PET_notebook_v1.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "view-in-github"
   },
   "source": [
    "<a href=\"https://colab.research.google.com/github/wikipathways/BioThings_Explorer_PFOCR_prioritization/blob/main/PET_notebook_v1.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# PET Notebook\n",
    "\n",
    "### A notebook for exploring Pathway Enriched TRAPI results\n",
    "\n",
    "For interactive exploration of TRAPI results using the PFOCR pathway figure database to help interpret results by the enrichment of nodes (CURIEs) representing genes, proteins, drugs, metabolites, and diseases.\n",
    "\n",
    "Run the entire notebook to process an example TRAPI response. Paste your own result URL into the User Inputs form below and re-run all cells below it. Then scroll down to the Overview of Pathway Figure Results section to explore your own results.\n",
    "\n",
    "Features:\n",
    " * Toggle code view - this button will hide all code chunks (might not work in Google Colab, which has its own chunk hiding features)\n",
    " * Maximum number of pathway results - default is 15. Reduce this by slider or by typing to focus on fewer results.\n",
    " * Require specified query nodes in pathway results - default is True. Deselect if there are too few pathway results or if you really don't particularly care about the nodes specified in your query."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Setup\n",
    "\n",
    "Installation of required packages. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [],
   "source": [
    "!pip install numpy pandas requests requests_cache SetSimilaritySearch fisher > pip-log.txt"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {
    "id": "BrREAxe69lsV"
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "\n",
       "<script>\n",
       "code_show=true; \n",
       "function code_toggle() {\n",
       " if (code_show){\n",
       " $('div.input').hide();\n",
       " } else {\n",
       " $('div.input').show();\n",
       " }\n",
       " code_show = !code_show\n",
       "} \n",
       "$( document ).ready(code_toggle);\n",
       "</script>\n",
       "<form action=\"javascript:code_toggle()\"><input type=\"submit\" value=\"Toggle code view\"></form>\n"
      ],
      "text/plain": [
       "<IPython.core.display.HTML object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "from IPython.display import display, HTML\n",
    "display(HTML('''\n",
    "<script>\n",
    "code_show=true; \n",
    "function code_toggle() {\n",
    " if (code_show){\n",
    " $('div.input').hide();\n",
    " } else {\n",
    " $('div.input').show();\n",
    " }\n",
    " code_show = !code_show\n",
    "} \n",
    "$( document ).ready(code_toggle);\n",
    "</script>\n",
    "<form action=\"javascript:code_toggle()\"><input type=\"submit\" value=\"Toggle code view\"></form>\n",
    "'''))\n",
    "#load packages\n",
    "from urllib.request import urlopen\n",
    "import requests\n",
    "import json\n",
    "import numpy as np\n",
    "from copy import copy,deepcopy\n",
    "import pandas as pd\n",
    "from fisher import pvalue_npy\n",
    "from scipy.stats import fisher_exact\n",
    "# Importing reduce for rolling computations\n",
    "from functools import reduce\n",
    "import ipywidgets as widgets\n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns\n",
    "from matplotlib.colors import LinearSegmentedColormap\n",
    "from ipywidgets import Button"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## User Inputs\n",
    "\n",
    "Please complete the form below."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "9e70ff3094e847e4ad0726e3a7eeda7d",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "Box(children=(VBox(children=(HBox(children=(Label(value='TRAPI Result URL:'), Text(value='https://arax.ncats.i…"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# TRAPI Result URL\n",
    "trapi_results_url = widgets.Text(\n",
    "    value='https://arax.ncats.io/api/arax/v1.4/response/9930a1e1-20da-4770-b82f-dc383395dc51',\n",
    "    placeholder='Paste here',\n",
    "    description='',\n",
    "    disabled=False\n",
    ")\n",
    "\n",
    "# Max number of pathway results desired\n",
    "num_pathways = widgets.IntSlider(\n",
    "    value=15,\n",
    "    min=1,\n",
    "    max=15,\n",
    "    step=1,\n",
    "    description='',\n",
    "    disabled=False,\n",
    "    continuous_update=False,\n",
    "    orientation='horizontal',\n",
    "    readout=True,\n",
    "    readout_format='d'\n",
    ")\n",
    "\n",
    "# Whether to require figure results include user-specified node IDs from the query\n",
    "# If true then atleast one of the specified query node ids must be in the result figures\n",
    "# If false then figures without any of these node ids are also included\n",
    "require_specified_query_nodes = widgets.Checkbox(\n",
    "    value=True,\n",
    "    description='',\n",
    "    disabled=False,\n",
    "    indent=False\n",
    ")\n",
    "require_specified_query_nodes\n",
    "\n",
    "#define the button\n",
    "button = Button(description='Run all cells below')\n",
    "from IPython.display import display, Javascript\n",
    "\n",
    "def run_all_cells_below(b):\n",
    "    display(Javascript('IPython.notebook.execute_cell_range(IPython.notebook.get_selected_index()+1, IPython.notebook.ncells())'))\n",
    "button.on_click(run_all_cells_below)\n",
    "\n",
