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>5</th>\n",
+       "      <td>Kyobu geka. The Japanese journal of thoracic s...</td>\n",
+       "      <td>nlm:0413533</td>\n",
+       "      <td>12377</td>\n",
+       "      <td>1788</td>\n",
+       "      <td>14</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>6</th>\n",
+       "      <td>Gan to kagaku ryoho. Cancer &amp; chemotherapy</td>\n",
+       "      <td>nlm:7810034</td>\n",
+       "      <td>21263</td>\n",
+       "      <td>2211</td>\n",
+       "      <td>10</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>7</th>\n",
+       "      <td>Journal of Clinical Oncology</td>\n",
+       "      <td>nlm:8309333</td>\n",
+       "      <td>25992</td>\n",
+       "      <td>2454</td>\n",
+       "      <td>9</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>8</th>\n",
+       "      <td>International Journal of Radiation Oncology Bi...</td>\n",
+       "      <td>nlm:7603616</td>\n",
+       "      <td>24320</td>\n",
+       "      <td>2276</td>\n",
+       "      <td>9</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>9</th>\n",
+       "      <td>Anticancer research</td>\n",
+       "      <td>nlm:8102988</td>\n",
+       "      <td>24442</td>\n",
+       "      <td>2218</td>\n",
+       "      <td>9</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>10</th>\n",
+       "      <td>Clinical Cancer Research</td>\n",
+       "      <td>nlm:9502500</td>\n",
+       "      <td>20263</td>\n",
+       "      <td>1887</td>\n",
+       "      <td>9</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>11</th>\n",
+       "      <td>The Annals of Thoracic Surgery</td>\n",
+       "      <td>nlm:15030100R</td>\n",
+       "      <td>39278</td>\n",
+       "      <td>3314</td>\n",
+       "      <td>8</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>12</th>\n",
+       "      <td>British Journal of Cancer</td>\n",
+       "      <td>nlm:0370635</td>\n",
+       "      <td>24852</td>\n",
+       "      <td>2031</td>\n",
+       "      <td>8</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>13</th>\n",
+       "      <td>Cancer Research</td>\n",
+       "      <td>nlm:2984705R</td>\n",
+       "      <td>53303</td>\n",
+       "      <td>3832</td>\n",
+       "      <td>7</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>14</th>\n",
+       "      <td>Cancer</td>\n",
+       "      <td>nlm:0374236</td>\n",
+       "      <td>43649</td>\n",
+       "      <td>3414</td>\n",
+       "      <td>7</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>15</th>\n",
+       "      <td>Chest</td>\n",
+       "      <td>nlm:0231335</td>\n",
+       "      <td>36945</td>\n",
+       "      <td>2917</td>\n",
+       "      <td>7</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>16</th>\n",
+       "      <td>International Journal of Cancer</td>\n",
+       "      <td>nlm:0042124</td>\n",
+       "      <td>26286</td>\n",
+       "      <td>1925</td>\n",
+       "      <td>7</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>17</th>\n",
+       "      <td>Journal of the National Cancer Institute</td>\n",
+       "      <td>nlm:7503089</td>\n",
+       "      <td>22744</td>\n",
+       "      <td>1673</td>\n",
+       "      <td>7</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>18</th>\n",
+       "      <td>The Journal of Thoracic and Cardiovascular Sur...</td>\n",
+       "      <td>nlm:0376343</td>\n",
+       "      <td>30956</td>\n",
+       "      <td>2131</td>\n",
+       "      <td>6</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>19</th>\n",
+       "      <td>PLOS One</td>\n",
+       "      <td>nlm:101285081</td>\n",
+       "      <td>278162</td>\n",
+       "      <td>2346</td>\n",
+       "      <td>0</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "                                                 name             id  \\\n",
+       "0                                Clinical Lung Cancer  nlm:100893225   \n",
+       "1                                         Lung Cancer    nlm:8800805   \n",
+       "2                        Journal of Thoracic Oncology  nlm:101274235   \n",
+       "3   Zhongguo fei ai za zhi = Chinese journal of lu...  nlm:101126433   \n",
+       "4                                     Thoracic Cancer  nlm:101531441   \n",
+       "5   Kyobu geka. The Japanese journal of thoracic s...    nlm:0413533   \n",
+       "6          Gan to kagaku ryoho. Cancer & chemotherapy    nlm:7810034   \n",
+       "7                        Journal of Clinical Oncology    nlm:8309333   \n",
