notebooks/bte_clustering.ipynb

{
 "cells": [
  {
   "cell_type": "raw",
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "UZZqPBS0UppM",
    "outputId": "281abfa6-b935-4d99-a267-e237900aa5dc"
   },
   "source": [
    "!pip install numpy pandas requests requests_cache SetSimilaritySearch"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "id": "uG7JfetZjcXH"
   },
   "outputs": [],
   "source": [
    "from copy import copy,deepcopy\n",
    "from itertools import combinations\n",
    "import json\n",
    "import textwrap\n",
    "\n",
    "\n",
    "from fisher import pvalue_npy\n",
    "import matplotlib.pyplot as plt\n",
    "import networkx as nx\n",
    "import pandas as pd"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "id": "rzoUUSDeVz0a"
   },
   "outputs": [],
   "source": [
    "import requests\n",
    "import requests_cache\n",
    "\n",
    "\n",
    "requests_cache.install_cache(\n",
    "    \"pfocr_cache\",\n",
    "    allowable_methods=['GET', 'POST'],\n",
    "    stale_if_error=False,\n",
    ")\n",
    "#requests_cache.remove_expired_responses()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "j9DiSC9NU_Fa"
   },
   "source": [
    "# BTE: Cluster TRAPI Results by Selected PFOCR Figures\n",
    "\n",
    "From discussion with Andrew, we want to first identify a set of figures that can serve to cluster the TRAPI results. We can call these the \"cluster figures\".\n",
    "\n",
    "**Identify Cluster Figures**\n",
    "\n",
    "We identify the top cluster figures by iteratively running Fisher's Exact Test to identify PFOCR figures that are most similar to all the CURIEs from the BTE TRAPI results. To get each cluster figure, we do the following:\n",
    "\n",
    "1. Run Fisher's Exact Test for the CURIEs from each figure vs. the set of the unique CURIEs found from all the TRAPI results, limiting each  set of CURIEs to those with categories found in both PFOCR and BTE TRAPI, e.g., \"disease\" is found in PFOCR and \"biolink:Disease\" is found in BTE TRAPI\n",
    "2. Select the figure with the lowest p-value as a cluster figure\n",
    "3. Exclude that figure and the CURIEs in it\n",
    "4. Repeat to get the next cluster figure, continuing until:\n",
    "    - we run out of BTE TRAPI results\n",
    "    - we run out of PFOCR figures\n",
    "    - we've repeated steps 1-3 a certain number of times (currently limited to 15)\n",
    "\n",
    "**Cluster BTE TRAPI Results by Selected PFOCR Figures (Cluster Figures)**\n",
    "\n",
    "Once we have a set of cluster figures, we use them to cluster the BTE TRAPI results. We do this by running Fisher's Exact Test to identify which Cluster Figure each BTE TRAPI result is most similar to. We don't actually cluster the TRAPI results by figure, however, because if a result is highly relevant to two figures, it can show up in both. It's more accurate to think of the figure as being a different view of the TRAPI results.\n",
    "\n",
    "**View TRAPI Results by PFOCR Figure**\n",
    "\n",
    "With BTE TRAPI results \"clustered\" (not actually clustered, because we show the same result multiple times for different figures if it's closely related to both) by PFOCR figure, we display the top figures, each with their most closely related BTE TRAPI results."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "TxW41muraAFL"
   },
   "source": [
    "## Get BTE TRAPI Results\n",
    "\n",
    "imatinib (MESH:D000068877) - Gene - leukemia (MESH:D007938)\n",
    "\n",
    "vs.\n",
    "\n",
    "imatinib (MESH:D000068877) - Gene - asthma (MESH:D001249)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "N71NYHWeVCj9",
    "outputId": "d8c2c9ae-9750-455c-ff61-b357b4ff0b2d",
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "200\n",
      "got response from cache\n"
     ]
    }
   ],
   "source": [
    "query = {\n",
    "    \"message\": {\n",
    "        \"query_graph\": {\n",
    "            \"edges\": {\n",
    "                \"e0\": {\n",
    "                    \"subject\": \"n0\",\n",
    "                    \"object\": \"n1\",\n",
    "                    \"predicates\": [\n",
    "                        \"biolink:related_to\"\n",
    "                    ]\n",
    "                },\n",
    "                \"e1\": {\n",
    "                    \"subject\": \"n1\",\n",
    "                    \"object\": \"n2\",\n",
    "                    \"predicates\": [\n",
    "                        \"biolink:related_to\"\n",
    "                    ]\n",
    "                }\n",
    "            },\n",
    "            \"nodes\": {\n",
    "                \"n0\": {\n",
    "                    \"ids\": [\n",
    "                        \"MESH:D000068877\"\n",
    "                    ],\n",
    "                    \"categories\": [\n",
    "                        \"biolink:Drug\"\n",
    "                    ]\n",
    "                },\n",
    "                \"n1\": {\n",
    "                    \"categories\": [\n",
    "                        \"biolink:Gene\"\n",
    "                    ],\n",
    "                },\n",
    "                \"n2\": {\n",
    "                    \"ids\": [\n",
    "                        \"MESH:D007938\",\n",
    "                        \"MESH:D001249\",\n",
    "                    ],\n",
    "                    \"categories\": [\n",
    "                        \"biolink:Disease\"\n",
    "                    ]\n",
    "                }\n",
    "            }\n",
    "        }\n",
    "    }\n",
    "}\n",
    "\n",
    "trapi_response = requests.post(\"http://localhost:3000/v1/query\", json=query)\n",
    "#trapi_response = requests.post(\"https://api.bte.ncats.io/v1/query\", json=query)\n",
    "#trapi_response = requests.post(\"https://arax.ncats.io/api/rtxkg2/v1.2/query\", json=query)\n",
    "#trapi_response = requests.post(\"https://arax.ncats.io/api/arax/v1.2/query\", json=query)\n",
    "print(trapi_response.status_code)\n",
    "if trapi_response.status_code != 200:\n",
    "    raise Exception(trapi_response.text)\n",
    "if trapi_response.from_cache:\n",
    "    print(f\"got response from cache\")\n",
    "\n",
    "trapi_message = trapi_response.json()[\"message\"]\n",
    "\n",
    "if len(trapi_message[\"results\"]) == 0:\n",
    "    raise Exception(f'No results returned')"
   ]
  },
  {
   "cell_type": "raw",
   "metadata": {},
   "source": [
    "print(json.dumps(query))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Analyze this GraphML file in Cytoscape: ./bte_sleeve_query_template.graphml\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "{('n0',\n",
       "  'n1'): Text(-0.4991114276680917, 0.44576953724722523, 'biolink:related_to'),\n",
       " ('n1',\n",
       "  'n2'): Text(0.49999999999999994, -0.44656318313571886, 'biolink:related_to')}"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "from collections import defaultdict\n",
    "\n",
    "\n",
    "G = nx.Graph()\n",
    "query_node_labels = {}\n",
    "for k, v in query['message']['query_graph']['nodes'].items():\n",
    "    node_ids = v.get('ids', [])\n",
    "    node_categories = v.get('categories', [])\n",
    "    G.add_node(k, categories=', '.join(node_categories))\n",
    "    query_node_labels[k] = '\\n'.join([k] + node_ids + node_categories)\n",
    "    #G.add_node(k, color='red')\n",
    "\n",
    "query_edge_labels = {}\n",
    "q_node_id_keys = set(['object', 'subject'])\n",
    "for q_edge_id,edge_v in query['message']['query_graph']['edges'].items():\n",
    "    for predicate in edge_v['predicates']:\n",
    "        G.add_edge(edge_v['subject'], edge_v['object'], predicate=predicate)\n",
    "    #G.add_edge(edge_v['subject'], edge_v['object'], predicates=', '.join(edge_v['predicates']))\n",
    "    #G.add_edge(edge_v['subject'], edge_v['object'], predicates=[1,2,3])\n",
    "    #G.add_edge(edge_v['subject'], edge_v['object'])\n",
    "    query_edge_labels[(edge_v['subject'], edge_v['object'])] = '\\n'.join(edge_v['predicates'])\n",
    "    \n",
    "query_template_f = './bte_sleeve_query_template.graphml'\n",
    "print(f'Analyze this GraphML file in Cytoscape: {query_template_f}')\n",
    "nx.write_graphml(G, query_template_f)\n",
    "\n",
    "options = {\n",
    "    'node_color': 'yellow',\n",
    "    'node_size': 5000,\n",
    "    'edge_color': 'black',\n",
    "    'width': 3,\n",
    "}\n",
    "query_template_layout = nx.spring_layout(G)\n",
    "nx.draw(G, pos=query_template_layout, **options)\n",
    "nx.draw_networkx_labels(G, query_template_layout, labels=query_node_labels, font_size=12,\n",
    "                        font_color='k', font_family='sans-serif',\n",
    "                        font_weight='normal', alpha=None, bbox=None, horizontalalignment='center',\n",
    "                        verticalalignment='center', ax=None, clip_on=False)\n",
    "nx.draw_networkx_edge_labels(G, pos=query_template_layout,\n",
    "                             edge_labels=query_edge_labels,\n",
    "                             label_pos=0.5, \n",
    "                             font_size=10, font_color='k', font_family='sans-serif',\n",
    "                             font_weight='normal', alpha=None, bbox=None, horizontalalignment='center',\n",
    "                             verticalalignment='center', ax=None, rotate=True, clip_on=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "# NOTE: we're including categories specified in the query template\n",
    "# in the next cell, and in a cell further down, we're also including\n",
    "# any categories from the TRAPI results.\n",
    "#\n",
    "# TODO: Some categories are supersets of others. Should we handle\n",
    "# this systematically?\n",
    "\n",
    "curie_categories = set()\n",
    "for v in query[\"message\"][\"query_graph\"][\"nodes\"].values():\n",
    "    if \"categories\" in v:\n",
    "        for category in v[\"categories\"]:\n",
    "            curie_categories.add(category)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "XRfyhW_Is73C",
    "outputId": "76ae9c51-0587-4f1e-91ac-f8d728ce6878"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "all CURIE prefixes found:\n",
      "{'ENSEMBL', 'CHEBI', 'NCBIGene', 'CAS', 'UniProtKB', 'INCHIKEY', 'UNII', 'SNOMEDCT', 'NCIT', 'MEDDRA', 'PR', 'MESH', 'HGNC', 'ICD0', 'ICD9', 'PUBCHEM.COMPOUND', 'HP', 'OMIM', 'DOID', 'UMLS', 'ICD10', 'MONDO', 'CHEMBL.COMPOUND'}\n",
      "\n",
      "unified CURIE prefixes found:\n",
      "{'MESH', 'NCBIGene'}\n",
      "\n",
      "failed to unify 0 CURIEs\n"
     ]
    }
   ],
   "source": [
    "# for genes/gene products, chemicals and diseases\n",
    "preferred_prefixes = set([\"NCBIGene\", \"MESH\"])\n",
    "trapi_results_unified_curies = set()\n",
    "unification_failed_curies = set()\n",
    "unified_prefixes = set()\n",
    "all_prefixes = set()\n",
    "curie_to_name = dict()\n",
    "curie_to_categories = dict()\n",
    "curie_to_unified_curie = dict()\n",
    "for k, v in trapi_message[\"knowledge_graph\"][\"nodes\"].items():\n",
    "    name = v[\"name\"]\n",
    "    categories = v[\"categories\"]\n",
    "    curie_categories |= set(categories)\n",
    "\n",
    "    for a in v[\"attributes\"]:\n",
    "        if a[\"attribute_type_id\"] == \"biolink:xref\":\n",
    "            curies = a[\"value\"]\n",
    "\n",
    "            # k should always be one of the curies\n",
    "            if not k in curies:\n",
    "                raise Exception(f\"key {k} not in {curies}\")\n",
    "\n",
    "            unified_curie = None\n",
    "            intersecting_trapi_results_unified_curies = trapi_results_unified_curies.intersection(\n",
    "                set(curies)\n",
    "            )\n",
    "            if len(intersecting_trapi_results_unified_curies) > 1:\n",
    "                multiple_matches = list(intersecting_trapi_results_unified_curies)\n",
    "                raise Exception(f\"matching multiple: {k} to {multiple_matches}\")\n",
    "            elif len(intersecting_trapi_results_unified_curies) == 1:\n",
    "                unified_curie = list(intersecting_trapi_results_unified_curies)[0]\n",
    "            else:\n",
    "                # get curie for preferred prefix. usually this is k, but not always.\n",
    "                for curie in curies:\n",
    "                    [prefix, identifier] = curie.split(\":\")\n",
    "                    if prefix in preferred_prefixes:\n",
    "                        unified_curie = curie\n",
    "                        trapi_results_unified_curies.add(unified_curie)\n",
    "                        break\n",
    "\n",
    "            if not unified_curie:\n",
    "                if k in curie_to_unified_curie:\n",
    "                    unified_curie = curie_to_unified_curie[k]\n",
    "                else:\n",
    "                    unification_failed_curies.add(k)\n",
    "                    break\n",
    "\n",
    "                #raise Exception(f\"failed to find a unified curie for {k} in {curies}\")\n",
    "\n",
    "            [unified_prefix, unified_identifier] = unified_curie.split(\":\")\n",
    "            unified_prefixes.add(unified_prefix)\n",
    "\n",
    "            for curie in curies:\n",
    "                [prefix, identifier] = curie.split(\":\")\n",
    "                all_prefixes.add(prefix)\n",
    "                if not curie in curie_to_unified_curie:\n",
    "                    curie_to_unified_curie[curie] = unified_curie\n",
    "                if not curie in curie_to_name:\n",
    "                    curie_to_name[curie] = name\n",
    "                    curie_to_categories[curie] = categories\n",
    "                elif curie_to_name[curie] != name:\n",
    "                    print(f\"curie {curie} has multiple primary names: {curie_to_name[curie]} and {name}\")\n",
    "                    #raise Exception(f\"curie{curie} has multiple names: {curie_to_name[curie]} and {name}\")\n",
    "\n",
    "\n",
    "#print(f'curie_categories: {curie_categories}')\n",
    "#print(\"\")\n",
    "#print(f\"curie_to_name key count: {len(curie_to_name.keys())}\")\n",
    "#print(f\"curie_to_unified_curie key count: {len(curie_to_unified_curie.keys())}\")\n",
    "#print(\"\")\n",
    "print(\"all CURIE prefixes found:\")\n",
    "print(all_prefixes)\n",
    "print(\"\")\n",
    "print(\"unified CURIE prefixes found:\")\n",
    "print(unified_prefixes)\n",
    "print(\"\")\n",
    "print(f\"failed to unify {len(unification_failed_curies)} CURIEs\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [],
   "source": [
    "columns = []\n",
    "q_node_id_keys = set([\"object\", \"subject\"])\n",
    "q_node_ids = []\n",
    "q_edge_ids = []\n",
    "for q_edge_id,edge_v in query[\"message\"][\"query_graph\"][\"edges\"].items():\n",
    "    q_edge_ids.append(q_edge_id)\n",
    "    q_node_id_found = False\n",
    "    for k,v in edge_v.items():\n",
    "        if (k in q_node_id_keys) and (type(v) is str):\n",
    "            if v not in columns:\n",
    "                q_node_ids.append(v)\n",
    "                columns.append(v)\n",
    "            if not q_node_id_found:\n",
    "                q_node_id_found = True\n",
    "                columns.append(q_edge_id)\n",
    "\n",
    "query_nodes_with_ids = set()\n",
    "for k, v in query['message']['query_graph']['nodes'].items():\n",
    "    node_ids = v.get('ids', [])\n",
    "    if node_ids:\n",
    "        query_nodes_with_ids.add(k)\n",
    "\n",
    "for q_node_id in q_node_ids:\n",
    "    columns.append(f\"{q_node_id}_original_curie\")\n",
    "    columns.append(f\"{q_node_id}_unified_curie\")\n",
    "\n",
    "columns.append(\"unified_curie_set\")\n",
    "\n",
    "trapi_result_columns = []\n",
    "for i in range(len(q_node_ids)):\n",
    "    trapi_result_columns.append(q_node_ids[i])\n",
    "    if i < len(q_edge_ids):\n",
    "        trapi_result_columns.append(q_edge_ids[i])\n",
    "\n",
    "unified_curie_columns = []\n",
    "for q_node_id in q_node_ids:\n",
    "    unified_curie_columns.append(f\"{q_node_id}_unified_curie\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
    "id": "Wjqcf5E4Yk_h"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "warning: predicate direction(s) may be switched\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>n0</th>\n",
       "      <th>e0</th>\n",
       "      <th>n1</th>\n",
       "      <th>e1</th>\n",
       "      <th>n2</th>\n",
       "      <th>n0_original_curie</th>\n",
       "      <th>n0_unified_curie</th>\n",
       "      <th>n1_original_curie</th>\n",
       "      <th>n1_unified_curie</th>\n",
       "      <th>n2_original_curie</th>\n",
       "      <th>n2_unified_curie</th>\n",
       "      <th>unified_curie_set</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Imatinib mesylate</td>\n",
       "      <td>decreases_molecular_modification_of</td>\n",
       "      <td>ABL1</td>\n",
       "      <td>related_to</td>\n",
       "      <td>leukemia</td>\n",
       "      <td>PUBCHEM.COMPOUND:123596</td>\n",
       "      <td>MESH:D000068877</td>\n",
       "      <td>NCBIGene:25</td>\n",
       "      <td>NCBIGene:25</td>\n",
       "      <td>MONDO:0005059</td>\n",
       "      <td>MESH:D007938</td>\n",
       "      <td>{MESH:D007938, MESH:D000068877, NCBIGene:25}</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Imatinib mesylate</td>\n",
       "      <td>decreases_molecular_modification_of</td>\n",
       "      <td>ABL1</td>\n",
       "      <td>affects_risk_for</td>\n",
       "      <td>leukemia</td>\n",
       "      <td>PUBCHEM.COMPOUND:123596</td>\n",
       "      <td>MESH:D000068877</td>\n",
       "      <td>NCBIGene:25</td>\n",
       "      <td>NCBIGene:25</td>\n",
       "      <td>MONDO:0005059</td>\n",
       "      <td>MESH:D007938</td>\n",
       "      <td>{MESH:D007938, MESH:D000068877, NCBIGene:25}</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Imatinib mesylate</td>\n",
       "      <td>decreases_molecular_modification_of</td>\n",
       "      <td>ABL1</td>\n",
       "      <td>contributes_to</td>\n",
       "      <td>leukemia</td>\n",
       "      <td>PUBCHEM.COMPOUND:123596</td>\n",
       "      <td>MESH:D000068877</td>\n",
       "      <td>NCBIGene:25</td>\n",
       "      <td>NCBIGene:25</td>\n",
       "      <td>MONDO:0005059</td>\n",
       "      <td>MESH:D007938</td>\n",
       "      <td>{MESH:D007938, MESH:D000068877, NCBIGene:25}</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Imatinib mesylate</td>\n",
       "      <td>decreases_molecular_modification_of</td>\n",
       "      <td>ABL1</td>\n",
       "      <td>gene_associated_with_condition</td>\n",
       "      <td>leukemia</td>\n",
       "      <td>PUBCHEM.COMPOUND:123596</td>\n",
       "      <td>MESH:D000068877</td>\n",
       "      <td>NCBIGene:25</td>\n",
       "      <td>NCBIGene:25</td>\n",
       "      <td>MONDO:0005059</td>\n",
       "      <td>MESH:D007938</td>\n",
       "      <td>{MESH:D007938, MESH:D000068877, NCBIGene:25}</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Imatinib mesylate</td>\n",
       "      <td>decreases_molecular_modification_of</td>\n",
       "      <td>ABL1</td>\n",
       "      <td>correlated_with</td>\n",
       "      <td>leukemia</td>\n",
       "      <td>PUBCHEM.COMPOUND:123596</td>\n",
       "      <td>MESH:D000068877</td>\n",
       "      <td>NCBIGene:25</td>\n",
       "      <td>NCBIGene:25</td>\n",
       "      <td>MONDO:0005059</td>\n",
       "      <td>MESH:D007938</td>\n",
       "      <td>{MESH:D007938, MESH:D000068877, NCBIGene:25}</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>86</th>\n",
       "      <td>Imatinib mesylate</td>\n",
       "      <td>increases_activity_of</td>\n",
       "      <td>JUN</td>\n",
       "      <td>gene_associated_with_condition</td>\n",
       "      <td>leukemia</td>\n",
       "      <td>PUBCHEM.COMPOUND:123596</td>\n",
       "      <td>MESH:D000068877</td>\n",
       "      <td>NCBIGene:3725</td>\n",
       "      <td>NCBIGene:3725</td>\n",
       "      <td>MONDO:0005059</td>\n",
       "      <td>MESH:D007938</td>\n",
       "      <td>{NCBIGene:3725, MESH:D000068877, MESH:D007938}</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>87</th>\n",
       "      <td>Imatinib mesylate</td>\n",
       "      <td>increases_activity_of</td>\n",
       "      <td>JUN</td>\n",
       "      <td>contributes_to</td>\n",
       "      <td>leukemia</td>\n",
       "      <td>PUBCHEM.COMPOUND:123596</td>\n",
       "      <td>MESH:D000068877</td>\n",
       "      <td>NCBIGene:3725</td>\n",
       "      <td>NCBIGene:3725</td>\n",
       "      <td>MONDO:0005059</td>\n",
       "      <td>MESH:D007938</td>\n",
       "      <td>{NCBIGene:3725, MESH:D000068877, MESH:D007938}</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>88</th>\n",
       "      <td>Imatinib mesylate</td>\n",
       "      <td>increases_activity_of</td>\n",
       "      <td>CASP8</td>\n",
       "      <td>gene_associated_with_condition</td>\n",