    "box_items = [widgets.VBox([widgets.HBox([widgets.Label('TRAPI Result URL:'),trapi_results_url]),\n",
    "                        widgets.HBox([widgets.Label('Maxiumum number of desired pathway results:'),num_pathways]),\n",
    "                        widgets.HBox([widgets.Label('Require specified query nodes in pathway results:'),require_specified_query_nodes])]),\n",
    "            button]\n",
    "widgets.Box(box_items, layout=widgets.Layout(\n",
    "    display='flex',\n",
    "    flex_flow='column',\n",
    "    border='solid 2px',\n",
    "    align_items='stretch',\n",
    "    width='62%'\n",
    "))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Load TRAPI Results\n",
    "\n",
    "Retrieve the JSON response from the provided URL."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Summary: query_graph: 2 edges, 3 nodes; results: 1600\n"
     ]
    }
   ],
   "source": [
    "with urlopen(trapi_results_url.value) as url:\n",
    "    trapi_results_json = json.load(url)\n",
    "\n",
    "# get the TRAPI results and knowledge graph from the json response\n",
    "trapi_message = trapi_results_json['message']\n",
    "trapi_query = trapi_message[\"query_graph\"]\n",
    "trapi_results = trapi_message[\"results\"]\n",
    "trapi_results_df = pd.json_normalize(trapi_results)\n",
    "\n",
    "# prepare results as a human-readable dataframe\n",
    "for c in list(trapi_results_df.columns):\n",
    "    if isinstance(trapi_results_df[c][0], list):\n",
    "        trapi_results_df[c] = trapi_results_df[c].str[0].str['id']\n",
    "        \n",
    "print('Summary: query_graph:',\n",
    "      len(trapi_query['edges'].items()),'edges,',\n",
    "      len(trapi_query['nodes'].items()),'nodes; results:',\n",
    "      len(trapi_results) )\n",
    "#display(trapi_results_df)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Normalize TRAPI Result Dataframe for Enrichment Analysis\n",
    "  \n",
    "Unify node identifiers to sources supported by PFOCR pathway figures (NCBIGene, MESH) and prepare CURIE sets for enrichment analysis."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {
    "scrolled": false
   },
   "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>normalized_score</th>\n",
       "      <th>resource_id</th>\n",
       "      <th>score</th>\n",
       "      <th>node_name.n0</th>\n",
       "      <th>node_name.n1</th>\n",
       "      <th>node_name.n2</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>100.00000</td>\n",
       "      <td>BTE</td>\n",
       "      <td>0.978769</td>\n",
       "      <td>SLC5A2</td>\n",
       "      <td>type 2 diabetes mellitus</td>\n",
       "      <td>Metformin</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>99.93750</td>\n",
       "      <td>BTE</td>\n",
       "      <td>0.976018</td>\n",
       "      <td>STK11</td>\n",
       "      <td>type 2 diabetes mellitus</td>\n",
       "      <td>Metformin</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>99.87500</td>\n",
       "      <td>BTE</td>\n",
       "      <td>0.973743</td>\n",
       "      <td>POU2F1</td>\n",
       "      <td>type 2 diabetes mellitus</td>\n",
       "      <td>Metformin</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>99.81250</td>\n",
       "      <td>BTE</td>\n",
       "      <td>0.967913</td>\n",
       "      <td>CCL2</td>\n",
       "      <td>type 2 diabetes mellitus</td>\n",
       "      <td>Metformin</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>99.75000</td>\n",
       "      <td>BTE</td>\n",
       "      <td>0.966776</td>\n",
       "      <td>HGS</td>\n",
       "      <td>type 2 diabetes mellitus</td>\n",
       "      <td>Metformin</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",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1595</th>\n",
       "      <td>10.96875</td>\n",
       "      <td>BTE</td>\n",
       "      <td>0.090659</td>\n",
       "      <td>SRRM1</td>\n",
       "      <td>type 2 diabetes mellitus</td>\n",
       "      <td>Metformin</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1596</th>\n",
       "      <td>10.96875</td>\n",
       "      <td>BTE</td>\n",
       "      <td>0.090659</td>\n",
       "      <td>XPNPEP1</td>\n",
       "      <td>type 2 diabetes mellitus</td>\n",
       "      <td>Metformin</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1597</th>\n",
       "      <td>10.96875</td>\n",
       "      <td>BTE</td>\n",
       "      <td>0.090659</td>\n",
       "      <td>PITX3</td>\n",
       "      <td>type 2 diabetes mellitus</td>\n",
       "      <td>Metformin</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1598</th>\n",
       "      <td>10.96875</td>\n",
       "      <td>BTE</td>\n",
       "      <td>0.090659</td>\n",
       "      <td>GLI3</td>\n",
       "      <td>type 2 diabetes mellitus</td>\n",
       "      <td>Metformin</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1599</th>\n",
       "      <td>10.96875</td>\n",
       "      <td>BTE</td>\n",
       "      <td>0.090659</td>\n",
       "      <td>VNN1</td>\n",
       "      <td>type 2 diabetes mellitus</td>\n",
       "      <td>Metformin</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>1600 rows × 6 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "      normalized_score resource_id     score node_name.n0  \\\n",
       "0            100.00000         BTE  0.978769       SLC5A2   \n",
       "1             99.93750         BTE  0.976018        STK11   \n",