+       "8   International Journal of Radiation Oncology Bi...    nlm:7603616   \n",
+       "9                                 Anticancer research    nlm:8102988   \n",
+       "10                           Clinical Cancer Research    nlm:9502500   \n",
+       "11                     The Annals of Thoracic Surgery  nlm:15030100R   \n",
+       "12                          British Journal of Cancer    nlm:0370635   \n",
+       "13                                    Cancer Research   nlm:2984705R   \n",
+       "14                                             Cancer    nlm:0374236   \n",
+       "15                                              Chest    nlm:0231335   \n",
+       "16                    International Journal of Cancer    nlm:0042124   \n",
+       "17           Journal of the National Cancer Institute    nlm:7503089   \n",
+       "18  The Journal of Thoracic and Cardiovascular Sur...    nlm:0376343   \n",
+       "19                                           PLOS One  nlm:101285081   \n",
+       "\n",
+       "    Total Pub  Lung Pub  Ratio  \n",
+       "0        2169      2043     94  \n",
+       "1        6772      6302     93  \n",
+       "2        5522      4843     87  \n",
+       "3        1995      1707     85  \n",
+       "4        2486      1686     67  \n",
+       "5       12377      1788     14  \n",
+       "6       21263      2211     10  \n",
+       "7       25992      2454      9  \n",
+       "8       24320      2276      9  \n",
+       "9       24442      2218      9  \n",
+       "10      20263      1887      9  \n",
+       "11      39278      3314      8  \n",
+       "12      24852      2031      8  \n",
+       "13      53303      3832      7  \n",
+       "14      43649      3414      7  \n",
+       "15      36945      2917      7  \n",
+       "16      26286      1925      7  \n",
+       "17      22744      1673      7  \n",
+       "18      30956      2131      6  \n",
+       "19     278162      2346      0  "
+      ]
+     },
+     "execution_count": 3,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "def get_df2():\n",
+    "\n",
+    "    # cypher query to get the journal, name and publications\n",
+    "    \n",
+    "    # pubmed entries w mesh annotations, rel: annotated_with, find the mesh term for lung cancer and look up the pubmed ids that are \n",
+    "    # annotated w that term and look for the journals \n",
+    "    # could calculate the ratio, like number of pubs about this disease/total#pubs to get who is publishing the most -> normalize the count\n",
+    "    \n",
+    "    cypher = \"\"\"MATCH (n:BioEntity)<-[:annotated_with]-(pub:Publication)-[:published_in]->(j:Journal)\n",
+    "    WHERE n.id = \"mesh:D008175\"\n",
+    "    WITH j, count (distinct pub) as LungPubs ORDER BY count (distinct pub) desc LIMIT 20\n",
+    "    MATCH (n1:BioEntity)<-[:annotated_with]-(pub1:Publication)-[:published_in]->(j:Journal)\n",
+    "    RETURN j.name, j.id, count(distinct pub1) as AllPubs, LungPubs, LungPubs*100/count(distinct pub1) as Ratio \n",
+    "    ORDER BY LungPubs*100/count(distinct pub1) DESC \"\"\"\n",
+    "\n",
+    "    results = client.query_tx(cypher)\n",
+    "    # loads the results into a dataframe \n",
+    "    df = pd.DataFrame(results, columns=[\"name\", \"id\", \"Total Pub\", \"Lung Pub\", \"Ratio\"])\n",
+    "    \n",
+    "    return df\n",
+    "    \n",
+    "df = get_df2()\n",
+    "df\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "id": "1b7fbf7c-a699-4ce9-b98b-39fa25b2119b",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 800x800 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "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>index</th>\n",
+       "      <th>name</th>\n",
+       "      <th>id</th>\n",
+       "      <th>Total Pub</th>\n",
+       "      <th>Lung Pub</th>\n",
+       "      <th>Ratio</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>0</td>\n",
+       "      <td>Clinical Lung Cancer</td>\n",
+       "      <td>nlm:100893225</td>\n",
+       "      <td>2169</td>\n",
+       "      <td>2043</td>\n",
+       "      <td>94</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>1</td>\n",
+       "      <td>Lung Cancer</td>\n",
+       "      <td>nlm:8800805</td>\n",
+       "      <td>6772</td>\n",
+       "      <td>6302</td>\n",
+       "      <td>93</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>2</td>\n",
+       "      <td>Journal of Thoracic Oncology</td>\n",
+       "      <td>nlm:101274235</td>\n",