       "      <td>Asthma</td>\n",
       "      <td>PUBCHEM.COMPOUND:123596</td>\n",
       "      <td>MESH:D000068877</td>\n",
       "      <td>NCBIGene:841</td>\n",
       "      <td>NCBIGene:841</td>\n",
       "      <td>HP:0002099</td>\n",
       "      <td>MESH:D001249</td>\n",
       "      <td>{NCBIGene:841, MESH:D000068877, MESH:D001249}</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>89</th>\n",
       "      <td>Imatinib mesylate</td>\n",
       "      <td>increases_activity_of</td>\n",
       "      <td>CASP8</td>\n",
       "      <td>gene_associated_with_condition</td>\n",
       "      <td>leukemia</td>\n",
       "      <td>PUBCHEM.COMPOUND:123596</td>\n",
       "      <td>MESH:D000068877</td>\n",
       "      <td>NCBIGene:841</td>\n",
       "      <td>NCBIGene:841</td>\n",
       "      <td>MONDO:0005059</td>\n",
       "      <td>MESH:D007938</td>\n",
       "      <td>{NCBIGene:841, MESH:D000068877, MESH:D007938}</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>90</th>\n",
       "      <td>Imatinib mesylate</td>\n",
       "      <td>increases_activity_of</td>\n",
       "      <td>CASP8</td>\n",
       "      <td>correlated_with</td>\n",
       "      <td>leukemia</td>\n",
       "      <td>PUBCHEM.COMPOUND:123596</td>\n",
       "      <td>MESH:D000068877</td>\n",
       "      <td>NCBIGene:841</td>\n",
       "      <td>NCBIGene:841</td>\n",
       "      <td>MONDO:0005059</td>\n",
       "      <td>MESH:D007938</td>\n",
       "      <td>{NCBIGene:841, MESH:D000068877, MESH:D007938}</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>91 rows × 12 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                   n0                                   e0     n1  \\\n",
       "0   Imatinib mesylate  decreases_molecular_modification_of   ABL1   \n",
       "1   Imatinib mesylate  decreases_molecular_modification_of   ABL1   \n",
       "2   Imatinib mesylate  decreases_molecular_modification_of   ABL1   \n",
       "3   Imatinib mesylate  decreases_molecular_modification_of   ABL1   \n",
       "4   Imatinib mesylate  decreases_molecular_modification_of   ABL1   \n",
       "..                ...                                  ...    ...   \n",
       "86  Imatinib mesylate                increases_activity_of    JUN   \n",
       "87  Imatinib mesylate                increases_activity_of    JUN   \n",
       "88  Imatinib mesylate                increases_activity_of  CASP8   \n",
       "89  Imatinib mesylate                increases_activity_of  CASP8   \n",
       "90  Imatinib mesylate                increases_activity_of  CASP8   \n",
       "\n",
       "                                e1        n2        n0_original_curie  \\\n",
       "0                       related_to  leukemia  PUBCHEM.COMPOUND:123596   \n",
       "1                 affects_risk_for  leukemia  PUBCHEM.COMPOUND:123596   \n",
       "2                   contributes_to  leukemia  PUBCHEM.COMPOUND:123596   \n",
       "3   gene_associated_with_condition  leukemia  PUBCHEM.COMPOUND:123596   \n",
       "4                  correlated_with  leukemia  PUBCHEM.COMPOUND:123596   \n",
       "..                             ...       ...                      ...   \n",
       "86  gene_associated_with_condition  leukemia  PUBCHEM.COMPOUND:123596   \n",
       "87                  contributes_to  leukemia  PUBCHEM.COMPOUND:123596   \n",
       "88  gene_associated_with_condition    Asthma  PUBCHEM.COMPOUND:123596   \n",
       "89  gene_associated_with_condition  leukemia  PUBCHEM.COMPOUND:123596   \n",
       "90                 correlated_with  leukemia  PUBCHEM.COMPOUND:123596   \n",
       "\n",
       "   n0_unified_curie n1_original_curie n1_unified_curie n2_original_curie  \\\n",
       "0   MESH:D000068877       NCBIGene:25      NCBIGene:25     MONDO:0005059   \n",
       "1   MESH:D000068877       NCBIGene:25      NCBIGene:25     MONDO:0005059   \n",
       "2   MESH:D000068877       NCBIGene:25      NCBIGene:25     MONDO:0005059   \n",
       "3   MESH:D000068877       NCBIGene:25      NCBIGene:25     MONDO:0005059   \n",
       "4   MESH:D000068877       NCBIGene:25      NCBIGene:25     MONDO:0005059   \n",
       "..              ...               ...              ...               ...   \n",
       "86  MESH:D000068877     NCBIGene:3725    NCBIGene:3725     MONDO:0005059   \n",
       "87  MESH:D000068877     NCBIGene:3725    NCBIGene:3725     MONDO:0005059   \n",
       "88  MESH:D000068877      NCBIGene:841     NCBIGene:841        HP:0002099   \n",
       "89  MESH:D000068877      NCBIGene:841     NCBIGene:841     MONDO:0005059   \n",
       "90  MESH:D000068877      NCBIGene:841     NCBIGene:841     MONDO:0005059   \n",
       "\n",
       "   n2_unified_curie                               unified_curie_set  \n",
       "0      MESH:D007938    {MESH:D007938, MESH:D000068877, NCBIGene:25}  \n",
       "1      MESH:D007938    {MESH:D007938, MESH:D000068877, NCBIGene:25}  \n",
       "2      MESH:D007938    {MESH:D007938, MESH:D000068877, NCBIGene:25}  \n",
       "3      MESH:D007938    {MESH:D007938, MESH:D000068877, NCBIGene:25}  \n",
       "4      MESH:D007938    {MESH:D007938, MESH:D000068877, NCBIGene:25}  \n",
       "..              ...                                             ...  \n",
       "86     MESH:D007938  {NCBIGene:3725, MESH:D000068877, MESH:D007938}  \n",
       "87     MESH:D007938  {NCBIGene:3725, MESH:D000068877, MESH:D007938}  \n",
       "88     MESH:D001249   {NCBIGene:841, MESH:D000068877, MESH:D001249}  \n",
       "89     MESH:D007938   {NCBIGene:841, MESH:D000068877, MESH:D007938}  \n",
       "90     MESH:D007938   {NCBIGene:841, MESH:D000068877, MESH:D007938}  \n",
       "\n",
       "[91 rows x 12 columns]"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "trapi_results = trapi_message[\"results\"]\n",
    "\n",
    "result_row_data = []\n",
    "for trapi_result in trapi_results:\n",
    "    curie_to_qnode_ids = dict()\n",
    "    for qnode_id, entries in trapi_result[\"node_bindings\"].items():\n",
    "        for entry in entries:\n",
    "            curie = entry[\"id\"]\n",
    "            if curie not in curie_to_qnode_ids:\n",
    "                curie_to_qnode_ids[curie] = []\n",
    "            curie_to_qnode_ids[curie].append(qnode_id)\n",
    "\n",
    "    row_data_template = dict()\n",
    "    q_edge_id_to_predicates = dict()\n",
    "    trapi_result_curie_set = set()\n",
    "    for qedge_id, entries in trapi_result[\"edge_bindings\"].items():\n",
    "        for entry in entries:\n",
    "            curie = entry[\"id\"]\n",
    "            kg_entry = trapi_message[\"knowledge_graph\"][\"edges\"][curie]\n",
    "            subject_curie = kg_entry[\"subject\"]\n",
    "            object_curie = kg_entry[\"object\"]\n",
    "            predicate_curie = kg_entry[\"predicate\"]\n",
    "            [predicate_prefix, predicate_identifier] = predicate_curie.split(\":\")\n",
    "\n",
    "            if qedge_id not in q_edge_id_to_predicates:\n",
    "                q_edge_id_to_predicates[qedge_id] = set()\n",
    "            q_edge_id_to_predicates[qedge_id].add(predicate_identifier)\n",
    "\n",
    "            for curie in [subject_curie, object_curie]:\n",
    "                for qnode_id in curie_to_qnode_ids[curie]:\n",
    "                    if curie in curie_to_unified_curie:\n",
    "                        unified_curie = curie_to_unified_curie[curie]\n",
    "                    else:\n",
    "                        break\n",
    "\n",
    "                    name = curie_to_name[curie]\n",
    "                    row_data_template[qnode_id] = name\n",
    "\n",
    "                    trapi_result_curie_set.add(unified_curie)\n",
    "                    row_data_template[qnode_id + \"_original_curie\"] = curie\n",
    "                    row_data_template[qnode_id + \"_unified_curie\"] = unified_curie\n",
    "\n",
    "    if len(trapi_result_curie_set) != len(q_node_ids):\n",
    "        #print(f'skipping {list(curie_to_qnode_ids.keys())}')\n",
    "        continue\n",
    "\n",
    "    row_data_template[\"unified_curie_set\"] = trapi_result_curie_set\n",
    "    q_edge_ids_processed = set()\n",
    "    row_datas = [row_data_template]\n",
    "    for q_edge_id,predicates in q_edge_id_to_predicates.items():\n",
    "        next_row_datas = []\n",
    "        for row_data in row_datas:\n",
    "            for predicate in predicates:\n",
    "                next_row_data = deepcopy(row_data)\n",
    "                next_row_data[q_edge_id] = predicate\n",
    "                next_row_datas.append(\n",
    "                    next_row_data \n",
    "                )\n",
    "        row_datas = next_row_datas\n",
    "    result_row_data += row_datas\n",
    "\n",
    "print(\"warning: predicate direction(s) may be switched\")\n",
    "trapi_results_df = pd.DataFrame.from_records(result_row_data, columns=columns)\n",
    "trapi_results_df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'MESH:D000068877', 'MESH:D001249', 'MESH:D007938'}"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "user_specified_ids = set()\n",
    "for query_node in query_nodes_with_ids:\n",
    "    user_specified_ids.update(trapi_results_df[f'{query_node}_unified_curie'].tolist())\n",
    "user_specified_ids"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Unique CURIE count per query node:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "n0: 1\n",
      "n1: 20\n",
      "n2: 2\n"
     ]
    }
   ],
   "source": [
    "for q_node_id in q_node_ids:\n",
    "    print(f'{q_node_id}: {len(set(trapi_results_df[q_node_id]))}')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Total results count (excluding any that couldn't be unified):"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "25"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "len(set(trapi_results_df[\"unified_curie_set\"].map(sorted).map(tuple)))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "That's too too high for a researcher to efficiently go through manually. Let's try filtering to make that more manageable."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "uCW1x4PxnjYJ",
    "tags": []
   },
   "source": [
    "## Get PFOCR Data\n",
    "\n",
    "We could get it from the API, but for now, we'll just go ahead and download the entire JSON file we gave to BTE."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "OyJJUHbepKp2",
    "outputId": "7237b5f0-8740-47cb-9e3d-b03ea02bdcab"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "status_code: 200\n"
     ]
    }
   ],
   "source": [
    "pfocr_url = \"https://www.dropbox.com/s/1f14t5zaseocyg6/bte_chemicals_diseases_genes.ndjson?dl=1\"\n",
    "pfocr_request = requests.get(pfocr_url)\n",
    "print(f\"status_code: {pfocr_request.status_code}\")\n",
    "if pfocr_request.status_code != 200:\n",
    "    print(pfocr_request.text)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {
    "id": "8fUaenkS0gg1"
   },
   "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>figure_id</th>\n",
       "      <th>chemical_curie_set</th>\n",
       "      <th>disease_curie_set</th>\n",
       "      <th>gene_curie_set</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>PMC5732092__cshperspect-CYT-028522_F2.jpg</td>\n",
       "      <td>{}</td>\n",
       "      <td>{}</td>\n",
       "      <td>{NCBIGene:5601, NCBIGene:2921, NCBIGene:2920, ...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>PMC5793760__cshperspect-TGF-022210_F4.jpg</td>\n",
       "      <td>{}</td>\n",
       "      <td>{}</td>\n",
       "      <td>{NCBIGene:650, NCBIGene:8200, NCBIGene:353500,...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>PMC5793761__cshperspect-TGF-031989_F1.jpg</td>\n",
       "      <td>{}</td>\n",
       "      <td>{}</td>\n",
       "      <td>{NCBIGene:64750, NCBIGene:650, NCBIGene:5601, ...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>PMC5830892__cshperspect-CEL-027961_F2.jpg</td>\n",
       "      <td>{}</td>\n",
       "      <td>{}</td>\n",
       "      <td>{NCBIGene:2119, NCBIGene:55249, NCBIGene:72, N...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>PMC5830900__cshperspect-TGF-031997_F1.jpg</td>\n",
       "      <td>{MESH:D011374}</td>\n",
       "      <td>{}</td>\n",
       "      <td>{NCBIGene:650, NCBIGene:8200, NCBIGene:353500,...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>77714</th>\n",
       "      <td>PMC2804790__253_2009_2262_Fig1_HTML.jpg</td>\n",
       "      <td>{MESH:D018698, MESH:D000440, MESH:D007532, MES...</td>\n",
       "      <td>{}</td>\n",
       "      <td>{NCBIGene:126, NCBIGene:137872, NCBIGene:127, ...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>77715</th>\n",
       "      <td>PMC6332787__thnov09p0126g006.jpg</td>\n",
       "      <td>{}</td>\n",
       "      <td>{}</td>\n",
       "      <td>{NCBIGene:5133, NCBIGene:29126, NCBIGene:8295,...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>77716</th>\n",
       "      <td>PMC5807036__IJO-52-03-0787-g01.jpg</td>\n",
       "      <td>{}</td>\n",
       "      <td>{}</td>\n",
       "      <td>{NCBIGene:6721, NCBIGene:285, NCBIGene:2313, N...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>77717</th>\n",
       "      <td>PMC6770832__cancers-11-01236-g005.jpg</td>\n",
       "      <td>{}</td>\n",
       "      <td>{}</td>\n",
       "      <td>{NCBIGene:4089, NCBIGene:4090, NCBIGene:5595, ...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>77718</th>\n",
       "      <td>PMC4609065__12885_2015_1721_Fig5_HTML.jpg</td>\n",
       "      <td>{MESH:C029269, MESH:D014443}</td>\n",
       "      <td>{}</td>\n",
       "      <td>{NCBIGene:3845, NCBIGene:1956, NCBIGene:1806, ...</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>77719 rows × 4 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                                       figure_id  \\\n",
       "0      PMC5732092__cshperspect-CYT-028522_F2.jpg   \n",
       "1      PMC5793760__cshperspect-TGF-022210_F4.jpg   \n",
       "2      PMC5793761__cshperspect-TGF-031989_F1.jpg   \n",
       "3      PMC5830892__cshperspect-CEL-027961_F2.jpg   \n",
       "4      PMC5830900__cshperspect-TGF-031997_F1.jpg   \n",
       "...                                          ...   \n",
       "77714    PMC2804790__253_2009_2262_Fig1_HTML.jpg   \n",
       "77715           PMC6332787__thnov09p0126g006.jpg   \n",
       "77716         PMC5807036__IJO-52-03-0787-g01.jpg   \n",
       "77717      PMC6770832__cancers-11-01236-g005.jpg   \n",
       "77718  PMC4609065__12885_2015_1721_Fig5_HTML.jpg   \n",
       "\n",
       "                                      chemical_curie_set disease_curie_set  \\\n",
       "0                                                     {}                {}   \n",
       "1                                                     {}                {}   \n",
       "2                                                     {}                {}   \n",
       "3                                                     {}                {}   \n",
       "4                                         {MESH:D011374}                {}   \n",
       "...                                                  ...               ...   \n",
       "77714  {MESH:D018698, MESH:D000440, MESH:D007532, MES...                {}   \n",
       "77715                                                 {}                {}   \n",
       "77716                                                 {}                {}   \n",
       "77717                                                 {}                {}   \n",
       "77718                       {MESH:C029269, MESH:D014443}                {}   \n",
       "\n",
       "                                          gene_curie_set  \n",
       "0      {NCBIGene:5601, NCBIGene:2921, NCBIGene:2920, ...  \n",
       "1      {NCBIGene:650, NCBIGene:8200, NCBIGene:353500,...  \n",
       "2      {NCBIGene:64750, NCBIGene:650, NCBIGene:5601, ...  \n",
       "3      {NCBIGene:2119, NCBIGene:55249, NCBIGene:72, N...  \n",
       "4      {NCBIGene:650, NCBIGene:8200, NCBIGene:353500,...  \n",
       "...                                                  ...  \n",
       "77714  {NCBIGene:126, NCBIGene:137872, NCBIGene:127, ...  \n",
       "77715  {NCBIGene:5133, NCBIGene:29126, NCBIGene:8295,...  \n",
       "77716  {NCBIGene:6721, NCBIGene:285, NCBIGene:2313, N...  \n",
       "77717  {NCBIGene:4089, NCBIGene:4090, NCBIGene:5595, ...  \n",
       "77718  {NCBIGene:3845, NCBIGene:1956, NCBIGene:1806, ...  \n",
       "\n",
       "[77719 rows x 4 columns]"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "figures_df_data = []\n",
    "curies_to_figure_ids = {}\n",
    "figure_id_to_curies = {}\n",
    "figure_id_to_pfocr_result = {}\n",
    "for line in pfocr_request.text.splitlines():\n",
    "    pfocr_result = json.loads(line)\n",
    "    figure_id = pfocr_result[\"_id\"]\n",
    "\n",
    "    chemical_curies = set()\n",
    "    disease_curies = set()\n",
    "    gene_curies = set()\n",
    "    for identifier in pfocr_result[\"associatedWith\"][\"mentions\"][\"chemicals\"][\"mesh\"]:\n",
    "        curie = \"MESH:\" + identifier\n",
    "        chemical_curies.add(curie)\n",
    "    for identifier in pfocr_result[\"associatedWith\"][\"mentions\"][\"diseases\"][\"mesh\"]:\n",
    "        curie = \"MESH:\" + identifier\n",
    "        disease_curies.add(curie)\n",
    "    for identifier in pfocr_result[\"associatedWith\"][\"mentions\"][\"genes\"][\"ncbigene\"]:\n",
    "        curie = \"NCBIGene:\" + identifier\n",
    "        gene_curies.add(curie)\n",
    "\n",
    "    figure_id_to_pfocr_result[figure_id] = pfocr_result\n",
    "    figure_id_to_curies[figure_id] = curies\n",
    "\n",
    "    figures_df_data.append({\n",
    "        \"figure_id\": figure_id,\n",
    "        \"chemical_curie_set\": chemical_curies,\n",
    "        \"disease_curie_set\": disease_curies,\n",
    "        \"gene_curie_set\": gene_curies,\n",
    "    })\n",
    "\n",
    "figures_df = pd.DataFrame.from_records(figures_df_data)\n",
    "figures_df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [],
   "source": [
    "biolink_category_to_pfocr_category = {\n",
    "    \"biolink:Drug\": \"chemical\",\n",
    "    \"biolink:ChemicalEntity\": \"chemical\",\n",
    "    \"biolink:SmallMolecule\": \"chemical\",\n",
    "    \"biolink:Disease\": \"disease\",\n",
    "    \"biolink:DiseaseOrPhenotypicFeature\": \"disease\",\n",
    "    \"biolink:Gene\": \"gene\",\n",
    "    \"biolink:GeneProduct\": \"gene\",\n",
    "    \"biolink:Protein\": \"gene\",\n",
    "}\n",
    "\n",
    "# limit to categories returned from query\n",
    "relevant_biolink_categories = set(biolink_category_to_pfocr_category.keys()).intersection(curie_categories)\n",
    "relevant_pfocr_categories = set([\n",
    "    biolink_category_to_pfocr_category[\n",
    "        biolink_category\n",
    "    ] for biolink_category in relevant_biolink_categories\n",
    "])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "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>figure_url</th>\n",
       "      <th>figure_title</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>figure_id</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>PMC5732092__cshperspect-CYT-028522_F2.jpg</th>\n",
       "      <td>https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5...</td>\n",
       "      <td>Interleukin (IL)-17RA/RC signaling pathways</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>PMC5793760__cshperspect-TGF-022210_F4.jpg</th>\n",
       "      <td>https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5...</td>\n",
       "      <td>TGFB signaling pathways</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>PMC5793761__cshperspect-TGF-031989_F1.jpg</th>\n",
       "      <td>https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5...</td>\n",
       "      <td>Bone morphogenetic protein (BMP) signaling pat...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>PMC5830892__cshperspect-CEL-027961_F2.jpg</th>\n",
       "      <td>https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5...</td>\n",
       "      <td>FERM-binding partners of Crumbs3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>PMC5830900__cshperspect-TGF-031997_F1.jpg</th>\n",
       "      <td>https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5...</td>\n",
       "      <td>Role of the TGFB family in mammary gland devel...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>PMC2804790__253_2009_2262_Fig1_HTML.jpg</th>\n",
       "      <td>https://www.ncbi.nlm.nih.gov/pmc/articles/PMC2...</td>\n",
       "      <td>Ehrlich degradation pathway from amino acid to...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>PMC6332787__thnov09p0126g006.jpg</th>\n",
       "      <td>https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6...</td>\n",
       "      <td>Illustration of the mechanisms underlying LPD ...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>PMC5807036__IJO-52-03-0787-g01.jpg</th>\n",