       "2             99.87500         BTE  0.973743       POU2F1   \n",
       "3             99.81250         BTE  0.967913         CCL2   \n",
       "4             99.75000         BTE  0.966776          HGS   \n",
       "...                ...         ...       ...          ...   \n",
       "1595          10.96875         BTE  0.090659        SRRM1   \n",
       "1596          10.96875         BTE  0.090659      XPNPEP1   \n",
       "1597          10.96875         BTE  0.090659        PITX3   \n",
       "1598          10.96875         BTE  0.090659         GLI3   \n",
       "1599          10.96875         BTE  0.090659         VNN1   \n",
       "\n",
       "                  node_name.n1 node_name.n2  \n",
       "0     type 2 diabetes mellitus    Metformin  \n",
       "1     type 2 diabetes mellitus    Metformin  \n",
       "2     type 2 diabetes mellitus    Metformin  \n",
       "3     type 2 diabetes mellitus    Metformin  \n",
       "4     type 2 diabetes mellitus    Metformin  \n",
       "...                        ...          ...  \n",
       "1595  type 2 diabetes mellitus    Metformin  \n",
       "1596  type 2 diabetes mellitus    Metformin  \n",
       "1597  type 2 diabetes mellitus    Metformin  \n",
       "1598  type 2 diabetes mellitus    Metformin  \n",
       "1599  type 2 diabetes mellitus    Metformin  \n",
       "\n",
       "[1600 rows x 6 columns]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Using the xrefs in knowledge_graph node attributes we are building dictionaries of \n",
    "# result CURIEs and names.\n",
    "preferred_prefixes = set([\"NCBIGene\", \"MESH\"])\n",
    "curie_to_name = dict()\n",
    "curie_to_unified_curies = dict()\n",
    "for k, v in trapi_message[\"knowledge_graph\"][\"nodes\"].items():\n",
    "    #first check if k is already what we want\n",
    "    [prefix, identifier] = k.split(\":\")\n",
    "    if prefix in preferred_prefixes:\n",
    "        curie_to_unified_curies[k] = k\n",
    "    #else dig into attributes to collect one or more relevant mappings\n",
    "    else:            \n",
    "        for a in v[\"attributes\"]:\n",
    "            if a[\"attribute_type_id\"] == \"biolink:xref\":\n",
    "                curies = a[\"value\"]\n",
    "                unified_curies = set()\n",
    "                for curie in curies:\n",
    "                    [prefix, identifier] = curie.split(\":\")\n",
    "                    if prefix in preferred_prefixes:\n",
    "                        unified_curies.add(curie)\n",
    "                #assess collected curies\n",
    "                if len(unified_curies) == 1:\n",
    "                    curie_to_unified_curies[k] = list(unified_curies)[0]\n",
    "                elif len(unified_curies) > 1:\n",
    "                    curie_to_unified_curies[k] = unified_curies\n",
    "    #get the node name\n",
    "    if \"name\" in v:\n",
    "        curie_to_name[k] = v[\"name\"]\n",
    "    elif k in curie_to_unified_curies.keys():\n",
    "        curie_to_name[k] = curie_to_unified_curies[k]\n",
    "    else:\n",
    "        curie_to_name[k] = \"NaN\"\n",
    "                    \n",
    "# Append new columns to results df: name and unified_curie\n",
    "columns_to_lookup = [col for col in trapi_results_df.columns if col.startswith(\"node_bindings\") and col[-2].lower() == 'n'  and col[-1].isdigit()]\n",
    "new_name_columns = [col.replace(\"bindings\",\"name\") for col in columns_to_lookup]\n",
    "new_unified_curies_columns = [col.replace(\"bindings\",\"unified_curies\") for col in columns_to_lookup]\n",
    "\n",
    "# Simple lookup function\n",
    "def lookup(row, col, lookup_dict):\n",
    "    return lookup_dict.get(row[col], 'NA')\n",
    "\n",
    "# Add the new columns per node by applying the lookup function\n",
    "for i in range(len(columns_to_lookup)):\n",
    "    trapi_results_df[new_name_columns[i]] = trapi_results_df.apply(lambda row: lookup(row, columns_to_lookup[i], curie_to_name), axis=1)\n",
    "    trapi_results_df[new_unified_curies_columns[i]] = trapi_results_df.apply(lambda row: lookup(row, columns_to_lookup[i], curie_to_unified_curies), axis=1)\n",
    "\n",
    "# Append a new columns: result_name and unified_curie_set for pathway analysis\n",
    "#trapi_results_df['result_name'] = trapi_results_df[new_name_columns[0]].str.cat(trapi_results_df[new_name_columns], sep=\"_\")\n",
    "trapi_results_df['result_name'] = trapi_results_df.reset_index().index.astype(str)\n",
    "trapi_results_df['result_name'] = trapi_results_df['result_name'].str.cat(trapi_results_df[new_name_columns], sep='_')\n",
    "\n",
    "\n",
    "def collect_curie_set(row):\n",
    "    unified_curie_set = set()\n",
    "    for c in new_unified_curies_columns:\n",
    "        if isinstance(row[c], set):\n",
    "            unified_curie_set.update(row[c])\n",
    "        else:\n",
    "            unified_curie_set.add(row[c])\n",
    "    return unified_curie_set\n",
    "trapi_results_df['unified_curie_set'] = trapi_results_df.apply(collect_curie_set, axis=1)\n",
    "display_columns = ['normalized_score','resource_id','score']\n",
    "display_columns.extend(new_name_columns)\n",
    "display(trapi_results_df[display_columns])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Collect CURIEs specified in query\n",
    "\n",