+       "      <td>5522</td>\n",
+       "      <td>4843</td>\n",
+       "      <td>87</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>3</td>\n",
+       "      <td>Zhongguo fei ai za zhi = Chinese journal of lu...</td>\n",
+       "      <td>nlm:101126433</td>\n",
+       "      <td>1995</td>\n",
+       "      <td>1707</td>\n",
+       "      <td>85</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>4</td>\n",
+       "      <td>Thoracic Cancer</td>\n",
+       "      <td>nlm:101531441</td>\n",
+       "      <td>2486</td>\n",
+       "      <td>1686</td>\n",
+       "      <td>67</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>5</th>\n",
+       "      <td>5</td>\n",
+       "      <td>Kyobu geka. The Japanese journal of thoracic s...</td>\n",
+       "      <td>nlm:0413533</td>\n",
+       "      <td>12377</td>\n",
+       "      <td>1788</td>\n",
+       "      <td>14</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>6</th>\n",
+       "      <td>6</td>\n",
+       "      <td>Gan to kagaku ryoho. Cancer &amp; chemotherapy</td>\n",
+       "      <td>nlm:7810034</td>\n",
+       "      <td>21263</td>\n",
+       "      <td>2211</td>\n",
+       "      <td>10</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>7</th>\n",
+       "      <td>9</td>\n",
+       "      <td>Anticancer research</td>\n",
+       "      <td>nlm:8102988</td>\n",
+       "      <td>24442</td>\n",
+       "      <td>2218</td>\n",
+       "      <td>9</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>8</th>\n",
+       "      <td>10</td>\n",
+       "      <td>Clinical Cancer Research</td>\n",
+       "      <td>nlm:9502500</td>\n",
+       "      <td>20263</td>\n",
+       "      <td>1887</td>\n",
+       "      <td>9</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>9</th>\n",
+       "      <td>8</td>\n",
+       "      <td>International Journal of Radiation Oncology Bi...</td>\n",
+       "      <td>nlm:7603616</td>\n",
+       "      <td>24320</td>\n",
+       "      <td>2276</td>\n",
+       "      <td>9</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>10</th>\n",
+       "      <td>7</td>\n",
+       "      <td>Journal of Clinical Oncology</td>\n",
+       "      <td>nlm:8309333</td>\n",
+       "      <td>25992</td>\n",
+       "      <td>2454</td>\n",
+       "      <td>9</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>11</th>\n",
+       "      <td>11</td>\n",
+       "      <td>The Annals of Thoracic Surgery</td>\n",
+       "      <td>nlm:15030100R</td>\n",
+       "      <td>39278</td>\n",
+       "      <td>3314</td>\n",
+       "      <td>8</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>12</th>\n",
+       "      <td>12</td>\n",
+       "      <td>British Journal of Cancer</td>\n",
+       "      <td>nlm:0370635</td>\n",
+       "      <td>24852</td>\n",
+       "      <td>2031</td>\n",
+       "      <td>8</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>13</th>\n",
+       "      <td>13</td>\n",
+       "      <td>Cancer Research</td>\n",
+       "      <td>nlm:2984705R</td>\n",
+       "      <td>53303</td>\n",
+       "      <td>3832</td>\n",
+       "      <td>7</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>14</th>\n",
+       "      <td>14</td>\n",
+       "      <td>Cancer</td>\n",
+       "      <td>nlm:0374236</td>\n",
+       "      <td>43649</td>\n",
+       "      <td>3414</td>\n",
+       "      <td>7</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>15</th>\n",
+       "      <td>15</td>\n",
+       "      <td>Chest</td>\n",
+       "      <td>nlm:0231335</td>\n",
+       "      <td>36945</td>\n",
+       "      <td>2917</td>\n",
+       "      <td>7</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>16</th>\n",
+       "      <td>16</td>\n",
+       "      <td>International Journal of Cancer</td>\n",
+       "      <td>nlm:0042124</td>\n",
+       "      <td>26286</td>\n",
+       "      <td>1925</td>\n",
+       "      <td>7</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>17</th>\n",
+       "      <td>17</td>\n",
+       "      <td>Journal of the National Cancer Institute</td>\n",
+       "      <td>nlm:7503089</td>\n",
+       "      <td>22744</td>\n",
+       "      <td>1673</td>\n",
+       "      <td>7</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>18</th>\n",
+       "      <td>18</td>\n",
+       "      <td>The Journal of Thoracic and Cardiovascular Sur...</td>\n",
+       "      <td>nlm:0376343</td>\n",
+       "      <td>30956</td>\n",
+       "      <td>2131</td>\n",