       "      <td>https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5...</td>\n",
       "      <td>Tumorigenic proteins significantly upregulated...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>PMC6770832__cancers-11-01236-g005.jpg</th>\n",
       "      <td>https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6...</td>\n",
       "      <td>Simplified TGFB pathway leading to p21 expression</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>PMC4609065__12885_2015_1721_Fig5_HTML.jpg</th>\n",
       "      <td>https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4...</td>\n",
       "      <td>EGFR and ANXA3 to fluoropyrimidine resistance ...</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>77719 rows × 2 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                                                                                  figure_url  \\\n",
       "figure_id                                                                                      \n",
       "PMC5732092__cshperspect-CYT-028522_F2.jpg  https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5...   \n",
       "PMC5793760__cshperspect-TGF-022210_F4.jpg  https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5...   \n",
       "PMC5793761__cshperspect-TGF-031989_F1.jpg  https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5...   \n",
       "PMC5830892__cshperspect-CEL-027961_F2.jpg  https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5...   \n",
       "PMC5830900__cshperspect-TGF-031997_F1.jpg  https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5...   \n",
       "...                                                                                      ...   \n",
       "PMC2804790__253_2009_2262_Fig1_HTML.jpg    https://www.ncbi.nlm.nih.gov/pmc/articles/PMC2...   \n",
       "PMC6332787__thnov09p0126g006.jpg           https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6...   \n",
       "PMC5807036__IJO-52-03-0787-g01.jpg         https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5...   \n",
       "PMC6770832__cancers-11-01236-g005.jpg      https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6...   \n",
       "PMC4609065__12885_2015_1721_Fig5_HTML.jpg  https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4...   \n",
       "\n",
       "                                                                                figure_title  \n",
       "figure_id                                                                                     \n",
       "PMC5732092__cshperspect-CYT-028522_F2.jpg        Interleukin (IL)-17RA/RC signaling pathways  \n",
       "PMC5793760__cshperspect-TGF-022210_F4.jpg                            TGFB signaling pathways  \n",
       "PMC5793761__cshperspect-TGF-031989_F1.jpg  Bone morphogenetic protein (BMP) signaling pat...  \n",
       "PMC5830892__cshperspect-CEL-027961_F2.jpg                   FERM-binding partners of Crumbs3  \n",
       "PMC5830900__cshperspect-TGF-031997_F1.jpg  Role of the TGFB family in mammary gland devel...  \n",
       "...                                                                                      ...  \n",
       "PMC2804790__253_2009_2262_Fig1_HTML.jpg    Ehrlich degradation pathway from amino acid to...  \n",
       "PMC6332787__thnov09p0126g006.jpg           Illustration of the mechanisms underlying LPD ...  \n",
       "PMC5807036__IJO-52-03-0787-g01.jpg         Tumorigenic proteins significantly upregulated...  \n",
       "PMC6770832__cancers-11-01236-g005.jpg      Simplified TGFB pathway leading to p21 expression  \n",
       "PMC4609065__12885_2015_1721_Fig5_HTML.jpg  EGFR and ANXA3 to fluoropyrimidine resistance ...  \n",
       "\n",
       "[77719 rows x 2 columns]"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "figure_metadata_records = []\n",
    "figure_curie_df_data = []\n",
    "for line in pfocr_request.text.splitlines():\n",
    "    pfocr_result = json.loads(line)\n",
    "    figure_id = pfocr_result[\"_id\"]\n",
    "\n",
    "    chemical_curies = set()\n",
    "    disease_curies = set()\n",
    "    gene_curies = set()\n",
    "    for identifier in pfocr_result[\"associatedWith\"][\"mentions\"][\"chemicals\"][\"mesh\"]:\n",
    "        curie = \"MESH:\" + identifier\n",
    "        chemical_curies.add(curie)\n",
    "        figure_curie_df_data.append({\n",
    "            \"figure_id\": figure_id,\n",
    "            \"curie\": curie,\n",
    "            \"category\": \"chemical\",\n",
    "        })\n",
    "    for identifier in pfocr_result[\"associatedWith\"][\"mentions\"][\"diseases\"][\"mesh\"]:\n",
    "        curie = \"MESH:\" + identifier\n",
    "        disease_curies.add(curie)\n",
    "        figure_curie_df_data.append({\n",
    "            \"figure_id\": figure_id,\n",
    "            \"curie\": curie,\n",
    "            \"category\": \"disease\",\n",
    "        })\n",
    "    for identifier in pfocr_result[\"associatedWith\"][\"mentions\"][\"genes\"][\"ncbigene\"]:\n",
    "        curie = \"NCBIGene:\" + identifier\n",
    "        gene_curies.add(curie)\n",
    "        figure_curie_df_data.append({\n",
    "            \"figure_id\": figure_id,\n",
    "            \"curie\": curie,\n",
    "            \"category\": \"gene\",\n",
    "        })\n",
    "\n",
    "    figure_id_to_pfocr_result[figure_id] = pfocr_result\n",
    "    figure_id_to_curies[figure_id] = curies\n",
    "\n",
    "    figure_metadata_records.append({\n",
    "        \"figure_id\": figure_id,\n",
    "        \"figure_url\": pfocr_result[\"associatedWith\"][\"figureUrl\"],\n",
    "        \"figure_title\": pfocr_result[\"associatedWith\"][\"title\"],\n",
    "    })\n",
    "\n",
    "figure_metadata_df = pd.DataFrame.from_records(figure_metadata_records, index='figure_id')\n",
    "figure_metadata_df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "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>figure_id</th>\n",
       "      <th>curie</th>\n",
       "      <th>category</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>PMC5732092__cshperspect-CYT-028522_F2.jpg</td>\n",
       "      <td>NCBIGene:5601</td>\n",
       "      <td>gene</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>PMC5732092__cshperspect-CYT-028522_F2.jpg</td>\n",
       "      <td>NCBIGene:5595</td>\n",
       "      <td>gene</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>PMC5732092__cshperspect-CYT-028522_F2.jpg</td>\n",
       "      <td>NCBIGene:1051</td>\n",
       "      <td>gene</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>PMC5732092__cshperspect-CYT-028522_F2.jpg</td>\n",
       "      <td>NCBIGene:10131</td>\n",
       "      <td>gene</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>PMC5732092__cshperspect-CYT-028522_F2.jpg</td>\n",
       "      <td>NCBIGene:23765</td>\n",
       "      <td>gene</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1665596</th>\n",
       "      <td>PMC4609065__12885_2015_1721_Fig5_HTML.jpg</td>\n",
       "      <td>NCBIGene:1956</td>\n",
       "      <td>gene</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1665597</th>\n",
       "      <td>PMC4609065__12885_2015_1721_Fig5_HTML.jpg</td>\n",
       "      <td>NCBIGene:4893</td>\n",
       "      <td>gene</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1665598</th>\n",
       "      <td>PMC4609065__12885_2015_1721_Fig5_HTML.jpg</td>\n",
       "      <td>NCBIGene:2064</td>\n",
       "      <td>gene</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1665599</th>\n",
       "      <td>PMC4609065__12885_2015_1721_Fig5_HTML.jpg</td>\n",
       "      <td>NCBIGene:3845</td>\n",
       "      <td>gene</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1665600</th>\n",
       "      <td>PMC4609065__12885_2015_1721_Fig5_HTML.jpg</td>\n",
       "      <td>NCBIGene:306</td>\n",
       "      <td>gene</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>1665601 rows × 3 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                                         figure_id           curie category\n",
       "0        PMC5732092__cshperspect-CYT-028522_F2.jpg   NCBIGene:5601     gene\n",
       "1        PMC5732092__cshperspect-CYT-028522_F2.jpg   NCBIGene:5595     gene\n",
       "2        PMC5732092__cshperspect-CYT-028522_F2.jpg   NCBIGene:1051     gene\n",
       "3        PMC5732092__cshperspect-CYT-028522_F2.jpg  NCBIGene:10131     gene\n",
       "4        PMC5732092__cshperspect-CYT-028522_F2.jpg  NCBIGene:23765     gene\n",
       "...                                            ...             ...      ...\n",
       "1665596  PMC4609065__12885_2015_1721_Fig5_HTML.jpg   NCBIGene:1956     gene\n",
       "1665597  PMC4609065__12885_2015_1721_Fig5_HTML.jpg   NCBIGene:4893     gene\n",
       "1665598  PMC4609065__12885_2015_1721_Fig5_HTML.jpg   NCBIGene:2064     gene\n",
       "1665599  PMC4609065__12885_2015_1721_Fig5_HTML.jpg   NCBIGene:3845     gene\n",
       "1665600  PMC4609065__12885_2015_1721_Fig5_HTML.jpg    NCBIGene:306     gene\n",
       "\n",
       "[1665601 rows x 3 columns]"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "figure_curie_df = pd.DataFrame.from_records(figure_curie_df_data)\n",
    "figure_curie_df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index(['figure_id', 'curie', 'category'], dtype='object')"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "figure_curie_df.columns"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Clustering"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "tags": []
   },
   "source": [
    "### Identify Cluster Figures\n",
    "\n",
    "See details in write-up at the top."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "category_matched_pfocr_curie_count: 30163\n",
      "iteration  0: figures: 15, CURIEs:  8\n",
      "iteration  1: figures:  1, CURIEs:  5\n",
      "iteration  2: figures:  1, CURIEs:  3\n",
      "iteration  3: figures:  1, CURIEs:  1\n",
      "iteration  4: figures:  2, CURIEs:  2\n",
      "iteration  5: figures:  1, CURIEs:  1\n",
      "iteration  6: figures:  1, CURIEs:  1\n",
      "iteration  7: figures:  1, CURIEs:  1\n",
      "cluster figure count: 23\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>iteration</th>\n",
       "      <th>figure_id</th>\n",
       "      <th>p_value</th>\n",
       "      <th>next_best_figure_ids</th>\n",
       "      <th>next_best_p_value</th>\n",
       "      <th>yes_trapi_results_yes_figure</th>\n",
       "      <th>yes_trapi_results_no_figure</th>\n",
       "      <th>no_trapi_results_yes_figure</th>\n",
       "      <th>no_trapi_results_no_figure</th>\n",
       "      <th>figure_curies_in_trapi_results</th>\n",
       "      <th>curie_set</th>\n",
       "      <th>curies_to_exclude</th>\n",
       "      <th>len_curies_to_exclude</th>\n",
       "      <th>remaining_figure_count</th>\n",
       "      <th>remaining_trapi_curie_count</th>\n",
       "      <th>cluster_figure_id_count</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0</td>\n",
       "      <td>PMC1767116__21889.f3.jpg</td>\n",
       "      <td>4.454811e-09</td>\n",
       "      <td>[PMC2940266__nihms99348f1.jpg, PMC3240145__ar5...</td>\n",
       "      <td>5.194659e-09</td>\n",
       "      <td>5</td>\n",
       "      <td>18</td>\n",
       "      <td>22</td>\n",
       "      <td>30118</td>\n",
       "      <td>{NCBIGene:841, NCBIGene:836, NCBIGene:842, NCB...</td>\n",
       "      <td>{NCBIGene:317, NCBIGene:10000, NCBIGene:596, N...</td>\n",
       "      <td>{NCBIGene:142, NCBIGene:841, MESH:D007938, NCB...</td>\n",
       "      <td>8</td>\n",
       "      <td>19293</td>\n",
       "      <td>23</td>\n",
       "      <td>15</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>0</td>\n",
       "      <td>PMC2724780__1721fig1.jpg</td>\n",
       "      <td>4.454811e-09</td>\n",
       "      <td>[PMC2940266__nihms99348f1.jpg, PMC3240145__ar5...</td>\n",
       "      <td>5.194659e-09</td>\n",
       "      <td>4</td>\n",
       "      <td>19</td>\n",
       "      <td>23</td>\n",
       "      <td>30117</td>\n",
       "      <td>{NCBIGene:836, NCBIGene:841, NCBIGene:207, NCB...</td>\n",
       "      <td>{NCBIGene:317, NCBIGene:10000, NCBIGene:596, N...</td>\n",
       "      <td>{NCBIGene:142, NCBIGene:841, MESH:D007938, NCB...</td>\n",
       "      <td>8</td>\n",
       "      <td>19293</td>\n",
       "      <td>23</td>\n",
       "      <td>15</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>0</td>\n",
       "      <td>PMC2776288__ajtr0001-0001-f2.jpg</td>\n",
       "      <td>4.454811e-09</td>\n",
       "      <td>[PMC2940266__nihms99348f1.jpg, PMC3240145__ar5...</td>\n",
       "      <td>5.194659e-09</td>\n",
       "      <td>4</td>\n",
       "      <td>19</td>\n",
       "      <td>23</td>\n",
       "      <td>30117</td>\n",
       "      <td>{NCBIGene:836, NCBIGene:841, NCBIGene:56616, N...</td>\n",
       "      <td>{NCBIGene:839, NCBIGene:838, NCBIGene:7124, NC...</td>\n",
       "      <td>{NCBIGene:142, NCBIGene:841, MESH:D007938, NCB...</td>\n",
       "      <td>8</td>\n",
       "      <td>19293</td>\n",
       "      <td>23</td>\n",
       "      <td>15</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>0</td>\n",
       "      <td>PMC3173800__emboj2011307f2.jpg</td>\n",
       "      <td>4.454811e-09</td>\n",
       "      <td>[PMC2940266__nihms99348f1.jpg, PMC3240145__ar5...</td>\n",
       "      <td>5.194659e-09</td>\n",
       "      <td>4</td>\n",
       "      <td>19</td>\n",
       "      <td>23</td>\n",
       "      <td>30117</td>\n",
       "      <td>{NCBIGene:836, NCBIGene:841, NCBIGene:56616, N...</td>\n",
       "      <td>{NCBIGene:317, NCBIGene:356, NCBIGene:596, NCB...</td>\n",
       "      <td>{NCBIGene:142, NCBIGene:841, MESH:D007938, NCB...</td>\n",
       "      <td>8</td>\n",
       "      <td>19293</td>\n",
       "      <td>23</td>\n",
       "      <td>15</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>0</td>\n",
       "      <td>PMC3277752__zjw0011240500002.jpg</td>\n",
       "      <td>4.454811e-09</td>\n",
       "      <td>[PMC2940266__nihms99348f1.jpg, PMC3240145__ar5...</td>\n",
       "      <td>5.194659e-09</td>\n",
       "      <td>4</td>\n",
       "      <td>19</td>\n",
       "      <td>23</td>\n",
       "      <td>30117</td>\n",
       "      <td>{NCBIGene:836, NCBIGene:841, NCBIGene:56616, N...</td>\n",
       "      <td>{NCBIGene:317, NCBIGene:27113, NCBIGene:839, N...</td>\n",
       "      <td>{NCBIGene:142, NCBIGene:841, MESH:D007938, NCB...</td>\n",
       "      <td>8</td>\n",
       "      <td>19293</td>\n",
       "      <td>23</td>\n",
       "      <td>15</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>0</td>\n",
       "      <td>PMC3437743__nihms-400038-f0001.jpg</td>\n",
       "      <td>4.454811e-09</td>\n",
       "      <td>[PMC2940266__nihms99348f1.jpg, PMC3240145__ar5...</td>\n",
       "      <td>5.194659e-09</td>\n",
       "      <td>4</td>\n",
       "      <td>19</td>\n",
       "      <td>23</td>\n",
       "      <td>30117</td>\n",
       "      <td>{NCBIGene:836, NCBIGene:841, NCBIGene:56616, N...</td>\n",
       "      <td>{NCBIGene:9131, NCBIGene:317, NCBIGene:356, NC...</td>\n",
       "      <td>{NCBIGene:142, NCBIGene:841, MESH:D007938, NCB...</td>\n",
       "      <td>8</td>\n",
       "      <td>19293</td>\n",
       "      <td>23</td>\n",
       "      <td>15</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>0</td>\n",
       "      <td>PMC5086377__DM2016-9602472.001.jpg</td>\n",
       "      <td>4.454811e-09</td>\n",
       "      <td>[PMC2940266__nihms99348f1.jpg, PMC3240145__ar5...</td>\n",
       "      <td>5.194659e-09</td>\n",
       "      <td>4</td>\n",
       "      <td>19</td>\n",
       "      <td>23</td>\n",
       "      <td>30117</td>\n",
       "      <td>{NCBIGene:836, NCBIGene:841, NCBIGene:842, NCB...</td>\n",
       "      <td>{NCBIGene:2353, NCBIGene:959, NCBIGene:839, NC...</td>\n",
       "      <td>{NCBIGene:142, NCBIGene:841, MESH:D007938, NCB...</td>\n",
       "      <td>8</td>\n",
       "      <td>19293</td>\n",
       "      <td>23</td>\n",
       "      <td>15</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>0</td>\n",
       "      <td>PMC5382109__nihms824859f3.jpg</td>\n",
       "      <td>4.454811e-09</td>\n",
       "      <td>[PMC2940266__nihms99348f1.jpg, PMC3240145__ar5...</td>\n",
       "      <td>5.194659e-09</td>\n",
       "      <td>4</td>\n",
       "      <td>19</td>\n",
       "      <td>23</td>\n",
       "      <td>30117</td>\n",
       "      <td>{NCBIGene:836, NCBIGene:841, NCBIGene:56616, N...</td>\n",
       "      <td>{NCBIGene:8837, NCBIGene:317, NCBIGene:27113, ...</td>\n",
       "      <td>{NCBIGene:142, NCBIGene:841, MESH:D007938, NCB...</td>\n",
       "      <td>8</td>\n",
       "      <td>19293</td>\n",
       "      <td>23</td>\n",
       "      <td>15</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>0</td>\n",
       "      <td>PMC5386521__cddis2017115f1.jpg</td>\n",
       "      <td>4.454811e-09</td>\n",
       "      <td>[PMC2940266__nihms99348f1.jpg, PMC3240145__ar5...</td>\n",
       "      <td>5.194659e-09</td>\n",
       "      <td>4</td>\n",
       "      <td>19</td>\n",
       "      <td>23</td>\n",
       "      <td>30117</td>\n",
       "      <td>{NCBIGene:836, NCBIGene:841, NCBIGene:207, NCB...</td>\n",
       "      <td>{NCBIGene:1876, NCBIGene:1871, NCBIGene:10000,...</td>\n",
       "      <td>{NCBIGene:142, NCBIGene:841, MESH:D007938, NCB...</td>\n",
       "      <td>8</td>\n",
       "      <td>19293</td>\n",
       "      <td>23</td>\n",
       "      <td>15</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>0</td>\n",
       "      <td>PMC5979467__ijms-19-00971-g002.jpg</td>\n",
       "      <td>4.454811e-09</td>\n",
       "      <td>[PMC2940266__nihms99348f1.jpg, PMC3240145__ar5...</td>\n",
       "      <td>5.194659e-09</td>\n",
       "      <td>4</td>\n",
       "      <td>19</td>\n",
       "      <td>23</td>\n",
       "      <td>30117</td>\n",
       "      <td>{NCBIGene:836, NCBIGene:841, NCBIGene:56616, N...</td>\n",
       "      <td>{NCBIGene:7514, NCBIGene:317, NCBIGene:1431, N...</td>\n",
       "      <td>{NCBIGene:142, NCBIGene:841, MESH:D007938, NCB...</td>\n",
       "      <td>8</td>\n",
       "      <td>19293</td>\n",
       "      <td>23</td>\n",
       "      <td>15</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>0</td>\n",
       "      <td>PMC7075948__fcell-08-00143-g002.jpg</td>\n",
       "      <td>4.454811e-09</td>\n",
       "      <td>[PMC2940266__nihms99348f1.jpg, PMC3240145__ar5...</td>\n",
       "      <td>5.194659e-09</td>\n",
       "      <td>4</td>\n",
       "      <td>19</td>\n",
       "      <td>23</td>\n",
       "      <td>30117</td>\n",
       "      <td>{NCBIGene:836, NCBIGene:841, NCBIGene:207, NCB...</td>\n",
       "      <td>{NCBIGene:10000, NCBIGene:839, NCBIGene:838, N...</td>\n",
       "      <td>{NCBIGene:142, NCBIGene:841, MESH:D007938, NCB...</td>\n",
       "      <td>8</td>\n",
       "      <td>19293</td>\n",
       "      <td>23</td>\n",
       "      <td>15</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>0</td>\n",
       "      <td>PMC7174937__OMCL2020-8787156.009.jpg</td>\n",
       "      <td>4.454811e-09</td>\n",
       "      <td>[PMC2940266__nihms99348f1.jpg, PMC3240145__ar5...</td>\n",
       "      <td>5.194659e-09</td>\n",
       "      <td>4</td>\n",
       "      <td>19</td>\n",
       "      <td>23</td>\n",
       "      <td>30117</td>\n",
       "      <td>{NCBIGene:836, NCBIGene:841, NCBIGene:207, NCB...</td>\n",
       "      <td>{NCBIGene:10000, NCBIGene:839, NCBIGene:838, N...</td>\n",
       "      <td>{NCBIGene:142, NCBIGene:841, MESH:D007938, NCB...</td>\n",
       "      <td>8</td>\n",
       "      <td>19293</td>\n",
       "      <td>23</td>\n",
       "      <td>15</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>0</td>\n",
       "      <td>PMC7432057__ijms-21-05242-g003.jpg</td>\n",
       "      <td>4.454811e-09</td>\n",
       "      <td>[PMC2940266__nihms99348f1.jpg, PMC3240145__ar5...</td>\n",
       "      <td>5.194659e-09</td>\n",
       "      <td>4</td>\n",
       "      <td>19</td>\n",
       "      <td>23</td>\n",
       "      <td>30117</td>\n",
       "      <td>{NCBIGene:836, NCBIGene:841, NCBIGene:207, NCB...</td>\n",
       "      <td>{NCBIGene:5601, NCBIGene:6774, NCBIGene:10000,...</td>\n",
       "      <td>{NCBIGene:142, NCBIGene:841, MESH:D007938, NCB...</td>\n",
       "      <td>8</td>\n",
       "      <td>19293</td>\n",
       "      <td>23</td>\n",
       "      <td>15</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>0</td>\n",
       "      <td>PMC7499322__SCI2020-8844730.003.jpg</td>\n",
       "      <td>4.454811e-09</td>\n",
       "      <td>[PMC2940266__nihms99348f1.jpg, PMC3240145__ar5...</td>\n",
       "      <td>5.194659e-09</td>\n",
       "      <td>4</td>\n",
       "      <td>19</td>\n",
       "      <td>23</td>\n",