    "Prepare a set of unified CURIEs from the specified query nodes. This set will be used to restrict or prioritize enrichment results based on the user parameter: `require_specified_query_nodes`."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "{'MESH:D008687', 'MESH:D003924'}\n"
     ]
    }
   ],
   "source": [
    "#get all nodes with ids from the query \n",
    "user_specified_ids = set()\n",
    "for k, v in trapi_query['nodes'].items():\n",
    "    node_ids = v.get('ids', [])\n",
    "    if node_ids:\n",
    "        n = trapi_results[0]['node_bindings'][k][0]['id']\n",
    "        c = curie_to_unified_curies.get(n)\n",
    "        if isinstance(c, set):\n",
    "            user_specified_ids.update(c)\n",
    "        else:\n",
    "            user_specified_ids.add(c)\n",
    "print(user_specified_ids)        "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Load PFOCR Pathway Figure Database\n",
    "\n",
    "Load pathway figure content and metadata for enrichment analysis."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [],
   "source": [
    "# read in figure metadata for all PFOCR figures\n",
    "figure_metadata_df = pd.read_csv('https://www.dropbox.com/s/9jorbzpq2k8n5tr/figures_metadata.csv?dl=1', index_col=0)\n",
    "\n",
    "# read in figure CURIEs for all PFOCR figures\n",
    "figure_curie_df = pd.read_csv('https://www.dropbox.com/s/y3ks74obs3nrjjz/figures_curie.csv?dl=1')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Total number of PFOCR pathways:  77719\n",
      "Total number of PFOCR pathway CURIEs:  30163\n",
      "Total number of TRAPI result CURIEs:  1454\n"
     ]
    }
   ],
   "source": [
    "# Define total sets of unified curies for enrichment test\n",
    "total_pfocr_pathways = len(figure_metadata_df.index)\n",
    "print(\"Total number of PFOCR pathways: \", total_pfocr_pathways)\n",
    "total_pfocr_curies = set(figure_curie_df['curie'])\n",
    "print(\"Total number of PFOCR pathway CURIEs: \", len(total_pfocr_curies))\n",
    "total_result_curies = set([item for sub_set in trapi_results_df['unified_curie_set'] for item in sub_set])\n",
    "total_result_curies_possible = total_result_curies.intersection(total_pfocr_curies) #only consider possible matches\n",
    "print(\"Total number of TRAPI result CURIEs: \", len(total_result_curies_possible))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Iterative Enrichment of TRAPI Results Using PFOCR Pathway Figures\n",
    "\n",
    "We identify the top PFOCR figures by iteratively running Fisher's Exact Test to identify PFOCR pathway figures that are most similar to all the CURIEs from the TRAPI results. To get each 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 found in any PFOCR pathway figure\n",
    "2. Select the pathway figure with the lowest p-value\n",
    "3. Exclude that pathway figure and the CURIEs in it\n",
    "4. Repeat to get the next pathway figure, continuing until:\n",
    "    - we run out of enrichement results\n",
    "    - we repeated steps 1-3 the desired number of times (user input: ```num_pathways```)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Definition the parameters with example values for enrichment test\n",
    "# M = 30163  # Total number of curies in PFOCR pathway figure database\n",
    "# N = 571   # Total number of curies in TRAPI result set\n",
    "# x = 10   # Number of curies in the TRAPI result set that belong to a particular pathway\n",
    "# k = 24   # Number of curies in a particular pathway \n",
    "\n",
    "#reset the set of required CURIES\n",
    "required_curies = set()\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",
    "def get_results_by_curies(curie_set):\n",
    "    subset_df = trapi_results_df[trapi_results_df['unified_curie_set'].apply(lambda x: len(x.intersection(curie_set)) > 0)]\n",
    "    return set(subset_df['result_name'].tolist())\n",
    "\n",
    "pathway_figure_data = []\n",
    "def get_next_pathway_figures(\n",
    "    possible_matched_pfocr_curie_count,\n",
    "    remaining_figures_df,\n",
    "    remaining_trapi_curie_count,\n",
    "    required_curies,\n",
    "    iteration_limit=num_pathways.value, #this is the user-defined parameter for the number of figures\n",
    "    i=0,\n",
    "    pathway_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 pathway_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 = x\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-x\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 = k-x\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 = M-k-(N-x)\n",
    "    # ie., number of unique CURIEs only in other figures\n",
    "    remaining_figures_df[\"no_trapi_results_no_figure\"] = (\n",
    "        possible_matched_pfocr_curie_count -\n",
    "        remaining_figures_df[\"figure_curie_count\"] -\n",
    "        remaining_figures_df[\"yes_trapi_results_no_figure\"]\n",
    "    )\n",
    "\n",
    "    # Vectorized fisher_exact test, see https://stackoverflow.com/a/58661068/5354298\n",
    "    # Contingency table values (in arg order): x, N-x, k-x, M-k-(N-x) \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",
    "    # In case of a p_value tie, we sort by yes_trapi_results_yes_figure count and keep the top single hit\n",
    "    min_df = remaining_figures_df[\n",
    "        remaining_figures_df[\"p_value\"] == remaining_figures_df[\"p_value\"].min()\n",
    "    ]\n",
    "    min_df = min_df.sort_values(by='yes_trapi_results_yes_figure', ascending=False).head(1)\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 required CURIEs.\n",