+       "      <td>6</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>19</th>\n",
+       "      <td>19</td>\n",
+       "      <td>PLOS One</td>\n",
+       "      <td>nlm:101285081</td>\n",
+       "      <td>278162</td>\n",
+       "      <td>2346</td>\n",
+       "      <td>0</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "    index                                               name             id  \\\n",
+       "0       0                               Clinical Lung Cancer  nlm:100893225   \n",
+       "1       1                                        Lung Cancer    nlm:8800805   \n",
+       "2       2                       Journal of Thoracic Oncology  nlm:101274235   \n",
+       "3       3  Zhongguo fei ai za zhi = Chinese journal of lu...  nlm:101126433   \n",
+       "4       4                                    Thoracic Cancer  nlm:101531441   \n",
+       "5       5  Kyobu geka. The Japanese journal of thoracic s...    nlm:0413533   \n",
+       "6       6         Gan to kagaku ryoho. Cancer & chemotherapy    nlm:7810034   \n",
+       "7       9                                Anticancer research    nlm:8102988   \n",
+       "8      10                           Clinical Cancer Research    nlm:9502500   \n",
+       "9       8  International Journal of Radiation Oncology Bi...    nlm:7603616   \n",
+       "10      7                       Journal of Clinical Oncology    nlm:8309333   \n",
+       "11     11                     The Annals of Thoracic Surgery  nlm:15030100R   \n",
+       "12     12                          British Journal of Cancer    nlm:0370635   \n",
+       "13     13                                    Cancer Research   nlm:2984705R   \n",
+       "14     14                                             Cancer    nlm:0374236   \n",
+       "15     15                                              Chest    nlm:0231335   \n",
+       "16     16                    International Journal of Cancer    nlm:0042124   \n",
+       "17     17           Journal of the National Cancer Institute    nlm:7503089   \n",
+       "18     18  The Journal of Thoracic and Cardiovascular Sur...    nlm:0376343   \n",
+       "19     19                                           PLOS One  nlm:101285081   \n",
+       "\n",
+       "    Total Pub  Lung Pub  Ratio  \n",
+       "0        2169      2043     94  \n",
+       "1        6772      6302     93  \n",
+       "2        5522      4843     87  \n",
+       "3        1995      1707     85  \n",
+       "4        2486      1686     67  \n",
+       "5       12377      1788     14  \n",
+       "6       21263      2211     10  \n",
+       "7       24442      2218      9  \n",
+       "8       20263      1887      9  \n",
+       "9       24320      2276      9  \n",
+       "10      25992      2454      9  \n",
+       "11      39278      3314      8  \n",
+       "12      24852      2031      8  \n",
+       "13      53303      3832      7  \n",
+       "14      43649      3414      7  \n",
+       "15      36945      2917      7  \n",
+       "16      26286      1925      7  \n",
+       "17      22744      1673      7  \n",
+       "18      30956      2131      6  \n",
+       "19     278162      2346      0  "
+      ]
+     },
+     "execution_count": 5,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "# sorts the dataframe to get the top publications\n",
+    "df = df.sort_values(by = \"Ratio\", ascending = False)\n",
+    "df = df.dropna(axis = 0)\n",
+    "df = df.reset_index()\n",
+    "\n",
+    "# plots a histogram of the top 30 publications \n",
+    "def plot_histogram2():\n",
+    "    plt.figure(figsize=(8, 8))\n",
+    "    plt.title(\"Top 16 Journals Writing About Lung Cancer\")\n",
+    "    plt.bar(df[\"name\"].values, df[\"Ratio\"].values)\n",
+    "    plt.xlabel(\"Journal Names\")\n",
+    "    plt.ylabel(\"Frequency\")\n",
+    "    plt.xticks(rotation=90)\n",
+    "    plt.show()\n",
+    "    \n",
+    "plot_histogram2()\n",
+    "df"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "04f8da63-32ce-4ade-a8bf-efe002e38767",
+   "metadata": {},
+   "source": [
+    "## **Finding Novel Genes**\n",
+    "**This code finds all of the genes relating to lung cancer in the database, and all the genes in the journal \"Lung Cancer: Journal of the International Association for the Study of Lung Cancer\", and finds the genes listed in the database that are not mentioned in the journal, the genes mentioned in the journal that are not listed in the database, and the common genes. Based off the genes listed in the database that were not mentioned in the journal, I manually looked up the genes and saw how many PubMed articles linked that specific gene to lung cancer, to identify genes that could be further studied to explore their correlation to lung cancer.**"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 12,