       "      <td>30117</td>\n",
       "      <td>{NCBIGene:836, NCBIGene:841, MESH:D007938, NCB...</td>\n",
       "      <td>{MESH:D005909, MESH:D009369, MESH:C051905, MES...</td>\n",
       "      <td>{NCBIGene:142, NCBIGene:841, MESH:D007938, NCB...</td>\n",
       "      <td>8</td>\n",
       "      <td>19293</td>\n",
       "      <td>23</td>\n",
       "      <td>15</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>0</td>\n",
       "      <td>PMC8040309__ijbsv17p1079g003.jpg</td>\n",
       "      <td>4.454811e-09</td>\n",
       "      <td>[PMC2940266__nihms99348f1.jpg, PMC3240145__ar5...</td>\n",
       "      <td>5.194659e-09</td>\n",
       "      <td>5</td>\n",
       "      <td>18</td>\n",
       "      <td>22</td>\n",
       "      <td>30118</td>\n",
       "      <td>{NCBIGene:142, NCBIGene:841, NCBIGene:836, NCB...</td>\n",
       "      <td>{MESH:D009369, NCBIGene:6774, NCBIGene:10000, ...</td>\n",
       "      <td>{NCBIGene:142, NCBIGene:841, MESH:D007938, NCB...</td>\n",
       "      <td>8</td>\n",
       "      <td>19293</td>\n",
       "      <td>23</td>\n",
       "      <td>15</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>1</td>\n",
       "      <td>PMC5078657__OMCL2016-9324085.002.jpg</td>\n",
       "      <td>1.630866e-08</td>\n",
       "      <td>[PMC5226821__pcbi.1005287.g013.jpg, PMC6513836...</td>\n",
       "      <td>2.801800e-08</td>\n",
       "      <td>5</td>\n",
       "      <td>10</td>\n",
       "      <td>167</td>\n",
       "      <td>29981</td>\n",
       "      <td>{NCBIGene:9429, NCBIGene:1021, NCBIGene:7422, ...</td>\n",
       "      <td>{NCBIGene:4322, NCBIGene:7481, NCBIGene:10000,...</td>\n",
       "      <td>{NCBIGene:1021, NCBIGene:7422, NCBIGene:3725, ...</td>\n",
       "      <td>5</td>\n",
       "      <td>19278</td>\n",
       "      <td>15</td>\n",
       "      <td>16</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>2</td>\n",
       "      <td>PMC7604619__1052524.fig3.jpg</td>\n",
       "      <td>4.502922e-07</td>\n",
       "      <td>[PMC6927537__nihms-1544598-f0002.jpg]</td>\n",
       "      <td>5.100979e-07</td>\n",
       "      <td>3</td>\n",
       "      <td>7</td>\n",
       "      <td>45</td>\n",
       "      <td>30108</td>\n",
       "      <td>{NCBIGene:5156, NCBIGene:25, NCBIGene:1399}</td>\n",
       "      <td>{NCBIGene:5601, NCBIGene:596, NCBIGene:6777, N...</td>\n",
       "      <td>{NCBIGene:5156, NCBIGene:25, NCBIGene:1399}</td>\n",
       "      <td>3</td>\n",
       "      <td>19277</td>\n",
       "      <td>10</td>\n",
       "      <td>17</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>3</td>\n",
       "      <td>PMC4748076__zns9991682760008.jpg</td>\n",
       "      <td>2.320724e-04</td>\n",
       "      <td>[PMC3412280__srep00557-f2.jpg, PMC3605878__bph...</td>\n",
       "      <td>4.640986e-04</td>\n",
       "      <td>1</td>\n",
       "      <td>6</td>\n",
       "      <td>0</td>\n",
       "      <td>30156</td>\n",
       "      <td>{NCBIGene:5243}</td>\n",
       "      <td>{NCBIGene:5243}</td>\n",
       "      <td>{NCBIGene:5243}</td>\n",
       "      <td>1</td>\n",
       "      <td>19276</td>\n",
       "      <td>7</td>\n",
       "      <td>18</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>4</td>\n",
       "      <td>PMC3412280__srep00557-f2.jpg</td>\n",
       "      <td>3.978054e-04</td>\n",
       "      <td>[PMC3102743__pgen.1002072.g007.jpg, PMC4197584...</td>\n",
       "      <td>7.954790e-04</td>\n",
       "      <td>1</td>\n",
       "      <td>5</td>\n",
       "      <td>1</td>\n",
       "      <td>30156</td>\n",
       "      <td>{NCBIGene:2875}</td>\n",
       "      <td>{MESH:D005227, NCBIGene:2875}</td>\n",
       "      <td>{NCBIGene:64321, NCBIGene:2875}</td>\n",
       "      <td>2</td>\n",
       "      <td>19275</td>\n",
       "      <td>6</td>\n",
       "      <td>20</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>4</td>\n",
       "      <td>PMC7200694__41598_2020_64503_Fig6_HTML.jpg</td>\n",
       "      <td>3.978054e-04</td>\n",
       "      <td>[PMC3102743__pgen.1002072.g007.jpg, PMC4197584...</td>\n",
       "      <td>7.954790e-04</td>\n",
       "      <td>1</td>\n",
       "      <td>5</td>\n",
       "      <td>1</td>\n",
       "      <td>30156</td>\n",
       "      <td>{NCBIGene:64321}</td>\n",
       "      <td>{NCBIGene:64321, MESH:D005978}</td>\n",
       "      <td>{NCBIGene:64321, NCBIGene:2875}</td>\n",
       "      <td>2</td>\n",
       "      <td>19275</td>\n",
       "      <td>6</td>\n",
       "      <td>20</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20</th>\n",
       "      <td>5</td>\n",
       "      <td>PMC6530120__OMCL2019-7417561.009.jpg</td>\n",
       "      <td>7.954790e-04</td>\n",
       "      <td>[PMC7600041__viruses-12-01110-g008.jpg]</td>\n",
       "      <td>1.325535e-03</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>5</td>\n",
       "      <td>30154</td>\n",
       "      <td>{NCBIGene:9467}</td>\n",
       "      <td>{NCBIGene:9467, NCBIGene:5601, NCBIGene:5602, ...</td>\n",
       "      <td>{NCBIGene:9467}</td>\n",
       "      <td>1</td>\n",
       "      <td>19273</td>\n",
       "      <td>4</td>\n",
       "      <td>21</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21</th>\n",
       "      <td>6</td>\n",
       "      <td>PMC7600041__viruses-12-01110-g008.jpg</td>\n",
       "      <td>9.942993e-04</td>\n",
       "      <td>[PMC7023894__nihms-1556086-f0004.jpg]</td>\n",
       "      <td>1.292460e-03</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>9</td>\n",
       "      <td>30151</td>\n",
       "      <td>{NCBIGene:81608}</td>\n",
       "      <td>{NCBIGene:5595, NCBIGene:10000, NCBIGene:5594,...</td>\n",
       "      <td>{NCBIGene:81608}</td>\n",
       "      <td>1</td>\n",
       "      <td>19272</td>\n",
       "      <td>3</td>\n",
       "      <td>22</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22</th>\n",
       "      <td>7</td>\n",
       "      <td>PMC7023894__nihms-1556086-f0004.jpg</td>\n",
       "      <td>8.618118e-04</td>\n",
       "      <td>[PMC4346735__zh00051576930001.jpg]</td>\n",
       "      <td>1.259446e-03</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>12</td>\n",
       "      <td>30149</td>\n",
       "      <td>{MESH:D001249}</td>\n",
       "      <td>{NCBIGene:58, NCBIGene:387, MESH:D001249, NCBI...</td>\n",
       "      <td>{MESH:D001249}</td>\n",
       "      <td>1</td>\n",
       "      <td>19271</td>\n",
       "      <td>2</td>\n",
       "      <td>23</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    iteration                                   figure_id       p_value  \\\n",
       "0           0                    PMC1767116__21889.f3.jpg  4.454811e-09   \n",
       "1           0                    PMC2724780__1721fig1.jpg  4.454811e-09   \n",
       "2           0            PMC2776288__ajtr0001-0001-f2.jpg  4.454811e-09   \n",
       "3           0              PMC3173800__emboj2011307f2.jpg  4.454811e-09   \n",
       "4           0            PMC3277752__zjw0011240500002.jpg  4.454811e-09   \n",
       "5           0          PMC3437743__nihms-400038-f0001.jpg  4.454811e-09   \n",
       "6           0          PMC5086377__DM2016-9602472.001.jpg  4.454811e-09   \n",
       "7           0               PMC5382109__nihms824859f3.jpg  4.454811e-09   \n",
       "8           0              PMC5386521__cddis2017115f1.jpg  4.454811e-09   \n",
       "9           0          PMC5979467__ijms-19-00971-g002.jpg  4.454811e-09   \n",
       "10          0         PMC7075948__fcell-08-00143-g002.jpg  4.454811e-09   \n",
       "11          0        PMC7174937__OMCL2020-8787156.009.jpg  4.454811e-09   \n",
       "12          0          PMC7432057__ijms-21-05242-g003.jpg  4.454811e-09   \n",
       "13          0         PMC7499322__SCI2020-8844730.003.jpg  4.454811e-09   \n",
       "14          0            PMC8040309__ijbsv17p1079g003.jpg  4.454811e-09   \n",
       "15          1        PMC5078657__OMCL2016-9324085.002.jpg  1.630866e-08   \n",
       "16          2                PMC7604619__1052524.fig3.jpg  4.502922e-07   \n",
       "17          3            PMC4748076__zns9991682760008.jpg  2.320724e-04   \n",
       "18          4                PMC3412280__srep00557-f2.jpg  3.978054e-04   \n",
       "19          4  PMC7200694__41598_2020_64503_Fig6_HTML.jpg  3.978054e-04   \n",
       "20          5        PMC6530120__OMCL2019-7417561.009.jpg  7.954790e-04   \n",
       "21          6       PMC7600041__viruses-12-01110-g008.jpg  9.942993e-04   \n",
       "22          7         PMC7023894__nihms-1556086-f0004.jpg  8.618118e-04   \n",
       "\n",
       "                                 next_best_figure_ids  next_best_p_value  \\\n",
       "0   [PMC2940266__nihms99348f1.jpg, PMC3240145__ar5...       5.194659e-09   \n",
       "1   [PMC2940266__nihms99348f1.jpg, PMC3240145__ar5...       5.194659e-09   \n",
       "2   [PMC2940266__nihms99348f1.jpg, PMC3240145__ar5...       5.194659e-09   \n",
       "3   [PMC2940266__nihms99348f1.jpg, PMC3240145__ar5...       5.194659e-09   \n",
       "4   [PMC2940266__nihms99348f1.jpg, PMC3240145__ar5...       5.194659e-09   \n",
       "5   [PMC2940266__nihms99348f1.jpg, PMC3240145__ar5...       5.194659e-09   \n",
       "6   [PMC2940266__nihms99348f1.jpg, PMC3240145__ar5...       5.194659e-09   \n",
       "7   [PMC2940266__nihms99348f1.jpg, PMC3240145__ar5...       5.194659e-09   \n",
       "8   [PMC2940266__nihms99348f1.jpg, PMC3240145__ar5...       5.194659e-09   \n",
       "9   [PMC2940266__nihms99348f1.jpg, PMC3240145__ar5...       5.194659e-09   \n",
       "10  [PMC2940266__nihms99348f1.jpg, PMC3240145__ar5...       5.194659e-09   \n",
       "11  [PMC2940266__nihms99348f1.jpg, PMC3240145__ar5...       5.194659e-09   \n",
       "12  [PMC2940266__nihms99348f1.jpg, PMC3240145__ar5...       5.194659e-09   \n",
       "13  [PMC2940266__nihms99348f1.jpg, PMC3240145__ar5...       5.194659e-09   \n",
       "14  [PMC2940266__nihms99348f1.jpg, PMC3240145__ar5...       5.194659e-09   \n",
       "15  [PMC5226821__pcbi.1005287.g013.jpg, PMC6513836...       2.801800e-08   \n",
       "16              [PMC6927537__nihms-1544598-f0002.jpg]       5.100979e-07   \n",
       "17  [PMC3412280__srep00557-f2.jpg, PMC3605878__bph...       4.640986e-04   \n",
       "18  [PMC3102743__pgen.1002072.g007.jpg, PMC4197584...       7.954790e-04   \n",
       "19  [PMC3102743__pgen.1002072.g007.jpg, PMC4197584...       7.954790e-04   \n",
       "20            [PMC7600041__viruses-12-01110-g008.jpg]       1.325535e-03   \n",
       "21              [PMC7023894__nihms-1556086-f0004.jpg]       1.292460e-03   \n",
       "22                 [PMC4346735__zh00051576930001.jpg]       1.259446e-03   \n",
       "\n",
       "    yes_trapi_results_yes_figure  yes_trapi_results_no_figure  \\\n",
       "0                              5                           18   \n",
       "1                              4                           19   \n",
       "2                              4                           19   \n",
       "3                              4                           19   \n",
       "4                              4                           19   \n",
       "5                              4                           19   \n",
       "6                              4                           19   \n",
       "7                              4                           19   \n",
       "8                              4                           19   \n",
       "9                              4                           19   \n",
       "10                             4                           19   \n",
       "11                             4                           19   \n",
       "12                             4                           19   \n",
       "13                             4                           19   \n",
       "14                             5                           18   \n",
       "15                             5                           10   \n",
       "16                             3                            7   \n",
       "17                             1                            6   \n",
       "18                             1                            5   \n",
       "19                             1                            5   \n",
       "20                             1                            3   \n",
       "21                             1                            2   \n",
       "22                             1                            1   \n",
       "\n",
       "    no_trapi_results_yes_figure  no_trapi_results_no_figure  \\\n",
       "0                            22                       30118   \n",
       "1                            23                       30117   \n",
       "2                            23                       30117   \n",
       "3                            23                       30117   \n",
       "4                            23                       30117   \n",
       "5                            23                       30117   \n",
       "6                            23                       30117   \n",
       "7                            23                       30117   \n",
       "8                            23                       30117   \n",
       "9                            23                       30117   \n",
       "10                           23                       30117   \n",
       "11                           23                       30117   \n",
       "12                           23                       30117   \n",
       "13                           23                       30117   \n",
       "14                           22                       30118   \n",
       "15                          167                       29981   \n",
       "16                           45                       30108   \n",
       "17                            0                       30156   \n",
       "18                            1                       30156   \n",
       "19                            1                       30156   \n",
       "20                            5                       30154   \n",
       "21                            9                       30151   \n",
       "22                           12                       30149   \n",
       "\n",
       "                       figure_curies_in_trapi_results  \\\n",
       "0   {NCBIGene:841, NCBIGene:836, NCBIGene:842, NCB...   \n",
       "1   {NCBIGene:836, NCBIGene:841, NCBIGene:207, NCB...   \n",
       "2   {NCBIGene:836, NCBIGene:841, NCBIGene:56616, N...   \n",
       "3   {NCBIGene:836, NCBIGene:841, NCBIGene:56616, N...   \n",
       "4   {NCBIGene:836, NCBIGene:841, NCBIGene:56616, N...   \n",
       "5   {NCBIGene:836, NCBIGene:841, NCBIGene:56616, N...   \n",
       "6   {NCBIGene:836, NCBIGene:841, NCBIGene:842, NCB...   \n",
       "7   {NCBIGene:836, NCBIGene:841, NCBIGene:56616, N...   \n",
       "8   {NCBIGene:836, NCBIGene:841, NCBIGene:207, NCB...   \n",
       "9   {NCBIGene:836, NCBIGene:841, NCBIGene:56616, N...   \n",
       "10  {NCBIGene:836, NCBIGene:841, NCBIGene:207, NCB...   \n",
       "11  {NCBIGene:836, NCBIGene:841, NCBIGene:207, NCB...   \n",
       "12  {NCBIGene:836, NCBIGene:841, NCBIGene:207, NCB...   \n",
       "13  {NCBIGene:836, NCBIGene:841, MESH:D007938, NCB...   \n",
       "14  {NCBIGene:142, NCBIGene:841, NCBIGene:836, NCB...   \n",
       "15  {NCBIGene:9429, NCBIGene:1021, NCBIGene:7422, ...   \n",
       "16        {NCBIGene:5156, NCBIGene:25, NCBIGene:1399}   \n",
       "17                                    {NCBIGene:5243}   \n",
       "18                                    {NCBIGene:2875}   \n",
       "19                                   {NCBIGene:64321}   \n",
       "20                                    {NCBIGene:9467}   \n",
       "21                                   {NCBIGene:81608}   \n",
       "22                                     {MESH:D001249}   \n",
       "\n",
       "                                            curie_set  \\\n",
       "0   {NCBIGene:317, NCBIGene:10000, NCBIGene:596, N...   \n",
       "1   {NCBIGene:317, NCBIGene:10000, NCBIGene:596, N...   \n",
       "2   {NCBIGene:839, NCBIGene:838, NCBIGene:7124, NC...   \n",
       "3   {NCBIGene:317, NCBIGene:356, NCBIGene:596, NCB...   \n",
       "4   {NCBIGene:317, NCBIGene:27113, NCBIGene:839, N...   \n",
       "5   {NCBIGene:9131, NCBIGene:317, NCBIGene:356, NC...   \n",
       "6   {NCBIGene:2353, NCBIGene:959, NCBIGene:839, NC...   \n",
       "7   {NCBIGene:8837, NCBIGene:317, NCBIGene:27113, ...   \n",
       "8   {NCBIGene:1876, NCBIGene:1871, NCBIGene:10000,...   \n",
       "9   {NCBIGene:7514, NCBIGene:317, NCBIGene:1431, N...   \n",
       "10  {NCBIGene:10000, NCBIGene:839, NCBIGene:838, N...   \n",
       "11  {NCBIGene:10000, NCBIGene:839, NCBIGene:838, N...   \n",
       "12  {NCBIGene:5601, NCBIGene:6774, NCBIGene:10000,...   \n",
       "13  {MESH:D005909, MESH:D009369, MESH:C051905, MES...   \n",
       "14  {MESH:D009369, NCBIGene:6774, NCBIGene:10000, ...   \n",
       "15  {NCBIGene:4322, NCBIGene:7481, NCBIGene:10000,...   \n",
       "16  {NCBIGene:5601, NCBIGene:596, NCBIGene:6777, N...   \n",
       "17                                    {NCBIGene:5243}   \n",
       "18                      {MESH:D005227, NCBIGene:2875}   \n",
       "19                     {NCBIGene:64321, MESH:D005978}   \n",
       "20  {NCBIGene:9467, NCBIGene:5601, NCBIGene:5602, ...   \n",
       "21  {NCBIGene:5595, NCBIGene:10000, NCBIGene:5594,...   \n",
       "22  {NCBIGene:58, NCBIGene:387, MESH:D001249, NCBI...   \n",
       "\n",
       "                                    curies_to_exclude  len_curies_to_exclude  \\\n",
       "0   {NCBIGene:142, NCBIGene:841, MESH:D007938, NCB...                      8   \n",
       "1   {NCBIGene:142, NCBIGene:841, MESH:D007938, NCB...                      8   \n",
       "2   {NCBIGene:142, NCBIGene:841, MESH:D007938, NCB...                      8   \n",
       "3   {NCBIGene:142, NCBIGene:841, MESH:D007938, NCB...                      8   \n",
       "4   {NCBIGene:142, NCBIGene:841, MESH:D007938, NCB...                      8   \n",
       "5   {NCBIGene:142, NCBIGene:841, MESH:D007938, NCB...                      8   \n",
       "6   {NCBIGene:142, NCBIGene:841, MESH:D007938, NCB...                      8   \n",
       "7   {NCBIGene:142, NCBIGene:841, MESH:D007938, NCB...                      8   \n",
       "8   {NCBIGene:142, NCBIGene:841, MESH:D007938, NCB...                      8   \n",
       "9   {NCBIGene:142, NCBIGene:841, MESH:D007938, NCB...                      8   \n",
       "10  {NCBIGene:142, NCBIGene:841, MESH:D007938, NCB...                      8   \n",
       "11  {NCBIGene:142, NCBIGene:841, MESH:D007938, NCB...                      8   \n",
       "12  {NCBIGene:142, NCBIGene:841, MESH:D007938, NCB...                      8   \n",
       "13  {NCBIGene:142, NCBIGene:841, MESH:D007938, NCB...                      8   \n",
       "14  {NCBIGene:142, NCBIGene:841, MESH:D007938, NCB...                      8   \n",
       "15  {NCBIGene:1021, NCBIGene:7422, NCBIGene:3725, ...                      5   \n",
       "16        {NCBIGene:5156, NCBIGene:25, NCBIGene:1399}                      3   \n",
       "17                                    {NCBIGene:5243}                      1   \n",
       "18                    {NCBIGene:64321, NCBIGene:2875}                      2   \n",
       "19                    {NCBIGene:64321, NCBIGene:2875}                      2   \n",
       "20                                    {NCBIGene:9467}                      1   \n",
       "21                                   {NCBIGene:81608}                      1   \n",
       "22                                     {MESH:D001249}                      1   \n",
       "\n",
       "    remaining_figure_count  remaining_trapi_curie_count  \\\n",
       "0                    19293                           23   \n",
       "1                    19293                           23   \n",
       "2                    19293                           23   \n",
       "3                    19293                           23   \n",
       "4                    19293                           23   \n",
       "5                    19293                           23   \n",
       "6                    19293                           23   \n",
       "7                    19293                           23   \n",
       "8                    19293                           23   \n",
       "9                    19293                           23   \n",
       "10                   19293                           23   \n",
       "11                   19293                           23   \n",
       "12                   19293                           23   \n",
       "13                   19293                           23   \n",