    "        return pathway_figure_ids\n",
    "        \n",
    "    new_pathway_figure_ids = min_df.index.tolist()\n",
    "    pathway_figure_ids.extend(new_pathway_figure_ids)\n",
    "    \n",
    "    for pathway_figure_id, min_row in min_df.iterrows():\n",
    "        pathway_figure_data.append({\n",
    "            \"iteration\": i,\n",
    "            \"figure_id\": pathway_figure_id,\n",
    "            \"p_value\": min_row[\"p_value\"],\n",
    "            \"fraction_of_trapi_curies_in_figure\" : min_row[\"yes_trapi_results_yes_figure\"]/len(total_result_curies_possible),\n",
    "            \"fraction_of_figure_curies_in_trapi_results\" : min_row[\"yes_trapi_results_yes_figure\"]/(min_row[\"yes_trapi_results_yes_figure\"] + min_row[\"no_trapi_results_yes_figure\"]),\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",
    "            \"trapi_result_set\": get_results_by_curies(curies_to_exclude),\n",
    "            \"trapi_result_count\": 0,\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",
    "            \"pathway_figure_id_count\": len(pathway_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",
    "            pathway_figure_ids\n",
    "        ))\n",
    "    ]\n",
    "    \n",
    "    #print(f'iteration {i:>2}: figures: {len(new_pathway_figure_ids):>2}, CURIEs: {len(curies_to_exclude):>2}')\n",
    "    \n",
    "    return get_next_pathway_figures(\n",
    "        possible_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",
    "        pathway_figure_ids,\n",
    "    )\n",
    "\n",
    "possible_matched_figure_curie_df = figure_curie_df[figure_curie_df[\"curie\"].isin(\n",
    "    list(total_pfocr_curies)\n",
    ")]\n",
    "possible_matched_pfocr_curie_count = len(set(\n",
    "    possible_matched_figure_curie_df[\"curie\"].drop_duplicates()\n",
    "))\n",
    "#print(f'possible_matched_pfocr_curie_count: {possible_matched_pfocr_curie_count}')\n",
    "\n",
    "possible_matched_figures_df = possible_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",
    "possible_matched_figures_df[\"figure_curies_in_trapi_results_count\"] = possible_matched_figures_df[\n",
    "    \"curie_set\"\n",
    "].map(\n",
    "    lambda curie_set: len(total_result_curies_possible.intersection(\n",
    "        set(curie_set)\n",
    "    ))\n",
    ")\n",
    "\n",
    "overlapping_possible_matched_figures_df = possible_matched_figures_df[\n",
    "    possible_matched_figures_df[\"figure_curies_in_trapi_results_count\"] > 0\n",
    "]\n",
    "\n",
    "# Require that figures have user-specified CURIEs from the query\n",
    "if require_specified_query_nodes.value:\n",
    "    required_curies = user_specified_ids\n",
    "if required_curies:\n",
    "    overlapping_possible_matched_figures_df = overlapping_possible_matched_figures_df[\n",
    "        overlapping_possible_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",
    "pathway_figure_ids_out = get_next_pathway_figures(\n",
    "    possible_matched_pfocr_curie_count,\n",
    "    overlapping_possible_matched_figures_df.reset_index().set_index(\"figure_id\"),\n",
    "    total_result_curies_possible,\n",
    "    required_curies,\n",
    ")\n",
    "#print(f'pathway figure count: {len(pathway_figure_ids_out)}')\n",
    "pathway_figure_df = pd.DataFrame.from_records(pathway_figure_data)\n",
    "pathway_figure_df['trapi_result_count'] = pathway_figure_df['trapi_result_set'].apply(lambda x: len(x))\n",
    "#display(pathway_figure_df)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "        vertical-align: top;\n",
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       "    }\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>fraction_of_trapi_curies_in_figure</th>\n",
       "      <th>fraction_of_figure_curies_in_trapi_results</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>trapi_result_set</th>\n",
       "      <th>trapi_result_count</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>pathway_figure_id_count</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0</td>\n",
       "      <td>PMC5709209__nihms897483f1.jpg</td>\n",
       "      <td>1.242422e-08</td>\n",
       "      <td>0.004127</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>6</td>\n",
       "      <td>1448</td>\n",
       "      <td>0</td>\n",
       "      <td>28709</td>\n",
       "      <td>{MESH:D008687, NCBIGene:3716, NCBIGene:51135, ...</td>\n",
       "      <td>{449_JAK2_type 2 diabetes mellitus_Metformin, ...</td>\n",
       "      <td>5</td>\n",
       "      <td>{MESH:D008687, NCBIGene:3716, NCBIGene:51135, ...</td>\n",
       "      <td>{NCBIGene:3716, NCBIGene:51135, NCBIGene:7297,...</td>\n",
       "      <td>5</td>\n",
       "      <td>298</td>\n",
       "      <td>1454</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1</td>\n",
       "      <td>PMC4160009__RevDiabeticStud-10-228-001.jpg</td>\n",
       "      <td>6.147351e-08</td>\n",
       "      <td>0.006878</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>10</td>\n",
       "      <td>1439</td>\n",
       "      <td>0</td>\n",
       "      <td>28714</td>\n",
       "      <td>{NCBIGene:5571, MESH:D008687, NCBIGene:200186,...</td>\n",
       "      <td>{623_PRKAB1_type 2 diabetes mellitus_Metformin...</td>\n",
       "      <td>9</td>\n",
       "      <td>{NCBIGene:5571, MESH:D008687, NCBIGene:200186,...</td>\n",
       "      <td>{NCBIGene:5571, NCBIGene:200186, NCBIGene:5562...</td>\n",