+   "id": "0f8efb86-2e03-4f98-8ad5-9c50794aade0",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "def get_gene_lists():\n",
+    "    \n",
+    "    # cyphers to get genes from the journal and database relating to lung cancer\n",
+    "    journal_cypher = '''MATCH p3= (gene1:BioEntity)-[r:indra_rel]->(gene2:BioEntity) \n",
+    "    MATCH p4=(e:Evidence)-[:has_citation]->(pub:Publication)-[]->(j:Journal) \n",
+    "    WHERE gene1.id <> gene2.id AND e.stmt_hash = r.stmt_hash AND j.id='nlm:8800805' unwind [gene1, gene2] as gene \n",
+    "    RETURN gene.name, gene.id, count(r)\n",
+    "    ORDER BY count(r) desc'''\n",
+    "    database_cypher = \"\"\"MATCH p1=(gene1)-[:gene_disease_association]->(subdisease)-[:isa*0..]->(disease)\n",
+    "        WHERE disease.id in [\"mesh:D008175\",'doid:3905']\n",
+    "        RETURN distinct gene1.name, gene1.id\"\"\"\n",
+    "    \n",
+    "    journal_results = client.query_tx(journal_cypher)\n",
+    "    database_results = client.query_tx(database_cypher)\n",
+    "\n",
+    "    # loads the results into 2 dataframes\n",
+    "    journal_df = pd.DataFrame(journal_results, columns=[\"name\", \"gene_id\", \"indra statements\"])\n",
+    "    database_df = pd.DataFrame(database_results, columns=[\"name\", \"gene_id\"])\n",
+    "\n",
+    "    return journal_df, database_df\n",
+    "    \n",
+    "journal_df, database_df = get_gene_lists()\n",
+    "\n",
+    "# for the journal, filters for genes starting with hgnc to match with the database\n",
+    "journal_df = journal_df[journal_df[\"gene_id\"].str.startswith(\"hgnc\")]\n",
+    "novel_df = journal_df[~journal_df[\"gene_id\"].isin(database_df[\"gene_id\"])]\n",
+    "\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 14,
+   "id": "1ed82441-71cd-47e5-b1ff-a16120f3f4b6",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 1000x600 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "plt.figure(figsize=(10, 6))\n",
+    "plt.bar(novel_df[\"name\"].head(50), novel_df[\"indra statements\"].head(50))\n",
+    "plt.xlabel(\"Gene Name\")\n",
+    "plt.ylabel(\"Number of Indra Statements\")\n",
+    "plt.title(\"Top 50 Database-Absent Genes Reported in Journal\")\n",
+    "plt.xticks(rotation=90)\n",
+    "plt.show()\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 15,
+   "id": "47082e3d-e440-4015-8043-2ce8371ca69d",
+   "metadata": {},
+   "outputs": [
+    {
+     "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>name</th>\n",
+       "      <th>gene_id</th>\n",
+       "      <th>indra statements</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>18</th>\n",
+       "      <td>MUC16</td>\n",
+       "      <td>hgnc:15582</td>\n",
+       "      <td>89</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>22</th>\n",
+       "      <td>CDH1</td>\n",
+       "      <td>hgnc:1748</td>\n",
+       "      <td>75</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>34</th>\n",
+       "      <td>PDCD1</td>\n",
+       "      <td>hgnc:8760</td>\n",
+       "      <td>51</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>40</th>\n",
+       "      <td>SRC</td>\n",
+       "      <td>hgnc:11283</td>\n",
+       "      <td>46</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>41</th>\n",
+       "      <td>TACC3</td>\n",
+       "      <td>hgnc:11524</td>\n",
+       "      <td>46</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>43</th>\n",
+       "      <td>PPARG</td>\n",
+       "      <td>hgnc:9236</td>\n",
+       "      <td>46</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>49</th>\n",
+       "      <td>EGF</td>\n",
+       "      <td>hgnc:3229</td>\n",
+       "      <td>40</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>50</th>\n",
+       "      <td>CISH</td>\n",
+       "      <td>hgnc:1984</td>\n",
+       "      <td>40</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>58</th>\n",
+       "      <td>FGF2</td>\n",
+       "      <td>hgnc:3676</td>\n",
+       "      <td>37</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>59</th>\n",
+       "      <td>BAX</td>\n",
+       "      <td>hgnc:959</td>\n",
+       "      <td>37</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "     name     gene_id  indra statements\n",