       "14                   19293                           23   \n",
       "15                   19278                           15   \n",
       "16                   19277                           10   \n",
       "17                   19276                            7   \n",
       "18                   19275                            6   \n",
       "19                   19275                            6   \n",
       "20                   19273                            4   \n",
       "21                   19272                            3   \n",
       "22                   19271                            2   \n",
       "\n",
       "    cluster_figure_id_count  \n",
       "0                        15  \n",
       "1                        15  \n",
       "2                        15  \n",
       "3                        15  \n",
       "4                        15  \n",
       "5                        15  \n",
       "6                        15  \n",
       "7                        15  \n",
       "8                        15  \n",
       "9                        15  \n",
       "10                       15  \n",
       "11                       15  \n",
       "12                       15  \n",
       "13                       15  \n",
       "14                       15  \n",
       "15                       16  \n",
       "16                       17  \n",
       "17                       18  \n",
       "18                       20  \n",
       "19                       20  \n",
       "20                       21  \n",
       "21                       22  \n",
       "22                       23  "
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Importing reduce for \n",
    "# rolling computations\n",
    "from functools import reduce\n",
    "  \n",
    "def set_union(series):\n",
    "    return reduce(lambda x, y: x | y, series)\n",
    "\n",
    "def create_set(series):\n",
    "    return set(series.tolist())\n",
    "\n",
    "\n",
    "# TODO: remove cluster_figure_data and its associated DF.\n",
    "# It's just a temporary thing to show what's happening inside.\n",
    "cluster_figure_data = []\n",
    "def get_next_cluster_figures(\n",
    "    category_matched_pfocr_curie_count,\n",
    "    remaining_figures_df,\n",
    "    remaining_trapi_curie_count,\n",
    "    required_curies,\n",
    "    iteration_limit=15,\n",
    "    i=0,\n",
    "    cluster_figure_ids=[],\n",
    "):\n",
    "    remaining_figure_count = len(\n",
    "        remaining_figures_df\n",
    "    )\n",
    "\n",
    "    if (\n",
    "        i >= iteration_limit\n",
    "    ) or (\n",
    "        len(remaining_trapi_curie_count) == 0\n",
    "    ) or (\n",
    "        remaining_figure_count == 0\n",
    "    ):\n",
    "        return cluster_figure_ids\n",
    "    \n",
    "    remaining_figures_df[\"figure_curie_count\"] = remaining_figures_df[\"curie_set\"].map(len)\n",
    "\n",
    "    remaining_figures_df[\"figure_curies_in_trapi_results\"] = remaining_figures_df[\"curie_set\"].map(\n",
    "        lambda curie_set: remaining_trapi_curie_count.intersection(\n",
    "            set(curie_set)\n",
    "        )\n",
    "    )\n",
    "\n",
    "    # number of CURIEs in both TRAPI results and figure\n",
    "    remaining_figures_df[\"yes_trapi_results_yes_figure\"] = remaining_figures_df[\n",
    "        \"figure_curies_in_trapi_results\"\n",
    "    ].map(len)\n",
    "\n",
    "    # number of CURIEs in TRAPI results but not in figure\n",
    "    remaining_figures_df[\"yes_trapi_results_no_figure\"] = (\n",
    "        len(remaining_trapi_curie_count) -\n",
    "        remaining_figures_df[\"yes_trapi_results_yes_figure\"]\n",
    "    )\n",
    "\n",
    "    # number of CURIEs not in TRAPI results but in figure\n",
    "    remaining_figures_df[\"no_trapi_results_yes_figure\"] = (\n",
    "        remaining_figures_df[\"figure_curie_count\"] -\n",
    "        remaining_figures_df[\"yes_trapi_results_yes_figure\"]\n",
    "    )\n",
    "\n",
    "    # number of CURIEs not in TRAPI results and not in figure,\n",
    "    # ie., number of unique CURIEs only in other figures\n",
    "    remaining_figures_df[\"no_trapi_results_no_figure\"] = (\n",
    "        category_matched_pfocr_curie_count -\n",
    "        len(remaining_trapi_curie_count) -\n",
    "        remaining_figures_df[\"no_trapi_results_yes_figure\"]\n",
    "    )\n",
    "\n",
    "    # see https://stackoverflow.com/a/58661068/5354298\n",
    "    _, _, twosided = pvalue_npy(\n",
    "        remaining_figures_df[\"yes_trapi_results_yes_figure\"].to_numpy('uint'),\n",
    "        remaining_figures_df[\"yes_trapi_results_no_figure\"].to_numpy('uint'),\n",
    "        remaining_figures_df[\"no_trapi_results_yes_figure\"].to_numpy('uint'),\n",
    "        remaining_figures_df[\"no_trapi_results_no_figure\"].to_numpy('uint'),\n",
    "    )\n",
    "\n",
    "    remaining_figures_df[\"p_value\"] = pd.Series(twosided, index=remaining_figures_df.index)\n",
    "    remaining_figures_df[\"p_value\"].sort_values()\n",
    "    \n",
    "    min_df = remaining_figures_df[\n",
    "        remaining_figures_df[\"p_value\"] == remaining_figures_df[\"p_value\"].min()\n",
    "    ]\n",
    "    \n",
    "    if required_curies:\n",
    "        # TODO: it might make sense to keep prioritizing the required CURIEs, but\n",
    "        #       it's not clear whether this is always what we want.\n",
    "        #       In some cases, we could obscure a different set of results that may\n",
    "        #       be valuable but not because of the overlap with the required CURIEs.\n",
    "        curies_to_exclude = set(\n",
    "            min_df[\"figure_curies_in_trapi_results\"].agg(set_union)\n",
    "        ) - required_curies\n",
    "    else:\n",
    "        curies_to_exclude = set(min_df[\"figure_curies_in_trapi_results\"].agg(set_union))\n",
    "        \n",
    "    if len(curies_to_exclude - required_curies) == 0:\n",
    "        # On this iteration, we failed to match any new CURIE(s), other than\n",
    "        # possibly just the require CURIEs.\n",
    "        return cluster_figure_ids\n",
    "        \n",
    "    new_cluster_figure_ids = min_df.index.tolist()\n",
    "    cluster_figure_ids.extend(new_cluster_figure_ids)\n",
    "    \n",
    "    next_min_df_score = remaining_figures_df[\n",
    "        remaining_figures_df[\"p_value\"] > remaining_figures_df[\"p_value\"].min()\n",
    "    ][\"p_value\"].min()\n",
    "    next_min_df = remaining_figures_df[remaining_figures_df[\"p_value\"] == next_min_df_score]\n",
    "    \n",
    "    for cluster_figure_id, min_row in min_df.iterrows():\n",
    "        cluster_figure_data.append({\n",
    "            \"iteration\": i,\n",
    "            \"figure_id\": cluster_figure_id,\n",
    "            \"p_value\": min_row[\"p_value\"],\n",
    "            \"next_best_figure_ids\": next_min_df.index.tolist(),\n",
    "            \"next_best_p_value\": next_min_df[\"p_value\"].min(),\n",
    "            \"yes_trapi_results_yes_figure\": min_row[\"yes_trapi_results_yes_figure\"],\n",
    "            \"yes_trapi_results_no_figure\": min_row[\"yes_trapi_results_no_figure\"],\n",
    "            \"no_trapi_results_yes_figure\": min_row[\"no_trapi_results_yes_figure\"],\n",
    "            \"no_trapi_results_no_figure\": min_row[\"no_trapi_results_no_figure\"],\n",
    "            \"figure_curies_in_trapi_results\": min_row[\"figure_curies_in_trapi_results\"],\n",
    "            \"curie_set\": min_row[\"curie_set\"],\n",
    "            \"curies_to_exclude\": curies_to_exclude,\n",
    "            \"len_curies_to_exclude\": len(curies_to_exclude),\n",
    "            \"remaining_figure_count\": remaining_figure_count,\n",
    "            \"remaining_trapi_curie_count\": len(remaining_trapi_curie_count),\n",
    "            \"cluster_figure_id_count\": len(cluster_figure_ids),\n",
    "        })\n",
    "    \n",
    "    # for next iteration, exclude figures we've already added to cluster figures\n",
    "    next_remaining_figures_df = remaining_figures_df[\n",
    "        (~remaining_figures_df.index.isin(\n",
    "            cluster_figure_ids\n",
    "        ))\n",
    "    ]\n",
    "    \n",
    "    print(\n",
    "        f'iteration {i:>2}: figures: {len(new_cluster_figure_ids):>2}, CURIEs: {len(curies_to_exclude):>2}'\n",
    "    )\n",
    "    \n",
    "    return get_next_cluster_figures(\n",
    "        category_matched_pfocr_curie_count,\n",
    "        next_remaining_figures_df.reset_index().set_index(\"figure_id\"),\n",
    "        remaining_trapi_curie_count - curies_to_exclude,\n",
    "        required_curies,\n",
    "        iteration_limit,\n",
    "        i + 1,\n",
    "        cluster_figure_ids,\n",
    "    )\n",
    "\n",
    "category_matched_figure_curie_df = figure_curie_df[figure_curie_df[\"category\"].isin(\n",
    "    list(relevant_pfocr_categories)\n",
    ")]\n",
    "category_matched_pfocr_curie_count = len(set(\n",
    "    category_matched_figure_curie_df[\"curie\"].drop_duplicates()\n",
    "))\n",
    "print(f'category_matched_pfocr_curie_count: {category_matched_pfocr_curie_count}')\n",
    "\n",
    "category_matched_figures_df = category_matched_figure_curie_df[\n",
    "    [\"figure_id\", \"curie\"]\n",
    "].groupby(\"figure_id\").agg(\n",
    "    create_set\n",
    ").rename(columns={\n",
    "    \"curie\": \"curie_set\"\n",
    "})\n",
    "\n",
    "category_matched_figures_df[\"figure_curies_in_trapi_results_count\"] = category_matched_figures_df[\n",
    "    \"curie_set\"\n",
    "].map(\n",
    "    lambda curie_set: len(trapi_results_unified_curies.intersection(\n",
    "        set(curie_set)\n",
    "    ))\n",
    ")\n",
    "\n",
    "overlapping_category_matched_figures_df = category_matched_figures_df[\n",
    "    category_matched_figures_df[\"figure_curies_in_trapi_results_count\"] > 0\n",
    "]\n",
    "\n",
    "# If you want to require that cluster figures have specific CURIEs,\n",
    "# such as the explicitly user-specified CURIEs, set them here:\n",
    "required_curies = set()\n",
    "# required_curies = user_specified_ids\n",
    "if required_curies:\n",
    "    overlapping_category_matched_figures_df = overlapping_category_matched_figures_df[\n",
    "        overlapping_category_matched_figures_df[\"curie_set\"].map(\n",
    "            # require all of them\n",
    "            # lambda curie_set: len(curie_set.intersection(required_curies)) == len(required_curies)\n",
    "            # require at least one of them\n",
    "            lambda curie_set: len(curie_set.intersection(required_curies)) > 0\n",
    "        )\n",
    "    ]\n",
    "\n",
    "cluster_figure_ids_out = get_next_cluster_figures(\n",
    "    category_matched_pfocr_curie_count,\n",
    "    overlapping_category_matched_figures_df.reset_index().set_index(\"figure_id\"),\n",
    "    trapi_results_unified_curies,\n",
    "    required_curies,\n",
    ")\n",
    "print(f'cluster figure count: {len(cluster_figure_ids_out)}')\n",
    "cluster_figure_df = pd.DataFrame.from_records(cluster_figure_data)\n",
    "cluster_figure_df"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Cluster BTE TRAPI Results by Selected PFOCR Figures (Cluster Figures)\n",
    "\n",
    "See details in write-up at the top."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [],
   "source": [
    "figures_df = category_matched_figure_curie_df[\n",
    "    category_matched_figure_curie_df[\"figure_id\"].isin(set(cluster_figure_df[\"figure_id\"]))\n",
    "][[\"figure_id\", \"curie\"]].groupby(\"figure_id\").agg(\n",
    "    create_set\n",
    ").rename(columns={\n",
    "    \"curie\": \"figure_curie_set\"\n",
    "}).join(\n",
    "    figure_metadata_df\n",
    ").reset_index()\n",
    "\n",
    "figures_df[\"figure_curie_count\"] = figures_df[\"figure_curie_set\"].map(len)\n",
    "\n",
    "figures_df['crossjoin_key'] = 0\n",
    "trapi_results_df['crossjoin_key'] = 0\n",
    "\n",
    "trapi_results_with_figures_df = trapi_results_df.merge(\n",
    "    figures_df, on='crossjoin_key', how='outer'\n",
    ").rename(columns={\n",
    "    \"unified_curie_set\": \"trapi_result_curie_set\"\n",
    "})\n",
    "\n",
    "trapi_results_with_figures_df[\"common_curie_set\"] = trapi_results_with_figures_df.apply(\n",
    "    lambda r: r[\"trapi_result_curie_set\"] & r[\"figure_curie_set\"], axis=1\n",
    ")\n",
    "trapi_results_with_figures_df\n",
    "# number of CURIEs in both TRAPI result and figure\n",
    "# trapi_curies_in_figure\n",
    "# x\n",
    "# Selected & Having the property\n",
    "trapi_results_with_figures_df[\"yes_trapi_result_yes_figure\"] = (\n",
    "    trapi_results_with_figures_df[\"common_curie_set\"].map(len)\n",
    ")\n",
    "\n",
    "# number of CURIEs in TRAPI result but not in figure\n",
    "# q_node_id_count - trapi_curies_in_figure\n",
    "# n - x\n",
    "# Selected & Not Having the property\n",
    "trapi_results_with_figures_df[\"yes_trapi_result_no_figure\"] = (\n",
    "    len(q_node_ids) - trapi_results_with_figures_df[\"yes_trapi_result_yes_figure\"]\n",
    ")\n",
    "\n",
    "# number of CURIEs not in TRAPI result but in figure\n",
    "# curies_in_figure - trapi_curies_in_figure\n",
    "# N - x\n",
    "# Not Selected & Having the property\n",
    "trapi_results_with_figures_df[\"no_trapi_result_yes_figure\"] = (\n",
    "    trapi_results_with_figures_df[\"figure_curie_set\"].map(len) - \n",
    "    trapi_results_with_figures_df[\"yes_trapi_result_yes_figure\"]\n",
    ")\n",
    "\n",
    "# number of CURIEs not in TRAPI result and not in figure,\n",
    "# ie., number of unique CURIEs only in other figures\n",
    "# 28735 - curies_in_figure - q_node_id_count + trapi_curies_in_figure\n",
    "# M - (n + N) + x\n",
    "# Not Selected & Not Having the property\n",
    "trapi_results_with_figures_df[\"no_trapi_result_no_figure\"] = (\n",
    "    category_matched_pfocr_curie_count -\n",
    "    trapi_results_with_figures_df[\"figure_curie_set\"].map(len) -\n",
    "    len(q_node_ids) +\n",
    "    trapi_results_with_figures_df[\"yes_trapi_result_yes_figure\"]\n",
    ")\n",
    "\n",
    "# see https://stackoverflow.com/a/58661068/5354298\n",
    "_, _, twosided = pvalue_npy(\n",
    "    trapi_results_with_figures_df[\"yes_trapi_result_yes_figure\"].to_numpy('uint'),\n",
    "    trapi_results_with_figures_df[\"yes_trapi_result_no_figure\"].to_numpy('uint'),\n",
    "    trapi_results_with_figures_df[\"no_trapi_result_yes_figure\"].to_numpy('uint'),\n",
    "    trapi_results_with_figures_df[\"no_trapi_result_no_figure\"].to_numpy('uint'),\n",
    ")\n",
    "\n",
    "trapi_results_with_figures_df[\"p_value\"] = pd.Series(twosided, index=trapi_results_with_figures_df.index)\n",
    "trapi_results_with_figures_df[\"p_value\"].sort_values()\n",
    "\n",
    "# Needed in order to use the TRAPI result CURIEs as a key. A Python Set is not hashable.\n",
    "trapi_results_with_figures_df[\"trapi_result_curies_key\"] = trapi_results_with_figures_df[\n",
    "    \"trapi_result_curie_set\"\n",
    "].map(\n",
    "    lambda x: tuple(sorted(x))\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(array([ 381.,    0.,    0.,    0.,    0.,    0.,    0.,    0.,    0.,\n",
       "        1712.]),\n",
       " array([2.31357533e-06, 1.00002082e-01, 2.00001851e-01, 3.00001620e-01,\n",
       "        4.00001388e-01, 5.00001157e-01, 6.00000925e-01, 7.00000694e-01,\n",
       "        8.00000463e-01, 9.00000231e-01, 1.00000000e+00]),\n",
       " <BarContainer object of 10 artists>)"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.hist(trapi_results_with_figures_df[\"p_value\"])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## View TRAPI Results by PFOCR Figure"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "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>n0</th>\n",
       "      <th>e0</th>\n",
       "      <th>n1</th>\n",
       "      <th>e1</th>\n",
       "      <th>n2</th>\n",
       "      <th>n0_original_curie</th>\n",
       "      <th>n0_unified_curie</th>\n",
       "      <th>n1_original_curie</th>\n",
       "      <th>n1_unified_curie</th>\n",
       "      <th>n2_original_curie</th>\n",
       "      <th>...</th>\n",
       "      <th>figure_url</th>\n",
       "      <th>figure_title</th>\n",
       "      <th>figure_curie_count</th>\n",
       "      <th>common_curie_set</th>\n",
       "      <th>yes_trapi_result_yes_figure</th>\n",
       "      <th>yes_trapi_result_no_figure</th>\n",
       "      <th>no_trapi_result_yes_figure</th>\n",
       "      <th>no_trapi_result_no_figure</th>\n",
       "      <th>p_value</th>\n",
       "      <th>trapi_result_curies_key</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1330</th>\n",
       "      <td>Imatinib mesylate</td>\n",
       "      <td>increases_activity_of</td>\n",
       "      <td>CASP3</td>\n",
       "      <td>contributes_to</td>\n",
       "      <td>leukemia</td>\n",
       "      <td>PUBCHEM.COMPOUND:123596</td>\n",
       "      <td>MESH:D000068877</td>\n",
       "      <td>NCBIGene:836</td>\n",
       "      <td>NCBIGene:836</td>\n",
       "      <td>MONDO:0005059</td>\n",
       "      <td>...</td>\n",
       "      <td>https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7...</td>\n",
       "      <td>Inhibitory roles of MSC-derived exosomes in tu...</td>\n",
       "      <td>27</td>\n",
       "      <td>{MESH:D007938, NCBIGene:836}</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>25</td>\n",
       "      <td>30135</td>\n",
       "      <td>0.000002</td>\n",
       "      <td>(MESH:D000068877, MESH:D007938, NCBIGene:836)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2089</th>\n",
       "      <td>Imatinib mesylate</td>\n",
       "      <td>increases_activity_of</td>\n",
       "      <td>CASP8</td>\n",
       "      <td>correlated_with</td>\n",
       "      <td>leukemia</td>\n",
       "      <td>PUBCHEM.COMPOUND:123596</td>\n",
       "      <td>MESH:D000068877</td>\n",
       "      <td>NCBIGene:841</td>\n",
       "      <td>NCBIGene:841</td>\n",
       "      <td>MONDO:0005059</td>\n",
       "      <td>...</td>\n",
       "      <td>https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7...</td>\n",
       "      <td>Inhibitory roles of MSC-derived exosomes in tu...</td>\n",
       "      <td>27</td>\n",
       "      <td>{NCBIGene:841, MESH:D007938}</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>25</td>\n",
       "      <td>30135</td>\n",
       "      <td>0.000002</td>\n",
       "      <td>(MESH:D000068877, MESH:D007938, NCBIGene:841)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1307</th>\n",
       "      <td>Imatinib mesylate</td>\n",
       "      <td>increases_activity_of</td>\n",
       "      <td>CASP3</td>\n",
       "      <td>correlated_with</td>\n",
       "      <td>leukemia</td>\n",
       "      <td>PUBCHEM.COMPOUND:123596</td>\n",
       "      <td>MESH:D000068877</td>\n",
       "      <td>NCBIGene:836</td>\n",
       "      <td>NCBIGene:836</td>\n",
       "      <td>MONDO:0005059</td>\n",
       "      <td>...</td>\n",
       "      <td>https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7...</td>\n",
       "      <td>Inhibitory roles of MSC-derived exosomes in tu...</td>\n",
       "      <td>27</td>\n",
       "      <td>{MESH:D007938, NCBIGene:836}</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>25</td>\n",
       "      <td>30135</td>\n",
       "      <td>0.000002</td>\n",
       "      <td>(MESH:D000068877, MESH:D007938, NCBIGene:836)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1353</th>\n",
       "      <td>Imatinib mesylate</td>\n",
       "      <td>increases_degradation_of</td>\n",
       "      <td>CASP3</td>\n",
       "      <td>gene_associated_with_condition</td>\n",
       "      <td>leukemia</td>\n",
       "      <td>PUBCHEM.COMPOUND:123596</td>\n",
       "      <td>MESH:D000068877</td>\n",
       "      <td>NCBIGene:836</td>\n",
       "      <td>NCBIGene:836</td>\n",
       "      <td>MONDO:0005059</td>\n",
       "      <td>...</td>\n",
       "      <td>https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7...</td>\n",
       "      <td>Inhibitory roles of MSC-derived exosomes in tu...</td>\n",
       "      <td>27</td>\n",
       "      <td>{MESH:D007938, NCBIGene:836}</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>25</td>\n",
       "      <td>30135</td>\n",
       "      <td>0.000002</td>\n",
       "      <td>(MESH:D000068877, MESH:D007938, NCBIGene:836)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1284</th>\n",
       "      <td>Imatinib mesylate</td>\n",
       "      <td>increases_activity_of</td>\n",
       "      <td>CASP3</td>\n",
       "      <td>gene_associated_with_condition</td>\n",
       "      <td>leukemia</td>\n",
       "      <td>PUBCHEM.COMPOUND:123596</td>\n",
       "      <td>MESH:D000068877</td>\n",
       "      <td>NCBIGene:836</td>\n",