       "      <td>9</td>\n",
       "      <td>297</td>\n",
       "      <td>1449</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2</td>\n",
       "      <td>PMC4484428__oncotarget-06-10908-g008.jpg</td>\n",
       "      <td>7.516325e-08</td>\n",
       "      <td>0.006878</td>\n",
       "      <td>0.526316</td>\n",
       "      <td>10</td>\n",
       "      <td>1430</td>\n",
       "      <td>9</td>\n",
       "      <td>28714</td>\n",
       "      <td>{MESH:D008687, NCBIGene:5228, NCBIGene:2277, N...</td>\n",
       "      <td>{50_SIRT1_type 2 diabetes mellitus_Metformin, ...</td>\n",
       "      <td>9</td>\n",
       "      <td>{NCBIGene:5571, NCBIGene:2277, NCBIGene:5228, ...</td>\n",
       "      <td>{NCBIGene:5228, NCBIGene:2277, NCBIGene:3569, ...</td>\n",
       "      <td>9</td>\n",
       "      <td>296</td>\n",
       "      <td>1440</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>3</td>\n",
       "      <td>PMC6110909__fendo-09-00472-g0001.jpg</td>\n",
       "      <td>7.122699e-08</td>\n",
       "      <td>0.006878</td>\n",
       "      <td>0.526316</td>\n",
       "      <td>10</td>\n",
       "      <td>1421</td>\n",
       "      <td>9</td>\n",
       "      <td>28723</td>\n",
       "      <td>{NCBIGene:2475, MESH:D008687, NCBIGene:7249, N...</td>\n",
       "      <td>{902_CRTC1_type 2 diabetes mellitus_Metformin,...</td>\n",
       "      <td>9</td>\n",
       "      <td>{NCBIGene:5571, NCBIGene:7249, NCBIGene:51422,...</td>\n",
       "      <td>{NCBIGene:2475, NCBIGene:7249, NCBIGene:23373,...</td>\n",
       "      <td>9</td>\n",
       "      <td>295</td>\n",
       "      <td>1431</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>4</td>\n",
       "      <td>PMC3659509__MI2013-750540.001.jpg</td>\n",
       "      <td>8.970538e-08</td>\n",
       "      <td>0.014443</td>\n",
       "      <td>0.840000</td>\n",
       "      <td>21</td>\n",
       "      <td>1401</td>\n",
       "      <td>4</td>\n",
       "      <td>28737</td>\n",
       "      <td>{NCBIGene:5575, NCBIGene:488, NCBIGene:5573, N...</td>\n",
       "      <td>{1261_PRKACA_type 2 diabetes mellitus_Metformi...</td>\n",
       "      <td>20</td>\n",
       "      <td>{NCBIGene:5575, NCBIGene:488, NCBIGene:5573, N...</td>\n",
       "      <td>{NCBIGene:5575, NCBIGene:5465, NCBIGene:487, N...</td>\n",
       "      <td>20</td>\n",
       "      <td>294</td>\n",
       "      <td>1422</td>\n",
       "      <td>5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>5</td>\n",
       "      <td>PMC7076893__12906_2020_2871_Fig8_HTML.jpg</td>\n",
       "      <td>1.041298e-07</td>\n",
       "      <td>0.008253</td>\n",
       "      <td>0.600000</td>\n",
       "      <td>12</td>\n",
       "      <td>1390</td>\n",
       "      <td>8</td>\n",
       "      <td>28753</td>\n",
       "      <td>{NCBIGene:5580, NCBIGene:3643, NCBIGene:3630, ...</td>\n",
       "      <td>{515_MAPK1_type 2 diabetes mellitus_Metformin,...</td>\n",
       "      <td>11</td>\n",
       "      <td>{MESH:C043561, NCBIGene:3630, MESH:D006943, NC...</td>\n",
       "      <td>{NCBIGene:5580, NCBIGene:3643, NCBIGene:3630, ...</td>\n",
       "      <td>11</td>\n",
       "      <td>293</td>\n",
       "      <td>1402</td>\n",
       "      <td>6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>6</td>\n",
       "      <td>PMC3884529__neo1512_1379_fig006.jpg</td>\n",
       "      <td>9.734804e-08</td>\n",
       "      <td>0.006190</td>\n",
       "      <td>0.450000</td>\n",
       "      <td>9</td>\n",
       "      <td>1382</td>\n",
       "      <td>11</td>\n",
       "      <td>28761</td>\n",
       "      <td>{NCBIGene:5290, NCBIGene:5293, MESH:D008687, N...</td>\n",
       "      <td>{503_PIK3CA_type 2 diabetes mellitus_Metformin...</td>\n",
       "      <td>8</td>\n",
       "      <td>{NCBIGene:5571, NCBIGene:23533, NCBIGene:51422...</td>\n",
       "      <td>{NCBIGene:5290, NCBIGene:23533, NCBIGene:14685...</td>\n",
       "      <td>8</td>\n",
       "      <td>292</td>\n",
       "      <td>1391</td>\n",
       "      <td>7</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>7</td>\n",
       "      <td>PMC2936028__bph0160-1195-f8.jpg</td>\n",
       "      <td>1.076956e-07</td>\n",
       "      <td>0.012380</td>\n",
       "      <td>0.692308</td>\n",
       "      <td>18</td>\n",
       "      <td>1365</td>\n",
       "      <td>8</td>\n",
       "      <td>28772</td>\n",
       "      <td>{NCBIGene:5588, NCBIGene:5581, NCBIGene:5582, ...</td>\n",
       "      <td>{438_MAPK9_type 2 diabetes mellitus_Metformin,...</td>\n",
       "      <td>17</td>\n",
       "      <td>{NCBIGene:5581, NCBIGene:5582, MESH:C068811, N...</td>\n",
       "      <td>{NCBIGene:5588, NCBIGene:5581, NCBIGene:5582, ...</td>\n",
       "      <td>17</td>\n",
       "      <td>291</td>\n",
       "      <td>1383</td>\n",
       "      <td>8</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>8</td>\n",
       "      <td>PMC7069501__nihms-1022857-f0003.jpg</td>\n",
       "      <td>1.216332e-07</td>\n",
       "      <td>0.008941</td>\n",
       "      <td>0.393939</td>\n",
       "      <td>13</td>\n",
       "      <td>1353</td>\n",
       "      <td>20</td>\n",
       "      <td>28777</td>\n",
       "      <td>{NCBIGene:7124, NCBIGene:5605, NCBIGene:64127,...</td>\n",
       "      <td>{960_MAP2K1_type 2 diabetes mellitus_Metformin...</td>\n",
       "      <td>12</td>\n",