+       "18  MUC16  hgnc:15582                89\n",
+       "22   CDH1   hgnc:1748                75\n",
+       "34  PDCD1   hgnc:8760                51\n",
+       "40    SRC  hgnc:11283                46\n",
+       "41  TACC3  hgnc:11524                46\n",
+       "43  PPARG   hgnc:9236                46\n",
+       "49    EGF   hgnc:3229                40\n",
+       "50   CISH   hgnc:1984                40\n",
+       "58   FGF2   hgnc:3676                37\n",
+       "59    BAX    hgnc:959                37"
+      ]
+     },
+     "execution_count": 15,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "novel_df.head(10)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "3a1ceedb-ebb3-45a0-b762-581e38541972",
+   "metadata": {},
+   "source": [
+    "## Display INDRA Statements"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "a3b8aa1c-959d-4d06-9113-e7283a70caba",
+   "metadata": {},
+   "source": [
+    "Wrote a cypher query to get the indra statements for a given gene, then use indra.assemblers.html to create an html page with the INDRA statements"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 21,
+   "id": "e9c49116-95c5-4ca3-b433-55bfe2156ae6",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from indra.assemblers.html import HtmlAssembler\n",
+    "import json\n",
+    "from indra.statements import *"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 19,
+   "id": "20c86f28",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "def get_statements():\n",
+    "    # gets a list of the top 6 gene ids and names \n",
+    "    gene_ids = novel_df.head(6)[\"gene_id\"]\n",
+    "    gene_names = novel_df.head(6)[\"name\"]\n",
+    "    \n",
+    "    # initializes empty lists\n",
+    "    string_list =  []\n",
+    "    df_list = []\n",
+    "\n",
+    "    # for loop to iterate through each gene \n",
+    "    for id, name in zip(gene_ids, gene_names):\n",
+    "        # cypher to get the indra statements, uses f-string formatting for query to efficently test each gene\n",
+    "        indra_cypher = f\"MATCH p3= (gene1:BioEntity)-[r:indra_rel]->(gene2:BioEntity) MATCH p4=(e:Evidence)-[:has_citation]->(pub:Publication)-[]->(j:Journal) WHERE gene1.id <> gene2.id AND e.stmt_hash = r.stmt_hash AND j.id='nlm:8800805' AND (gene1.id = '{id}' OR gene2.id ='{id}') unwind [gene1, gene2] as gene RETURN gene.name, gene.id, r.stmt_json\"\n",
+    "        indra_results = client.query_tx(indra_cypher)\n",
+    "        \n",
+    "        # saves result in a dataframe that contains the name, id and indra statements in string json form\n",
+    "        indra_df = pd.DataFrame(indra_results, columns=[\"name\", \"gene_id\", \"indra statements\"])\n",
+    "        # the query returns \"double\" the results due to how it was written, so the dataframe needs to be filtered for the correct gene \n",
+    "        filtered_indra = indra_df[indra_df[\"name\"] == name]\n",
+    "        filtered_indra = filtered_indra.reset_index()\n",
+    "\n",
+    "        # returns a list of data frames and a nested list of indra statements as json strings \n",
+    "        df_list.append(filtered_indra)\n",
+    "        json_strings = list(filtered_indra[\"indra statements\"].values)\n",
+    "        string_list.append(json_strings)\n",
+    "        \n",
+    "    return df_list, string_list, gene_names\n",
+    "df_list, string_list, gene_names = get_statements()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 22,
+   "id": "bdb4c766",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "MUC16\n",
+      "CDH1\n",
+      "PDCD1\n",
+      "SRC\n",
+      "TACC3\n",
+      "PPARG\n"
+     ]
+    }
+   ],
+   "source": [
+    "# iterates through the gene name and json strings for each gene \n",
+    "for genes, json_strings in zip(gene_names, string_list):\n",
+    "    stmt_jsons = []\n",
+    "    # iterates through the individual json string within the statements for each gene \n",
+    "    # and converts it to an INDRA statement object\n",
+    "    for stmt_json_string in json_strings:\n",
+    "        stmt_jsons.append(json.loads(stmt_json_string))\n",
+    "    stmts = stmts_from_json(json_in=stmt_jsons)\n",
+    "\n",
+    "    # uses HtmlAssembler to get html pages of INDRA statements for each gene \n",
+    "    ha = HtmlAssembler(stmts, title='Statements for %s' % genes, db_rest_url='https://db.indra.bio')\n",
+    "    ha.save_model('%s_statements.html' % genes)\n",
+    "    print(genes)"
+   ]
+  }
+ ],
+ "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
+}