       "      <td>NCBIGene:836</td>\n",
       "      <td>MONDO:0005059</td>\n",
       "      <td>...</td>\n",
       "      <td>https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7...</td>\n",
       "      <td>Inhibitory roles of MSC-derived exosomes in tu...</td>\n",
       "      <td>27</td>\n",
       "      <td>{MESH:D007938, NCBIGene:836}</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>25</td>\n",
       "      <td>30135</td>\n",
       "      <td>0.000002</td>\n",
       "      <td>(MESH:D000068877, MESH:D007938, NCBIGene:836)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1963</th>\n",
       "      <td>Imatinib mesylate</td>\n",
       "      <td>response_decreased_by</td>\n",
       "      <td>CDK6</td>\n",
       "      <td>gene_associated_with_condition</td>\n",
       "      <td>leukemia</td>\n",
       "      <td>PUBCHEM.COMPOUND:123596</td>\n",
       "      <td>MESH:D000068877</td>\n",
       "      <td>NCBIGene:1021</td>\n",
       "      <td>NCBIGene:1021</td>\n",
       "      <td>MONDO:0005059</td>\n",
       "      <td>...</td>\n",
       "      <td>https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5...</td>\n",
       "      <td>Cancer pathway targets affected by curcumin</td>\n",
       "      <td>172</td>\n",
       "      <td>{NCBIGene:1021}</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>171</td>\n",
       "      <td>29989</td>\n",
       "      <td>0.017010</td>\n",
       "      <td>(MESH:D000068877, MESH:D007938, NCBIGene:1021)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1986</th>\n",
       "      <td>Imatinib mesylate</td>\n",
       "      <td>increases_activity_of</td>\n",
       "      <td>JUN</td>\n",
       "      <td>gene_associated_with_condition</td>\n",
       "      <td>leukemia</td>\n",
       "      <td>PUBCHEM.COMPOUND:123596</td>\n",
       "      <td>MESH:D000068877</td>\n",
       "      <td>NCBIGene:3725</td>\n",
       "      <td>NCBIGene:3725</td>\n",
       "      <td>MONDO:0005059</td>\n",
       "      <td>...</td>\n",
       "      <td>https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5...</td>\n",
       "      <td>Cancer pathway targets affected by curcumin</td>\n",
       "      <td>172</td>\n",
       "      <td>{NCBIGene:3725}</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>171</td>\n",
       "      <td>29989</td>\n",
       "      <td>0.017010</td>\n",
       "      <td>(MESH:D000068877, MESH:D007938, NCBIGene:3725)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2009</th>\n",
       "      <td>Imatinib mesylate</td>\n",
       "      <td>increases_activity_of</td>\n",
       "      <td>JUN</td>\n",
       "      <td>contributes_to</td>\n",
       "      <td>leukemia</td>\n",
       "      <td>PUBCHEM.COMPOUND:123596</td>\n",
       "      <td>MESH:D000068877</td>\n",
       "      <td>NCBIGene:3725</td>\n",
       "      <td>NCBIGene:3725</td>\n",
       "      <td>MONDO:0005059</td>\n",
       "      <td>...</td>\n",
       "      <td>https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5...</td>\n",
       "      <td>Cancer pathway targets affected by curcumin</td>\n",
       "      <td>172</td>\n",
       "      <td>{NCBIGene:3725}</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>171</td>\n",
       "      <td>29989</td>\n",
       "      <td>0.017010</td>\n",
       "      <td>(MESH:D000068877, MESH:D007938, NCBIGene:3725)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>974</th>\n",
       "      <td>Imatinib mesylate</td>\n",
       "      <td>increases_secretion_of</td>\n",
       "      <td>VEGFA</td>\n",
       "      <td>contributes_to</td>\n",
       "      <td>leukemia</td>\n",
       "      <td>PUBCHEM.COMPOUND:123596</td>\n",
       "      <td>MESH:D000068877</td>\n",
       "      <td>NCBIGene:7422</td>\n",
       "      <td>NCBIGene:7422</td>\n",
       "      <td>MONDO:0005059</td>\n",
       "      <td>...</td>\n",
       "      <td>https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5...</td>\n",
       "      <td>Cancer pathway targets affected by curcumin</td>\n",
       "      <td>172</td>\n",
       "      <td>{NCBIGene:7422}</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>171</td>\n",
       "      <td>29989</td>\n",
       "      <td>0.017010</td>\n",
       "      <td>(MESH:D000068877, MESH:D007938, NCBIGene:7422)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>997</th>\n",
       "      <td>Imatinib mesylate</td>\n",
       "      <td>decreases_secretion_of</td>\n",
       "      <td>VEGFA</td>\n",
       "      <td>gene_associated_with_condition</td>\n",
       "      <td>Asthma</td>\n",
       "      <td>PUBCHEM.COMPOUND:123596</td>\n",
       "      <td>MESH:D000068877</td>\n",
       "      <td>NCBIGene:7422</td>\n",
       "      <td>NCBIGene:7422</td>\n",
       "      <td>HP:0002099</td>\n",
       "      <td>...</td>\n",
       "      <td>https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5...</td>\n",
       "      <td>Cancer pathway targets affected by curcumin</td>\n",
       "      <td>172</td>\n",
       "      <td>{NCBIGene:7422}</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>171</td>\n",
       "      <td>29989</td>\n",
       "      <td>0.017010</td>\n",
       "      <td>(MESH:D000068877, MESH:D001249, NCBIGene:7422)</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>381 rows × 25 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                     n0                        e0     n1  \\\n",
       "1330  Imatinib mesylate     increases_activity_of  CASP3   \n",
       "2089  Imatinib mesylate     increases_activity_of  CASP8   \n",
       "1307  Imatinib mesylate     increases_activity_of  CASP3   \n",
       "1353  Imatinib mesylate  increases_degradation_of  CASP3   \n",
       "1284  Imatinib mesylate     increases_activity_of  CASP3   \n",
       "...                 ...                       ...    ...   \n",
       "1963  Imatinib mesylate     response_decreased_by   CDK6   \n",
       "1986  Imatinib mesylate     increases_activity_of    JUN   \n",
       "2009  Imatinib mesylate     increases_activity_of    JUN   \n",
       "974   Imatinib mesylate    increases_secretion_of  VEGFA   \n",
       "997   Imatinib mesylate    decreases_secretion_of  VEGFA   \n",
       "\n",
       "                                  e1        n2        n0_original_curie  \\\n",
       "1330                  contributes_to  leukemia  PUBCHEM.COMPOUND:123596   \n",
       "2089                 correlated_with  leukemia  PUBCHEM.COMPOUND:123596   \n",
       "1307                 correlated_with  leukemia  PUBCHEM.COMPOUND:123596   \n",
       "1353  gene_associated_with_condition  leukemia  PUBCHEM.COMPOUND:123596   \n",
       "1284  gene_associated_with_condition  leukemia  PUBCHEM.COMPOUND:123596   \n",
       "...                              ...       ...                      ...   \n",
       "1963  gene_associated_with_condition  leukemia  PUBCHEM.COMPOUND:123596   \n",
       "1986  gene_associated_with_condition  leukemia  PUBCHEM.COMPOUND:123596   \n",
       "2009                  contributes_to  leukemia  PUBCHEM.COMPOUND:123596   \n",
       "974                   contributes_to  leukemia  PUBCHEM.COMPOUND:123596   \n",
       "997   gene_associated_with_condition    Asthma  PUBCHEM.COMPOUND:123596   \n",
       "\n",
       "     n0_unified_curie n1_original_curie n1_unified_curie n2_original_curie  \\\n",
       "1330  MESH:D000068877      NCBIGene:836     NCBIGene:836     MONDO:0005059   \n",
       "2089  MESH:D000068877      NCBIGene:841     NCBIGene:841     MONDO:0005059   \n",
       "1307  MESH:D000068877      NCBIGene:836     NCBIGene:836     MONDO:0005059   \n",
       "1353  MESH:D000068877      NCBIGene:836     NCBIGene:836     MONDO:0005059   \n",
       "1284  MESH:D000068877      NCBIGene:836     NCBIGene:836     MONDO:0005059   \n",
       "...               ...               ...              ...               ...   \n",
       "1963  MESH:D000068877     NCBIGene:1021    NCBIGene:1021     MONDO:0005059   \n",
       "1986  MESH:D000068877     NCBIGene:3725    NCBIGene:3725     MONDO:0005059   \n",
       "2009  MESH:D000068877     NCBIGene:3725    NCBIGene:3725     MONDO:0005059   \n",
       "974   MESH:D000068877     NCBIGene:7422    NCBIGene:7422     MONDO:0005059   \n",
       "997   MESH:D000068877     NCBIGene:7422    NCBIGene:7422        HP:0002099   \n",
       "\n",
       "      ...                                         figure_url  \\\n",
       "1330  ...  https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7...   \n",
       "2089  ...  https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7...   \n",
       "1307  ...  https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7...   \n",
       "1353  ...  https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7...   \n",
       "1284  ...  https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7...   \n",
       "...   ...                                                ...   \n",
       "1963  ...  https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5...   \n",
       "1986  ...  https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5...   \n",
       "2009  ...  https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5...   \n",
       "974   ...  https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5...   \n",
       "997   ...  https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5...   \n",
       "\n",
       "                                           figure_title  figure_curie_count  \\\n",
       "1330  Inhibitory roles of MSC-derived exosomes in tu...                  27   \n",
       "2089  Inhibitory roles of MSC-derived exosomes in tu...                  27   \n",
       "1307  Inhibitory roles of MSC-derived exosomes in tu...                  27   \n",
       "1353  Inhibitory roles of MSC-derived exosomes in tu...                  27   \n",
       "1284  Inhibitory roles of MSC-derived exosomes in tu...                  27   \n",
       "...                                                 ...                 ...   \n",
       "1963        Cancer pathway targets affected by curcumin                 172   \n",
       "1986        Cancer pathway targets affected by curcumin                 172   \n",
       "2009        Cancer pathway targets affected by curcumin                 172   \n",
       "974         Cancer pathway targets affected by curcumin                 172   \n",
       "997         Cancer pathway targets affected by curcumin                 172   \n",
       "\n",
       "                  common_curie_set yes_trapi_result_yes_figure  \\\n",
       "1330  {MESH:D007938, NCBIGene:836}                           2   \n",
       "2089  {NCBIGene:841, MESH:D007938}                           2   \n",
       "1307  {MESH:D007938, NCBIGene:836}                           2   \n",
       "1353  {MESH:D007938, NCBIGene:836}                           2   \n",
       "1284  {MESH:D007938, NCBIGene:836}                           2   \n",
       "...                            ...                         ...   \n",
       "1963               {NCBIGene:1021}                           1   \n",
       "1986               {NCBIGene:3725}                           1   \n",
       "2009               {NCBIGene:3725}                           1   \n",
       "974                {NCBIGene:7422}                           1   \n",
       "997                {NCBIGene:7422}                           1   \n",
       "\n",
       "     yes_trapi_result_no_figure no_trapi_result_yes_figure  \\\n",
       "1330                          1                         25   \n",
       "2089                          1                         25   \n",
       "1307                          1                         25   \n",
       "1353                          1                         25   \n",
       "1284                          1                         25   \n",
       "...                         ...                        ...   \n",
       "1963                          2                        171   \n",
       "1986                          2                        171   \n",
       "2009                          2                        171   \n",
       "974                           2                        171   \n",
       "997                           2                        171   \n",
       "\n",
       "      no_trapi_result_no_figure   p_value  \\\n",
       "1330                      30135  0.000002   \n",
       "2089                      30135  0.000002   \n",
       "1307                      30135  0.000002   \n",
       "1353                      30135  0.000002   \n",
       "1284                      30135  0.000002   \n",
       "...                         ...       ...   \n",
       "1963                      29989  0.017010   \n",
       "1986                      29989  0.017010   \n",
       "2009                      29989  0.017010   \n",
       "974                       29989  0.017010   \n",
       "997                       29989  0.017010   \n",
       "\n",
       "                             trapi_result_curies_key  \n",
       "1330   (MESH:D000068877, MESH:D007938, NCBIGene:836)  \n",
       "2089   (MESH:D000068877, MESH:D007938, NCBIGene:841)  \n",
       "1307   (MESH:D000068877, MESH:D007938, NCBIGene:836)  \n",
       "1353   (MESH:D000068877, MESH:D007938, NCBIGene:836)  \n",
       "1284   (MESH:D000068877, MESH:D007938, NCBIGene:836)  \n",
       "...                                              ...  \n",
       "1963  (MESH:D000068877, MESH:D007938, NCBIGene:1021)  \n",
       "1986  (MESH:D000068877, MESH:D007938, NCBIGene:3725)  \n",
       "2009  (MESH:D000068877, MESH:D007938, NCBIGene:3725)  \n",
       "974   (MESH:D000068877, MESH:D007938, NCBIGene:7422)  \n",
       "997   (MESH:D000068877, MESH:D001249, NCBIGene:7422)  \n",
       "\n",
       "[381 rows x 25 columns]"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# This isn't really a significance threshold. It's more just intended to\n",
    "# exclude results where the p-value is 1 or very close to it.\n",
    "threshold = 0.9\n",
    "significant_trapi_results_with_figures_df = trapi_results_with_figures_df[\n",
    "    trapi_results_with_figures_df[\"p_value\"] < 0.9\n",
    "]\n",
    "\n",
    "# all\n",
    "cooccurrence_df = significant_trapi_results_with_figures_df.sort_values(\n",
    "    \"p_value\"\n",
    ")\n",
    "\n",
    "### first and any other query node\n",
    "##q_node_id_combination = (q_node_ids[0],)\n",
    "\n",
    "## first and second query nodes\n",
    "##q_node_id_combination = (q_node_ids[0], q_node_ids[1])\n",
    "\n",
    "### first and last query nodes\n",
    "##q_node_id_combination = (q_node_ids[0], q_node_ids[-1])\n",
    "\n",
    "### second and last query nodes\n",
    "##q_node_id_combination = (q_node_ids[1], q_node_ids[-1])\n",
    "\n",
    "### every query node with 'ids' specified\n",
    "##q_node_id_combination = query_nodes_with_ids\n",
    "\n",
    "#print(f'{\" & \".join(q_node_id_combination)}')\n",
    "#s = pd.Series(\n",
    "#    [True] * len(significant_trapi_results_with_figures_df),\n",
    "#    index=significant_trapi_results_with_figures_dfindex\n",
    "#)\n",
    "#for q_node_id in q_node_id_combination:\n",
    "#    s = s & significant_trapi_results_with_figures_df.apply(\n",
    "#        lambda r: r[f'{q_node_id}_unified_curie'] in r[\"common_curie_set\"], axis=1\n",
    "#    )\n",
    "#cooccurrence_df = significant_trapi_results_with_figures_df[s == True].sort_values(\n",
    "#    \"p_value\"\n",
    "#)\n",
    "\n",
    "cooccurrence_df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "\n",
       "<div style=\"border: 1px solid slategray; padding: 6px;\">\n",
       "    <figure style=\"margin: 5px !important;\">\n",
       "        <a target=\"_blank\" href=\"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7499322/\">\n",
       "          <img src=\"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7499322/bin/SCI2020-8844730.003.jpg\" style=\"max-height: 500px; max-width: 100%;\">\n",
       "        </a>\n",
       "        <br>\n",
       "        <caption>\n",
       "            Inhibitory roles of MSC-derived exosomes in tumors\n",
       "        </caption>\n",
       "    </figure>\n",
       "</div>\n"
      ],
      "text/plain": [
       "<IPython.core.display.HTML object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "\n",
       "<div>\n",
       "    <span style=\"font-weight: bold; font-size: small;\">\n",
       "        TRAPI result MESH:D000068877, NCBIGene:836, MESH:D007938\n",
       "    </span> \n",
       "    (p-value: 2.3e-06)\n",
       "    <br>\n",
       "    Imatinib mesylate - increases_activity_of - \n",
       "<span style=\"border: 1px orange solid; padding: 1px; border-radius: 4px;\">CASP3</span>\n",
       " - contributes_to - \n",
       "<span style=\"border: 1px orange solid; padding: 1px; border-radius: 4px;\">leukemia</span>\n",
       "</span><br>Imatinib mesylate - increases_activity_of - \n",
       "<span style=\"border: 1px orange solid; padding: 1px; border-radius: 4px;\">CASP3</span>\n",
       " - correlated_with - \n",
       "<span style=\"border: 1px orange solid; padding: 1px; border-radius: 4px;\">leukemia</span>\n",
       "</span><br>Imatinib mesylate - increases_degradation_of - \n",
       "<span style=\"border: 1px orange solid; padding: 1px; border-radius: 4px;\">CASP3</span>\n",
       " - gene_associated_with_condition - \n",
       "<span style=\"border: 1px orange solid; padding: 1px; border-radius: 4px;\">leukemia</span>\n",
       "</span><br>Imatinib mesylate - increases_activity_of - \n",
       "<span style=\"border: 1px orange solid; padding: 1px; border-radius: 4px;\">CASP3</span>\n",
       " - gene_associated_with_condition - \n",
       "<span style=\"border: 1px orange solid; padding: 1px; border-radius: 4px;\">leukemia</span>\n",
       "</span><br>Imatinib mesylate - increases_degradation_of - \n",
       "<span style=\"border: 1px orange solid; padding: 1px; border-radius: 4px;\">CASP3</span>\n",
       " - correlated_with - \n",
       "<span style=\"border: 1px orange solid; padding: 1px; border-radius: 4px;\">leukemia</span>\n",
       "</span><br>Imatinib mesylate - increases_degradation_of - \n",
       "<span style=\"border: 1px orange solid; padding: 1px; border-radius: 4px;\">CASP3</span>\n",
       " - contributes_to - \n",
       "<span style=\"border: 1px orange solid; padding: 1px; border-radius: 4px;\">leukemia</span>\n",
       "</span>\n",
       "</div>\n",
       "<br>\n",
       "<div>\n",
       "    <span style=\"font-weight: bold; font-size: small;\">\n",
       "        TRAPI result MESH:D000068877, NCBIGene:841, MESH:D007938\n",
       "    </span> \n",
       "    (p-value: 2.3e-06)\n",
       "    <br>\n",
       "    Imatinib mesylate - increases_activity_of - \n",
       "<span style=\"border: 1px orange solid; padding: 1px; border-radius: 4px;\">CASP8</span>\n",
       " - correlated_with - \n",
       "<span style=\"border: 1px orange solid; padding: 1px; border-radius: 4px;\">leukemia</span>\n",
       "</span><br>Imatinib mesylate - increases_activity_of - \n",
       "<span style=\"border: 1px orange solid; padding: 1px; border-radius: 4px;\">CASP8</span>\n",
       " - gene_associated_with_condition - \n",
       "<span style=\"border: 1px orange solid; padding: 1px; border-radius: 4px;\">leukemia</span>\n",
       "</span>\n",
       "</div>\n",
       "<br>\n",
       "<div>\n",
       "    <span style=\"font-weight: bold; font-size: small;\">\n",
       "        TRAPI result MESH:D000068877, NCBIGene:842, MESH:D007938\n",
       "    </span> \n",
       "    (p-value: 2.3e-06)\n",
       "    <br>\n",
       "    Imatinib mesylate - increases_degradation_of - \n",
       "<span style=\"border: 1px orange solid; padding: 1px; border-radius: 4px;\">CASP9</span>\n",
       " - correlated_with - \n",
       "<span style=\"border: 1px orange solid; padding: 1px; border-radius: 4px;\">leukemia</span>\n",
       "</span><br>Imatinib mesylate - increases_degradation_of - \n",
       "<span style=\"border: 1px orange solid; padding: 1px; border-radius: 4px;\">CASP9</span>\n",
       " - gene_associated_with_condition - \n",
       "<span style=\"border: 1px orange solid; padding: 1px; border-radius: 4px;\">leukemia</span>\n",
       "</span>\n",
       "</div>\n",
       "<br>\n",
       "<div>\n",
       "    <span style=\"font-weight: bold; font-size: small;\">\n",
       "        TRAPI result MESH:D000068877, NCBIGene:64321, MESH:D007938\n",
       "    </span> \n",
       "    (p-value: 2.7e-03)\n",
       "    <br>\n",
       "    Imatinib mesylate - decreases_localization_of - SOX17 - contributes_to - \n",
       "<span style=\"border: 1px orange solid; padding: 1px; border-radius: 4px;\">leukemia</span>\n",
       "</span>\n",
       "</div>\n",
       "<br>\n",
       "<div>\n",