       "      <td>{NCBIGene:7124, NCBIGene:5605, NCBIGene:948, N...</td>\n",
       "      <td>{NCBIGene:7124, NCBIGene:5605, NCBIGene:64127,...</td>\n",
       "      <td>12</td>\n",
       "      <td>290</td>\n",
       "      <td>1366</td>\n",
       "      <td>9</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>9</td>\n",
       "      <td>PMC4775314__nihms754130f3.jpg</td>\n",
       "      <td>1.298503e-07</td>\n",
       "      <td>0.006190</td>\n",
       "      <td>0.428571</td>\n",
       "      <td>9</td>\n",
       "      <td>1345</td>\n",
       "      <td>12</td>\n",
       "      <td>28797</td>\n",
       "      <td>{NCBIGene:56731, NCBIGene:7046, NCBIGene:6774,...</td>\n",
       "      <td>{801_PDGFRB_type 2 diabetes mellitus_Metformin...</td>\n",
       "      <td>8</td>\n",
       "      <td>{MESH:C448039, MESH:D011392, NCBIGene:7048, NC...</td>\n",
       "      <td>{NCBIGene:56731, NCBIGene:7046, NCBIGene:6774,...</td>\n",
       "      <td>8</td>\n",
       "      <td>289</td>\n",
       "      <td>1354</td>\n",
       "      <td>10</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>10</td>\n",
       "      <td>PMC6721652__cells-08-00826-g003.jpg</td>\n",
       "      <td>1.536512e-07</td>\n",
       "      <td>0.019257</td>\n",
       "      <td>0.571429</td>\n",
       "      <td>28</td>\n",
       "      <td>1318</td>\n",
       "      <td>21</td>\n",
       "      <td>28796</td>\n",
       "      <td>{NCBIGene:2932, NCBIGene:7472, NCBIGene:51384,...</td>\n",
       "      <td>{1010_AXIN1_type 2 diabetes mellitus_Metformin...</td>\n",
       "      <td>27</td>\n",
       "      <td>{NCBIGene:2932, MESH:D006528, NCBIGene:7472, M...</td>\n",
       "      <td>{NCBIGene:2932, NCBIGene:7472, NCBIGene:8312, ...</td>\n",
       "      <td>27</td>\n",
       "      <td>288</td>\n",
       "      <td>1346</td>\n",
       "      <td>11</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>11</td>\n",
       "      <td>PMC6590912__nihms-1530776-f0002.jpg</td>\n",
       "      <td>1.519984e-07</td>\n",
       "      <td>0.018569</td>\n",
       "      <td>0.642857</td>\n",
       "      <td>27</td>\n",
       "      <td>1292</td>\n",
       "      <td>15</td>\n",
       "      <td>28829</td>\n",
       "      <td>{NCBIGene:5146, NCBIGene:5150, NCBIGene:5141, ...</td>\n",
       "      <td>{686_STAT1_type 2 diabetes mellitus_Metformin,...</td>\n",
       "      <td>26</td>\n",
       "      <td>{NCBIGene:5575, NCBIGene:5141, NCBIGene:5150, ...</td>\n",
       "      <td>{NCBIGene:5146, NCBIGene:5150, NCBIGene:5141, ...</td>\n",
       "      <td>26</td>\n",
       "      <td>287</td>\n",
       "      <td>1319</td>\n",
       "      <td>12</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>12</td>\n",
       "      <td>PMC3618249__1472-6947-13-S1-S4-3.jpg</td>\n",
       "      <td>1.309868e-07</td>\n",
       "      <td>0.008941</td>\n",
       "      <td>0.464286</td>\n",
       "      <td>13</td>\n",
       "      <td>1280</td>\n",
       "      <td>15</td>\n",
       "      <td>28855</td>\n",
       "      <td>{NCBIGene:3265, NCBIGene:8431, NCBIGene:2885, ...</td>\n",
       "      <td>{752_GHRL_type 2 diabetes mellitus_Metformin, ...</td>\n",
       "      <td>12</td>\n",
       "      <td>{NCBIGene:3036, NCBIGene:5571, NCBIGene:282974...</td>\n",
       "      <td>{NCBIGene:3265, NCBIGene:8431, NCBIGene:2885, ...</td>\n",
       "      <td>12</td>\n",
       "      <td>286</td>\n",
       "      <td>1293</td>\n",
       "      <td>13</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>13</td>\n",
       "      <td>PMC6917641__fonc-09-01404-g0001.jpg</td>\n",
       "      <td>1.265136e-07</td>\n",
       "      <td>0.007565</td>\n",
       "      <td>0.314286</td>\n",
       "      <td>11</td>\n",
       "      <td>1270</td>\n",
       "      <td>24</td>\n",
       "      <td>28858</td>\n",
       "      <td>{MESH:D008687, NCBIGene:10859, NCBIGene:5047, ...</td>\n",
       "      <td>{1085_PAEP_type 2 diabetes mellitus_Metformin,...</td>\n",
       "      <td>10</td>\n",
       "      <td>{MESH:D000596, MESH:C107135, NCBIGene:5047, ME...</td>\n",
       "      <td>{NCBIGene:10859, NCBIGene:5047, NCBIGene:3099,...</td>\n",
       "      <td>10</td>\n",
       "      <td>285</td>\n",
       "      <td>1281</td>\n",
       "      <td>14</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>14</td>\n",
       "      <td>PMC6348405__jcbn18-37f03.jpg</td>\n",
       "      <td>1.525789e-07</td>\n",
       "      <td>0.006190</td>\n",
       "      <td>0.750000</td>\n",
       "      <td>9</td>\n",
       "      <td>1262</td>\n",
       "      <td>3</td>\n",
       "      <td>28889</td>\n",
       "      <td>{NCBIGene:26291, NCBIGene:1352, MESH:D008687, ...</td>\n",
       "      <td>{750_FGF21_type 2 diabetes mellitus_Metformin,...</td>\n",
       "      <td>8</td>\n",
       "      <td>{NCBIGene:26291, NCBIGene:1352, MESH:D008687, ...</td>\n",
       "      <td>{NCBIGene:26291, NCBIGene:1352, NCBIGene:9927,...</td>\n",
       "      <td>8</td>\n",
       "      <td>284</td>\n",
       "      <td>1271</td>\n",
       "      <td>15</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    iteration                                   figure_id       p_value  \\\n",
       "0           0               PMC5709209__nihms897483f1.jpg  1.242422e-08   \n",
       "1           1  PMC4160009__RevDiabeticStud-10-228-001.jpg  6.147351e-08   \n",