       "    <span style=\"font-weight: bold; font-size: small;\">\n",
       "        TRAPI result MESH:D000068877, NCBIGene:207, MESH:D007938\n",
       "    </span> \n",
       "    (p-value: 2.7e-03)\n",
       "    <br>\n",
       "    Imatinib mesylate - decreases_molecular_modification_of - AKT1 - related_to - \n",
       "<span style=\"border: 1px orange solid; padding: 1px; border-radius: 4px;\">leukemia</span>\n",
       "</span><br>Imatinib mesylate - decreases_molecular_modification_of - AKT1 - gene_associated_with_condition - \n",
       "<span style=\"border: 1px orange solid; padding: 1px; border-radius: 4px;\">leukemia</span>\n",
       "</span><br>Imatinib mesylate - decreases_molecular_modification_of - AKT1 - entity_positively_regulates_entity - \n",
       "<span style=\"border: 1px orange solid; padding: 1px; border-radius: 4px;\">leukemia</span>\n",
       "</span><br>Imatinib mesylate - decreases_molecular_modification_of - AKT1 - contributes_to - \n",
       "<span style=\"border: 1px orange solid; padding: 1px; border-radius: 4px;\">leukemia</span>\n",
       "</span>\n",
       "</div>\n",
       "<br>\n",
       "<div>\n",
       "    <span style=\"font-weight: bold; font-size: small;\">\n",
       "        TRAPI result MESH:D000068877, NCBIGene:5156, MESH:D007938\n",
       "    </span> \n",
       "    (p-value: 2.7e-03)\n",
       "    <br>\n",
       "    Imatinib mesylate - response_increased_by - PDGFRA - gene_associated_with_condition - \n",
       "<span style=\"border: 1px orange solid; padding: 1px; border-radius: 4px;\">leukemia</span>\n",
       "</span><br>Imatinib mesylate - response_affected_by - PDGFRA - gene_associated_with_condition - \n",
       "<span style=\"border: 1px orange solid; padding: 1px; border-radius: 4px;\">leukemia</span>\n",
       "</span><br>Imatinib mesylate - decreases_expression_of - PDGFRA - gene_associated_with_condition - \n",
       "<span style=\"border: 1px orange solid; padding: 1px; border-radius: 4px;\">leukemia</span>\n",
       "</span>\n",
       "</div>\n",
       "<br>\n",
       "<div>\n",
       "    <span style=\"font-weight: bold; font-size: small;\">\n",
       "        TRAPI result MESH:D000068877, NCBIGene:841, MESH:D001249\n",
       "    </span> \n",
       "    (p-value: 2.7e-03)\n",
       "    <br>\n",
       "    Imatinib mesylate - increases_activity_of - \n",
       "<span style=\"border: 1px orange solid; padding: 1px; border-radius: 4px;\">CASP8</span>\n",
       " - gene_associated_with_condition - Asthma</span>\n",
       "</div>\n",
       "<br>\n",
       "<div>\n",
       "    <span style=\"font-weight: bold; font-size: small;\">\n",
       "        TRAPI result MESH:D000068877, NCBIGene:9467, MESH:D007938\n",
       "    </span> \n",
       "    (p-value: 2.7e-03)\n",
       "    <br>\n",
       "    Imatinib mesylate - response_increased_by - SH3BP5 - contributes_to - \n",
       "<span style=\"border: 1px orange solid; padding: 1px; border-radius: 4px;\">leukemia</span>\n",
       "</span><br>Imatinib mesylate - response_affected_by - SH3BP5 - contributes_to - \n",
       "<span style=\"border: 1px orange solid; padding: 1px; border-radius: 4px;\">leukemia</span>\n",
       "</span>\n",
       "</div>\n",
       "<br>\n",
       "<div>\n",
       "    <span style=\"font-weight: bold; font-size: small;\">\n",
       "        TRAPI result MESH:D000068877, NCBIGene:3725, MESH:D007938\n",
       "    </span> \n",
       "    (p-value: 2.7e-03)\n",
       "    <br>\n",
       "    Imatinib mesylate - increases_activity_of - JUN - contributes_to - \n",
       "<span style=\"border: 1px orange solid; padding: 1px; border-radius: 4px;\">leukemia</span>\n",
       "</span><br>Imatinib mesylate - increases_activity_of - JUN - gene_associated_with_condition - \n",
       "<span style=\"border: 1px orange solid; padding: 1px; border-radius: 4px;\">leukemia</span>\n",
       "</span>\n",
       "</div>\n",
       "<br>\n",
       "<div>\n",
       "    <span style=\"font-weight: bold; font-size: small;\">\n",
       "        TRAPI result MESH:D000068877, NCBIGene:1021, MESH:D007938\n",
       "    </span> \n",
       "    (p-value: 2.7e-03)\n",
       "    <br>\n",
       "    Imatinib mesylate - response_decreased_by - CDK6 - gene_associated_with_condition - \n",
       "<span style=\"border: 1px orange solid; padding: 1px; border-radius: 4px;\">leukemia</span>\n",
       "</span><br>Imatinib mesylate - response_affected_by - CDK6 - gene_associated_with_condition - \n",
       "<span style=\"border: 1px orange solid; padding: 1px; border-radius: 4px;\">leukemia</span>\n",
       "</span>\n",
       "</div>\n"
      ],
      "text/plain": [
       "<IPython.core.display.HTML object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "...\n",
      "\n",
      "plus 11 more TRAPI results\n",
      "\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "\n",
       "<div style=\"border: 1px solid slategray; padding: 6px;\">\n",
       "    <figure style=\"margin: 5px !important;\">\n",
       "        <a target=\"_blank\" href=\"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4748076/\">\n",
       "          <img src=\"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4748076/bin/zns9991682760008.jpg\" style=\"max-height: 500px; max-width: 100%;\">\n",
       "        </a>\n",
       "        <br>\n",
       "        <caption>\n",
       "            Proposed signaling pathway\n",
       "        </caption>\n",
       "    </figure>\n",
       "</div>\n"
      ],
      "text/plain": [
       "<IPython.core.display.HTML object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "\n",
       "<div>\n",
       "    <span style=\"font-weight: bold; font-size: small;\">\n",
       "        TRAPI result MESH:D000068877, NCBIGene:5243, MESH:D007938\n",
       "    </span> \n",
       "    (p-value: 9.9e-05)\n",
       "    <br>\n",
       "    Imatinib mesylate - response_decreased_by - \n",
       "<span style=\"border: 1px orange solid; padding: 1px; border-radius: 4px;\">ABCB1</span>\n",
       " - gene_associated_with_condition - leukemia</span><br>Imatinib mesylate - response_decreased_by - \n",
       "<span style=\"border: 1px orange solid; padding: 1px; border-radius: 4px;\">ABCB1</span>\n",
       " - part_of - leukemia</span><br>Imatinib mesylate - response_decreased_by - \n",
       "<span style=\"border: 1px orange solid; padding: 1px; border-radius: 4px;\">ABCB1</span>\n",
       " - affects_risk_for - leukemia</span><br>Imatinib mesylate - response_affected_by - \n",
       "<span style=\"border: 1px orange solid; padding: 1px; border-radius: 4px;\">ABCB1</span>\n",
       " - gene_associated_with_condition - leukemia</span><br>Imatinib mesylate - response_affected_by - \n",
       "<span style=\"border: 1px orange solid; padding: 1px; border-radius: 4px;\">ABCB1</span>\n",
       " - correlated_with - leukemia</span><br>Imatinib mesylate - response_decreased_by - \n",
       "<span style=\"border: 1px orange solid; padding: 1px; border-radius: 4px;\">ABCB1</span>\n",
       " - correlated_with - leukemia</span><br>Imatinib mesylate - response_affected_by - \n",
       "<span style=\"border: 1px orange solid; padding: 1px; border-radius: 4px;\">ABCB1</span>\n",
       " - part_of - leukemia</span><br>Imatinib mesylate - response_decreased_by - \n",
       "<span style=\"border: 1px orange solid; padding: 1px; border-radius: 4px;\">ABCB1</span>\n",
       " - related_to - leukemia</span><br>Imatinib mesylate - response_affected_by - \n",
       "<span style=\"border: 1px orange solid; padding: 1px; border-radius: 4px;\">ABCB1</span>\n",
       " - affects_risk_for - leukemia</span><br>Imatinib mesylate - response_affected_by - \n",
       "<span style=\"border: 1px orange solid; padding: 1px; border-radius: 4px;\">ABCB1</span>\n",
       " - related_to - leukemia</span>\n",
       "</div>\n"
      ],
      "text/plain": [
       "<IPython.core.display.HTML object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "\n",
       "<div style=\"border: 1px solid slategray; padding: 6px;\">\n",
       "    <figure style=\"margin: 5px !important;\">\n",
       "        <a target=\"_blank\" href=\"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7200694/\">\n",
       "          <img src=\"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7200694/bin/41598_2020_64503_Fig6_HTML.jpg\" style=\"max-height: 500px; max-width: 100%;\">\n",
       "        </a>\n",
       "        <br>\n",
       "        <caption>\n",
       "            Proposed pathway of biliatresone-induced injury\n",
       "        </caption>\n",
       "    </figure>\n",
       "</div>\n"
      ],
      "text/plain": [
       "<IPython.core.display.HTML object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "\n",
       "<div>\n",
       "    <span style=\"font-weight: bold; font-size: small;\">\n",
       "        TRAPI result MESH:D000068877, NCBIGene:64321, MESH:D007938\n",
       "    </span> \n",
       "    (p-value: 2.0e-04)\n",
       "    <br>\n",
       "    Imatinib mesylate - decreases_localization_of - \n",
       "<span style=\"border: 1px orange solid; padding: 1px; border-radius: 4px;\">SOX17</span>\n",
       " - contributes_to - leukemia</span>\n",
       "</div>\n"
      ],
      "text/plain": [
       "<IPython.core.display.HTML object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "\n",
       "<div style=\"border: 1px solid slategray; padding: 6px;\">\n",
       "    <figure style=\"margin: 5px !important;\">\n",
       "        <a target=\"_blank\" href=\"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC3412280/\">\n",
       "          <img src=\"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC3412280/bin/srep00557-f2.jpg\" style=\"max-height: 500px; max-width: 100%;\">\n",
       "        </a>\n",
       "        <br>\n",
       "        <caption>\n",
       "            SNP in starch biosynthesis genes associated with nutritional and functional properties of rice\n",
       "        </caption>\n",
       "    </figure>\n",
       "</div>\n"
      ],
      "text/plain": [
       "<IPython.core.display.HTML object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "\n",
       "<div>\n",
       "    <span style=\"font-weight: bold; font-size: small;\">\n",
       "        TRAPI result MESH:D000068877, NCBIGene:2875, MESH:D007938\n",
       "    </span> \n",
       "    (p-value: 2.0e-04)\n",
       "    <br>\n",
       "    Imatinib mesylate - increases_secretion_of - \n",
       "<span style=\"border: 1px orange solid; padding: 1px; border-radius: 4px;\">GPT</span>\n",
       " - gene_associated_with_condition - leukemia</span>\n",
       "</div>\n"
      ],
      "text/plain": [
       "<IPython.core.display.HTML object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "\n",
       "<div style=\"border: 1px solid slategray; padding: 6px;\">\n",
       "    <figure style=\"margin: 5px !important;\">\n",
       "        <a target=\"_blank\" href=\"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6530120/\">\n",
       "          <img src=\"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6530120/bin/OMCL2019-7417561.009.jpg\" style=\"max-height: 500px; max-width: 100%;\">\n",
       "        </a>\n",
       "        <br>\n",
       "        <caption>\n",
       "            Model of the JNK/Sab/Src/ROS signaling pathway and the role of F2 in H9c2 cells during H/R\n",
       "        </caption>\n",
       "    </figure>\n",
       "</div>\n"
      ],
      "text/plain": [
       "<IPython.core.display.HTML object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "\n",
       "<div>\n",
       "    <span style=\"font-weight: bold; font-size: small;\">\n",
       "        TRAPI result MESH:D000068877, NCBIGene:9467, MESH:D007938\n",
       "    </span> \n",
       "    (p-value: 6.0e-04)\n",
       "    <br>\n",
       "    Imatinib mesylate - response_affected_by - \n",
       "<span style=\"border: 1px orange solid; padding: 1px; border-radius: 4px;\">SH3BP5</span>\n",
       " - contributes_to - leukemia</span><br>Imatinib mesylate - response_increased_by - \n",
       "<span style=\"border: 1px orange solid; padding: 1px; border-radius: 4px;\">SH3BP5</span>\n",
       " - contributes_to - leukemia</span>\n",
       "</div>\n"
      ],
      "text/plain": [
       "<IPython.core.display.HTML object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "\n",
       "<div style=\"border: 1px solid slategray; padding: 6px;\">\n",
       "    <figure style=\"margin: 5px !important;\">\n",
       "        <a target=\"_blank\" href=\"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7600041/\">\n",
       "          <img src=\"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7600041/bin/viruses-12-01110-g008.jpg\" style=\"max-height: 500px; max-width: 100%;\">\n",
       "        </a>\n",
       "        <br>\n",
       "        <caption>\n",
       "            Proteins that regulate HPV RNA processing are controlled by extracellular stimuli and cellular responses\n",
       "        </caption>\n",
       "    </figure>\n",
       "</div>\n"
      ],
      "text/plain": [
       "<IPython.core.display.HTML object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "\n",
       "<div>\n",
       "    <span style=\"font-weight: bold; font-size: small;\">\n",
       "        TRAPI result MESH:D000068877, NCBIGene:207, MESH:D007938\n",
       "    </span> \n",
       "    (p-value: 9.9e-04)\n",
       "    <br>\n",
       "    Imatinib mesylate - decreases_molecular_modification_of - \n",
       "<span style=\"border: 1px orange solid; padding: 1px; border-radius: 4px;\">AKT1</span>\n",
       " - contributes_to - leukemia</span><br>Imatinib mesylate - decreases_molecular_modification_of - \n",
       "<span style=\"border: 1px orange solid; padding: 1px; border-radius: 4px;\">AKT1</span>\n",
       " - related_to - leukemia</span><br>Imatinib mesylate - decreases_molecular_modification_of - \n",
       "<span style=\"border: 1px orange solid; padding: 1px; border-radius: 4px;\">AKT1</span>\n",
       " - gene_associated_with_condition - leukemia</span><br>Imatinib mesylate - decreases_molecular_modification_of - \n",
       "<span style=\"border: 1px orange solid; padding: 1px; border-radius: 4px;\">AKT1</span>\n",
       " - entity_positively_regulates_entity - leukemia</span>\n",
       "</div>\n",
       "<br>\n",
       "<div>\n",
       "    <span style=\"font-weight: bold; font-size: small;\">\n",
       "        TRAPI result MESH:D000068877, NCBIGene:81608, MESH:D007938\n",
       "    </span> \n",
       "    (p-value: 9.9e-04)\n",
       "    <br>\n",
       "    Imatinib mesylate - response_affected_by - \n",
       "<span style=\"border: 1px orange solid; padding: 1px; border-radius: 4px;\">FIP1L1</span>\n",
       " - gene_associated_with_condition - leukemia</span><br>Imatinib mesylate - response_increased_by - \n",
       "<span style=\"border: 1px orange solid; padding: 1px; border-radius: 4px;\">FIP1L1</span>\n",
       " - contributes_to - leukemia</span><br>Imatinib mesylate - response_increased_by - \n",
       "<span style=\"border: 1px orange solid; padding: 1px; border-radius: 4px;\">FIP1L1</span>\n",
       " - gene_associated_with_condition - leukemia</span><br>Imatinib mesylate - response_affected_by - \n",
       "<span style=\"border: 1px orange solid; padding: 1px; border-radius: 4px;\">FIP1L1</span>\n",
       " - contributes_to - leukemia</span>\n",
       "</div>\n"
      ],
      "text/plain": [
       "<IPython.core.display.HTML object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "\n",
       "<div style=\"border: 1px solid slategray; padding: 6px;\">\n",
       "    <figure style=\"margin: 5px !important;\">\n",
       "        <a target=\"_blank\" href=\"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7023894/\">\n",
       "          <img src=\"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7023894/bin/nihms-1556086-f0004.jpg\" style=\"max-height: 500px; max-width: 100%;\">\n",
       "        </a>\n",
       "        <br>\n",
       "        <caption>\n",
       "            Key Figure Schematic of Transgelin-2 in the Intracellular Interaction and Signaling Mechanism\n",
       "        </caption>\n",
       "    </figure>\n",
       "</div>\n"
      ],
      "text/plain": [
       "<IPython.core.display.HTML object>"
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     "metadata": {},
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    {
     "data": {
      "text/html": [
       "\n",
       "<div>\n",
       "    <span style=\"font-weight: bold; font-size: small;\">\n",
       "        TRAPI result MESH:D000068877, NCBIGene:5156, MESH:D001249\n",
       "    </span> \n",
       "    (p-value: 1.3e-03)\n",
       "    <br>\n",
       "    Imatinib mesylate - response_increased_by - PDGFRA - gene_associated_with_condition - \n",
       "<span style=\"border: 1px orange solid; padding: 1px; border-radius: 4px;\">Asthma</span>\n",
       "</span><br>Imatinib mesylate - decreases_expression_of - PDGFRA - gene_associated_with_condition - \n",
       "<span style=\"border: 1px orange solid; padding: 1px; border-radius: 4px;\">Asthma</span>\n",
       "</span><br>Imatinib mesylate - response_affected_by - PDGFRA - gene_associated_with_condition - \n",
       "<span style=\"border: 1px orange solid; padding: 1px; border-radius: 4px;\">Asthma</span>\n",
       "</span>\n",
       "</div>\n",
       "<br>\n",
       "<div>\n",
       "    <span style=\"font-weight: bold; font-size: small;\">\n",
       "        TRAPI result MESH:D000068877, NCBIGene:7422, MESH:D001249\n",
       "    </span> \n",
       "    (p-value: 1.3e-03)\n",
       "    <br>\n",
       "    Imatinib mesylate - decreases_secretion_of - VEGFA - contributes_to - \n",
       "<span style=\"border: 1px orange solid; padding: 1px; border-radius: 4px;\">Asthma</span>\n",
       "</span><br>Imatinib mesylate - decreases_secretion_of - VEGFA - gene_associated_with_condition - \n",
       "<span style=\"border: 1px orange solid; padding: 1px; border-radius: 4px;\">Asthma</span>\n",
       "</span><br>Imatinib mesylate - increases_secretion_of - VEGFA - gene_associated_with_condition - \n",
       "<span style=\"border: 1px orange solid; padding: 1px; border-radius: 4px;\">Asthma</span>\n",
       "</span><br>Imatinib mesylate - increases_secretion_of - VEGFA - contributes_to - \n",
       "<span style=\"border: 1px orange solid; padding: 1px; border-radius: 4px;\">Asthma</span>\n",
       "</span>\n",
       "</div>\n",
       "<br>\n",
       "<div>\n",
       "    <span style=\"font-weight: bold; font-size: small;\">\n",
       "        TRAPI result MESH:D000068877, NCBIGene:3576, MESH:D001249\n",
       "    </span> \n",
       "    (p-value: 1.3e-03)\n",
       "    <br>\n",
       "    Imatinib mesylate - increases_secretion_of - CXCL8 - gene_associated_with_condition - \n",
       "<span style=\"border: 1px orange solid; padding: 1px; border-radius: 4px;\">Asthma</span>\n",
       "</span><br>Imatinib mesylate - increases_secretion_of - CXCL8 - contributes_to - \n",
       "<span style=\"border: 1px orange solid; padding: 1px; border-radius: 4px;\">Asthma</span>\n",
       "</span>\n",
       "</div>\n",
       "<br>\n",
       "<div>\n",
       "    <span style=\"font-weight: bold; font-size: small;\">\n",
       "        TRAPI result MESH:D000068877, NCBIGene:4067, MESH:D001249\n",
       "    </span> \n",
       "    (p-value: 1.3e-03)\n",
       "    <br>\n",
       "    Imatinib mesylate - response_affected_by - LYN - related_to - \n",
       "<span style=\"border: 1px orange solid; padding: 1px; border-radius: 4px;\">Asthma</span>\n",
       "</span><br>Imatinib mesylate - response_decreased_by - LYN - related_to - \n",
       "<span style=\"border: 1px orange solid; padding: 1px; border-radius: 4px;\">Asthma</span>\n",
       "</span>\n",
       "</div>\n",
       "<br>\n",
       "<div>\n",
       "    <span style=\"font-weight: bold; font-size: small;\">\n",
       "        TRAPI result MESH:D000068877, NCBIGene:841, MESH:D001249\n",
       "    </span> \n",
       "    (p-value: 1.3e-03)\n",
       "    <br>\n",
       "    Imatinib mesylate - increases_activity_of - CASP8 - gene_associated_with_condition - \n",
       "<span style=\"border: 1px orange solid; padding: 1px; border-radius: 4px;\">Asthma</span>\n",
       "</span>\n",
       "</div>\n"
      ],
      "text/plain": [
       "<IPython.core.display.HTML object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "\n",
       "<div style=\"border: 1px solid slategray; padding: 6px;\">\n",
       "    <figure style=\"margin: 5px !important;\">\n",
       "        <a target=\"_blank\" href=\"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC2724780/\">\n",
       "          <img src=\"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC2724780/bin/1721fig1.jpg\" style=\"max-height: 500px; max-width: 100%;\">\n",
       "        </a>\n",
       "        <br>\n",
       "        <caption>\n",
       "            Bile acid modulation of death and survival transduction pathways\n",
       "        </caption>\n",
       "    </figure>\n",
       "</div>\n"
      ],
      "text/plain": [
       "<IPython.core.display.HTML object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "\n",
       "<div>\n",
       "    <span style=\"font-weight: bold; font-size: small;\">\n",
       "        TRAPI result MESH:D000068877, NCBIGene:841, MESH:D001249\n",
       "    </span> \n",
       "    (p-value: 2.7e-03)\n",
       "    <br>\n",
       "    Imatinib mesylate - increases_activity_of - \n",
       "<span style=\"border: 1px orange solid; padding: 1px; border-radius: 4px;\">CASP8</span>\n",
       " - gene_associated_with_condition - Asthma</span>\n",
       "</div>\n",
       "<br>\n",
       "<div>\n",
       "    <span style=\"font-weight: bold; font-size: small;\">\n",
       "        TRAPI result MESH:D000068877, NCBIGene:207, MESH:D007938\n",
       "    </span> \n",
       "    (p-value: 2.7e-03)\n",
       "    <br>\n",
       "    Imatinib mesylate - decreases_molecular_modification_of - \n",
       "<span style=\"border: 1px orange solid; padding: 1px; border-radius: 4px;\">AKT1</span>\n",
       " - gene_associated_with_condition - leukemia</span><br>Imatinib mesylate - decreases_molecular_modification_of - \n",
       "<span style=\"border: 1px orange solid; padding: 1px; border-radius: 4px;\">AKT1</span>\n",
       " - related_to - leukemia</span><br>Imatinib mesylate - decreases_molecular_modification_of - \n",
       "<span style=\"border: 1px orange solid; padding: 1px; border-radius: 4px;\">AKT1</span>\n",
       " - contributes_to - leukemia</span><br>Imatinib mesylate - decreases_molecular_modification_of - \n",
       "<span style=\"border: 1px orange solid; padding: 1px; border-radius: 4px;\">AKT1</span>\n",
       " - entity_positively_regulates_entity - leukemia</span>\n",
       "</div>\n",
       "<br>\n",
       "<div>\n",
       "    <span style=\"font-weight: bold; font-size: small;\">\n",
       "        TRAPI result MESH:D000068877, NCBIGene:836, MESH:D007938\n",
       "    </span> \n",
       "    (p-value: 2.7e-03)\n",
       "    <br>\n",
       "    Imatinib mesylate - increases_degradation_of - \n",
       "<span style=\"border: 1px orange solid; padding: 1px; border-radius: 4px;\">CASP3</span>\n",
       " - contributes_to - leukemia</span><br>Imatinib mesylate - increases_degradation_of - \n",
       "<span style=\"border: 1px orange solid; padding: 1px; border-radius: 4px;\">CASP3</span>\n",
       " - gene_associated_with_condition - leukemia</span><br>Imatinib mesylate - increases_degradation_of - \n",
       "<span style=\"border: 1px orange solid; padding: 1px; border-radius: 4px;\">CASP3</span>\n",
       " - correlated_with - leukemia</span><br>Imatinib mesylate - increases_activity_of - \n",
       "<span style=\"border: 1px orange solid; padding: 1px; border-radius: 4px;\">CASP3</span>\n",
       " - gene_associated_with_condition - leukemia</span><br>Imatinib mesylate - increases_activity_of - \n",
       "<span style=\"border: 1px orange solid; padding: 1px; border-radius: 4px;\">CASP3</span>\n",
       " - contributes_to - leukemia</span><br>Imatinib mesylate - increases_activity_of - \n",
       "<span style=\"border: 1px orange solid; padding: 1px; border-radius: 4px;\">CASP3</span>\n",
       " - correlated_with - leukemia</span>\n",
       "</div>\n",
       "<br>\n",
       "<div>\n",
       "    <span style=\"font-weight: bold; font-size: small;\">\n",
       "        TRAPI result MESH:D000068877, NCBIGene:841, MESH:D007938\n",
       "    </span> \n",
       "    (p-value: 2.7e-03)\n",
       "    <br>\n",
       "    Imatinib mesylate - increases_activity_of - \n",
       "<span style=\"border: 1px orange solid; padding: 1px; border-radius: 4px;\">CASP8</span>\n",
       " - gene_associated_with_condition - leukemia</span><br>Imatinib mesylate - increases_activity_of - \n",
       "<span style=\"border: 1px orange solid; padding: 1px; border-radius: 4px;\">CASP8</span>\n",
       " - correlated_with - leukemia</span>\n",
       "</div>\n",
       "<br>\n",
       "<div>\n",
       "    <span style=\"font-weight: bold; font-size: small;\">\n",
       "        TRAPI result MESH:D000068877, NCBIGene:842, MESH:D007938\n",
       "    </span> \n",
       "    (p-value: 2.7e-03)\n",
       "    <br>\n",
       "    Imatinib mesylate - increases_degradation_of - \n",
       "<span style=\"border: 1px orange solid; padding: 1px; border-radius: 4px;\">CASP9</span>\n",
       " - gene_associated_with_condition - leukemia</span><br>Imatinib mesylate - increases_degradation_of - \n",
       "<span style=\"border: 1px orange solid; padding: 1px; border-radius: 4px;\">CASP9</span>\n",
       " - correlated_with - leukemia</span>\n",
       "</div>\n"
      ],
      "text/plain": [
       "<IPython.core.display.HTML object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "\n",
       "<div style=\"border: 1px solid slategray; padding: 6px;\">\n",
       "    <figure style=\"margin: 5px !important;\">\n",
       "        <a target=\"_blank\" href=\"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5086377/\">\n",
       "          <img src=\"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5086377/bin/DM2016-9602472.001.jpg\" style=\"max-height: 500px; max-width: 100%;\">\n",
       "        </a>\n",
       "        <br>\n",
       "        <caption>\n",
       "            TNF signaling pathway anomalies through antiapoptotic signaling by TNFR1 helped by upregulation of TRAF1 and inhibition of proapoptotic TNFR1 signaling by BIRC1/3 caspase inactivation\n",
       "        </caption>\n",
       "    </figure>\n",
       "</div>\n"
      ],
      "text/plain": [
       "<IPython.core.display.HTML object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "\n",
       "<div>\n",
       "    <span style=\"font-weight: bold; font-size: small;\">\n",
       "        TRAPI result MESH:D000068877, NCBIGene:841, MESH:D007938\n",
       "    </span> \n",
       "    (p-value: 2.7e-03)\n",
       "    <br>\n",
       "    Imatinib mesylate - increases_activity_of - \n",
       "<span style=\"border: 1px orange solid; padding: 1px; border-radius: 4px;\">CASP8</span>\n",
       " - correlated_with - leukemia</span><br>Imatinib mesylate - increases_activity_of - \n",
       "<span style=\"border: 1px orange solid; padding: 1px; border-radius: 4px;\">CASP8</span>\n",
       " - gene_associated_with_condition - leukemia</span>\n",
       "</div>\n",
       "<br>\n",
       "<div>\n",
       "    <span style=\"font-weight: bold; font-size: small;\">\n",
       "        TRAPI result MESH:D000068877, NCBIGene:836, MESH:D007938\n",
       "    </span> \n",
       "    (p-value: 2.7e-03)\n",
       "    <br>\n",
       "    Imatinib mesylate - increases_degradation_of - \n",
       "<span style=\"border: 1px orange solid; padding: 1px; border-radius: 4px;\">CASP3</span>\n",
       " - contributes_to - leukemia</span><br>Imatinib mesylate - increases_degradation_of - \n",
       "<span style=\"border: 1px orange solid; padding: 1px; border-radius: 4px;\">CASP3</span>\n",
       " - gene_associated_with_condition - leukemia</span><br>Imatinib mesylate - increases_degradation_of - \n",
       "<span style=\"border: 1px orange solid; padding: 1px; border-radius: 4px;\">CASP3</span>\n",
       " - correlated_with - leukemia</span><br>Imatinib mesylate - increases_activity_of - \n",
       "<span style=\"border: 1px orange solid; padding: 1px; border-radius: 4px;\">CASP3</span>\n",
       " - correlated_with - leukemia</span><br>Imatinib mesylate - increases_activity_of - \n",
       "<span style=\"border: 1px orange solid; padding: 1px; border-radius: 4px;\">CASP3</span>\n",
       " - gene_associated_with_condition - leukemia</span><br>Imatinib mesylate - increases_activity_of - \n",
       "<span style=\"border: 1px orange solid; padding: 1px; border-radius: 4px;\">CASP3</span>\n",
       " - contributes_to - leukemia</span>\n",
       "</div>\n",
       "<br>\n",
       "<div>\n",
       "    <span style=\"font-weight: bold; font-size: small;\">\n",
       "        TRAPI result MESH:D000068877, NCBIGene:4067, MESH:D001249\n",
       "    </span> \n",
       "    (p-value: 2.7e-03)\n",
       "    <br>\n",
       "    Imatinib mesylate - response_decreased_by - \n",
       "<span style=\"border: 1px orange solid; padding: 1px; border-radius: 4px;\">LYN</span>\n",
       " - related_to - Asthma</span><br>Imatinib mesylate - response_affected_by - \n",
       "<span style=\"border: 1px orange solid; padding: 1px; border-radius: 4px;\">LYN</span>\n",
       " - related_to - Asthma</span>\n",
       "</div>\n",
       "<br>\n",
       "<div>\n",
       "    <span style=\"font-weight: bold; font-size: small;\">\n",
       "        TRAPI result MESH:D000068877, NCBIGene:841, MESH:D001249\n",
       "    </span> \n",
       "    (p-value: 2.7e-03)\n",
       "    <br>\n",
       "    Imatinib mesylate - increases_activity_of - \n",
       "<span style=\"border: 1px orange solid; padding: 1px; border-radius: 4px;\">CASP8</span>\n",
       " - gene_associated_with_condition - Asthma</span>\n",
       "</div>\n",
       "<br>\n",
       "<div>\n",
       "    <span style=\"font-weight: bold; font-size: small;\">\n",
       "        TRAPI result MESH:D000068877, NCBIGene:4067, MESH:D007938\n",
       "    </span> \n",
       "    (p-value: 2.7e-03)\n",
       "    <br>\n",
       "    Imatinib mesylate - response_affected_by - \n",
       "<span style=\"border: 1px orange solid; padding: 1px; border-radius: 4px;\">LYN</span>\n",
       " - affects - leukemia</span><br>Imatinib mesylate - response_decreased_by - \n",
       "<span style=\"border: 1px orange solid; padding: 1px; border-radius: 4px;\">LYN</span>\n",
       " - gene_associated_with_condition - leukemia</span><br>Imatinib mesylate - response_decreased_by - \n",
       "<span style=\"border: 1px orange solid; padding: 1px; border-radius: 4px;\">LYN</span>\n",
       " - affects - leukemia</span><br>Imatinib mesylate - response_affected_by - \n",
       "<span style=\"border: 1px orange solid; padding: 1px; border-radius: 4px;\">LYN</span>\n",
       " - gene_associated_with_condition - leukemia</span>\n",
       "</div>\n",
       "<br>\n",
       "<div>\n",
       "    <span style=\"font-weight: bold; font-size: small;\">\n",
       "        TRAPI result MESH:D000068877, NCBIGene:842, MESH:D007938\n",
       "    </span> \n",
       "    (p-value: 2.7e-03)\n",
       "    <br>\n",
       "    Imatinib mesylate - increases_degradation_of - \n",
       "<span style=\"border: 1px orange solid; padding: 1px; border-radius: 4px;\">CASP9</span>\n",
       " - gene_associated_with_condition - leukemia</span><br>Imatinib mesylate - increases_degradation_of - \n",
       "<span style=\"border: 1px orange solid; padding: 1px; border-radius: 4px;\">CASP9</span>\n",
       " - correlated_with - leukemia</span>\n",
       "</div>\n"
      ],
      "text/plain": [
       "<IPython.core.display.HTML object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "\n",
       "<div style=\"border: 1px solid slategray; padding: 6px;\">\n",
       "    <figure style=\"margin: 5px !important;\">\n",
       "        <a target=\"_blank\" href=\"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7432057/\">\n",
       "          <img src=\"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7432057/bin/ijms-21-05242-g003.jpg\" style=\"max-height: 500px; max-width: 100%;\">\n",
       "        </a>\n",
       "        <br>\n",
       "        <caption>\n",
       "            Adiponectin and leptin in the progression of nonalcoholic fatty liver disease (NAFLD) to hepatocellular carcinoma (HCC)\n",
       "        </caption>\n",
       "    </figure>\n",
       "</div>\n"
      ],
      "text/plain": [
       "<IPython.core.display.HTML object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "\n",
       "<div>\n",
       "    <span style=\"font-weight: bold; font-size: small;\">\n",
       "        TRAPI result MESH:D000068877, NCBIGene:836, MESH:D007938\n",
       "    </span> \n",
       "    (p-value: 2.7e-03)\n",
       "    <br>\n",
       "    Imatinib mesylate - increases_degradation_of - \n",
       "<span style=\"border: 1px orange solid; padding: 1px; border-radius: 4px;\">CASP3</span>\n",
       " - contributes_to - leukemia</span><br>Imatinib mesylate - increases_degradation_of - \n",
       "<span style=\"border: 1px orange solid; padding: 1px; border-radius: 4px;\">CASP3</span>\n",
       " - gene_associated_with_condition - leukemia</span><br>Imatinib mesylate - increases_degradation_of - \n",
       "<span style=\"border: 1px orange solid; padding: 1px; border-radius: 4px;\">CASP3</span>\n",
       " - correlated_with - leukemia</span><br>Imatinib mesylate - increases_activity_of - \n",
       "<span style=\"border: 1px orange solid; padding: 1px; border-radius: 4px;\">CASP3</span>\n",
       " - contributes_to - leukemia</span><br>Imatinib mesylate - increases_activity_of - \n",
       "<span style=\"border: 1px orange solid; padding: 1px; border-radius: 4px;\">CASP3</span>\n",
       " - correlated_with - leukemia</span><br>Imatinib mesylate - increases_activity_of - \n",
       "<span style=\"border: 1px orange solid; padding: 1px; border-radius: 4px;\">CASP3</span>\n",
       " - gene_associated_with_condition - leukemia</span>\n",
       "</div>\n",
       "<br>\n",
       "<div>\n",
       "    <span style=\"font-weight: bold; font-size: small;\">\n",
       "        TRAPI result MESH:D000068877, NCBIGene:207, MESH:D007938\n",
       "    </span> \n",
       "    (p-value: 2.7e-03)\n",
       "    <br>\n",
       "    Imatinib mesylate - decreases_molecular_modification_of - \n",
       "<span style=\"border: 1px orange solid; padding: 1px; border-radius: 4px;\">AKT1</span>\n",
       " - gene_associated_with_condition - leukemia</span><br>Imatinib mesylate - decreases_molecular_modification_of - \n",
       "<span style=\"border: 1px orange solid; padding: 1px; border-radius: 4px;\">AKT1</span>\n",
       " - related_to - leukemia</span><br>Imatinib mesylate - decreases_molecular_modification_of - \n",
       "<span style=\"border: 1px orange solid; padding: 1px; border-radius: 4px;\">AKT1</span>\n",
       " - contributes_to - leukemia</span><br>Imatinib mesylate - decreases_molecular_modification_of - \n",
       "<span style=\"border: 1px orange solid; padding: 1px; border-radius: 4px;\">AKT1</span>\n",
       " - entity_positively_regulates_entity - leukemia</span>\n",
       "</div>\n",
       "<br>\n",
       "<div>\n",
       "    <span style=\"font-weight: bold; font-size: small;\">\n",
       "        TRAPI result MESH:D000068877, NCBIGene:841, MESH:D007938\n",
       "    </span> \n",
       "    (p-value: 2.7e-03)\n",
       "    <br>\n",
       "    Imatinib mesylate - increases_activity_of - \n",
       "<span style=\"border: 1px orange solid; padding: 1px; border-radius: 4px;\">CASP8</span>\n",
       " - correlated_with - leukemia</span><br>Imatinib mesylate - increases_activity_of - \n",
       "<span style=\"border: 1px orange solid; padding: 1px; border-radius: 4px;\">CASP8</span>\n",
       " - gene_associated_with_condition - leukemia</span>\n",
       "</div>\n",
       "<br>\n",
       "<div>\n",
       "    <span style=\"font-weight: bold; font-size: small;\">\n",
       "        TRAPI result MESH:D000068877, NCBIGene:841, MESH:D001249\n",
       "    </span> \n",
       "    (p-value: 2.7e-03)\n",
       "    <br>\n",
       "    Imatinib mesylate - increases_activity_of - \n",
       "<span style=\"border: 1px orange solid; padding: 1px; border-radius: 4px;\">CASP8</span>\n",
       " - gene_associated_with_condition - Asthma</span>\n",
       "</div>\n",
       "<br>\n",
       "<div>\n",
       "    <span style=\"font-weight: bold; font-size: small;\">\n",
       "        TRAPI result MESH:D000068877, NCBIGene:842, MESH:D007938\n",
       "    </span> \n",
       "    (p-value: 2.7e-03)\n",
       "    <br>\n",
       "    Imatinib mesylate - increases_degradation_of - \n",
       "<span style=\"border: 1px orange solid; padding: 1px; border-radius: 4px;\">CASP9</span>\n",
       " - gene_associated_with_condition - leukemia</span><br>Imatinib mesylate - increases_degradation_of - \n",
       "<span style=\"border: 1px orange solid; padding: 1px; border-radius: 4px;\">CASP9</span>\n",
       " - correlated_with - leukemia</span>\n",
       "</div>\n"
      ],
      "text/plain": [
       "<IPython.core.display.HTML object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "...\n",
      "\n",
      "\n",
      "plus 13 more figures, each with TRAPI results\n",
      "\n",
      "\n"
     ]
    }
   ],
   "source": [
    "from IPython.display import Image\n",
    "from IPython.core.display import HTML \n",
    "\n",
    "\n",
    "outer_limit = 10\n",
    "inner_limit = 10\n",
    "\n",
    "count = 0\n",
    "for figure_id, one_figure_id_df in cooccurrence_df.groupby(\"figure_id\", sort=False):\n",
    "    [pmc,filename] = figure_id.split(\"__\")\n",
    "    paper_url = f\"https://www.ncbi.nlm.nih.gov/pmc/articles/{pmc}/\"\n",
    "    figure_url = f\"https://www.ncbi.nlm.nih.gov/pmc/articles/{pmc}/bin/{filename}\"\n",
    "    figure_title = list(set(one_figure_id_df[\"figure_title\"]))[0]\n",
    "\n",
    "    display(HTML(data=f'''\n",
    "<div style=\"border: 1px solid slategray; padding: 6px;\">\n",
    "    <figure style=\"margin: 5px !important;\">\n",
    "        <a target=\"_blank\" href=\"{paper_url}\">\n",
    "          <img src=\"{figure_url}\" style=\"max-height: 500px; max-width: 100%;\">\n",
    "        </a>\n",
    "        <br>\n",
    "        <caption>\n",
    "            {figure_title}\n",
    "        </caption>\n",
    "    </figure>\n",
    "</div>\n",
    "'''))\n",
    "\n",
    "    trapi_results_row_sets = []\n",
    "    \n",
    "    for trapi_result_curie_combo, one_trapi_result_curie_combo_df in one_figure_id_df[(\n",
    "        trapi_result_columns +\n",
    "        unified_curie_columns +\n",
    "        [\"trapi_result_curies_key\", \"common_curie_set\", \"p_value\"]\n",
    "    )].groupby(\"trapi_result_curies_key\", sort=False):\n",
    "        overlap_names = []\n",
    "        for curie in one_trapi_result_curie_combo_df[\"common_curie_set\"].tolist()[0]:\n",
    "            name = curie_to_name[curie]\n",
    "            overlap_names.append(name)\n",
    "\n",
    "        trapi_results_rows = []\n",
    "        for i,row in one_trapi_result_curie_combo_df.iterrows():\n",
    "            trapi_result_chunks = []\n",
    "            for trapi_result_column in trapi_result_columns:\n",
    "                row_value = row[trapi_result_column]\n",
    "                if row_value in overlap_names:\n",
    "                    trapi_result_chunk = f'''\n",
    "<span style=\"border: 1px orange solid; padding: 1px; border-radius: 4px;\">{row_value}</span>\n",
    "'''\n",
    "                else:\n",
    "                    trapi_result_chunk = f'{row_value}'\n",
    "                trapi_result_chunks.append(trapi_result_chunk)\n",
    "                \n",
    "            trapi_results_rows.append(\n",
    "                f'{\" - \".join(trapi_result_chunks)}</span>'\n",
    "            )\n",
    "        \n",
    "        ordered_trapi_result_curie_combo = []\n",
    "        for unified_curie_column in unified_curie_columns:\n",
    "            ordered_trapi_result_curie_combo.append(list(set(\n",
    "                one_trapi_result_curie_combo_df[unified_curie_column]\n",
    "            ))[0])\n",
    "        \n",
    "        p_value = list(set(one_trapi_result_curie_combo_df[\"p_value\"]))[0]\n",
    "        trapi_results_row_sets.append(f'''\n",
    "<div>\n",
    "    <span style=\"font-weight: bold; font-size: small;\">\n",
    "        TRAPI result {\", \".join(ordered_trapi_result_curie_combo)}\n",
    "    </span> \n",
    "    (p-value: {p_value:.1e})\n",
    "    <br>\n",
    "    {\"<br>\".join(trapi_results_rows)}\n",
    "</div>\n",
    "''')\n",
    "\n",
    "    trapi_results_row_set_count = len(trapi_results_row_sets)\n",
    "    display(HTML(data=\"<br>\".join(trapi_results_row_sets[:inner_limit])))\n",
    "    if trapi_results_row_set_count > inner_limit:\n",
    "        print(f\"...\")\n",
    "        print(\"\")\n",
    "        print(f\"plus {trapi_results_row_set_count - inner_limit} more TRAPI results\")\n",
    "    print(\"\")\n",
    "\n",
    "    count += 1\n",
    "    if count >= outer_limit:\n",
    "        print(\"...\")\n",
    "        print(\"\")\n",
    "        print(f'''\n",
    "plus {len(set(cooccurrence_df[\"figure_id\"])) - outer_limit} more figures, each with TRAPI results\n",
    "''')\n",
    "        print(\"\")\n",
    "        break"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
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