       "2           2    PMC4484428__oncotarget-06-10908-g008.jpg  7.516325e-08   \n",
       "3           3        PMC6110909__fendo-09-00472-g0001.jpg  7.122699e-08   \n",
       "4           4           PMC3659509__MI2013-750540.001.jpg  8.970538e-08   \n",
       "5           5   PMC7076893__12906_2020_2871_Fig8_HTML.jpg  1.041298e-07   \n",
       "6           6         PMC3884529__neo1512_1379_fig006.jpg  9.734804e-08   \n",
       "7           7             PMC2936028__bph0160-1195-f8.jpg  1.076956e-07   \n",
       "8           8         PMC7069501__nihms-1022857-f0003.jpg  1.216332e-07   \n",
       "9           9               PMC4775314__nihms754130f3.jpg  1.298503e-07   \n",
       "10         10         PMC6721652__cells-08-00826-g003.jpg  1.536512e-07   \n",
       "11         11         PMC6590912__nihms-1530776-f0002.jpg  1.519984e-07   \n",
       "12         12        PMC3618249__1472-6947-13-S1-S4-3.jpg  1.309868e-07   \n",
       "13         13         PMC6917641__fonc-09-01404-g0001.jpg  1.265136e-07   \n",
       "14         14                PMC6348405__jcbn18-37f03.jpg  1.525789e-07   \n",
       "\n",
       "    fraction_of_trapi_curies_in_figure  \\\n",
       "0                             0.004127   \n",
       "1                             0.006878   \n",
       "2                             0.006878   \n",
       "3                             0.006878   \n",
       "4                             0.014443   \n",
       "5                             0.008253   \n",
       "6                             0.006190   \n",
       "7                             0.012380   \n",
       "8                             0.008941   \n",
       "9                             0.006190   \n",
       "10                            0.019257   \n",
       "11                            0.018569   \n",
       "12                            0.008941   \n",
       "13                            0.007565   \n",
       "14                            0.006190   \n",
       "\n",
       "    fraction_of_figure_curies_in_trapi_results  yes_trapi_results_yes_figure  \\\n",
       "0                                     1.000000                             6   \n",
       "1                                     1.000000                            10   \n",
       "2                                     0.526316                            10   \n",
       "3                                     0.526316                            10   \n",
       "4                                     0.840000                            21   \n",
       "5                                     0.600000                            12   \n",
       "6                                     0.450000                             9   \n",
       "7                                     0.692308                            18   \n",
       "8                                     0.393939                            13   \n",
       "9                                     0.428571                             9   \n",
       "10                                    0.571429                            28   \n",
       "11                                    0.642857                            27   \n",
       "12                                    0.464286                            13   \n",
       "13                                    0.314286                            11   \n",
       "14                                    0.750000                             9   \n",
       "\n",
       "    yes_trapi_results_no_figure  no_trapi_results_yes_figure  \\\n",
       "0                          1448                            0   \n",
       "1                          1439                            0   \n",
       "2                          1430                            9   \n",
       "3                          1421                            9   \n",
       "4                          1401                            4   \n",
       "5                          1390                            8   \n",
       "6                          1382                           11   \n",
       "7                          1365                            8   \n",
       "8                          1353                           20   \n",
       "9                          1345                           12   \n",
       "10                         1318                           21   \n",
       "11                         1292                           15   \n",
       "12                         1280                           15   \n",
       "13                         1270                           24   \n",
       "14                         1262                            3   \n",
       "\n",
       "    no_trapi_results_no_figure  \\\n",
       "0                        28709   \n",
       "1                        28714   \n",
       "2                        28714   \n",
       "3                        28723   \n",
       "4                        28737   \n",
       "5                        28753   \n",
       "6                        28761   \n",
       "7                        28772   \n",
       "8                        28777   \n",
       "9                        28797   \n",
       "10                       28796   \n",
       "11                       28829   \n",
       "12                       28855   \n",
       "13                       28858   \n",
       "14                       28889   \n",
       "\n",
       "                       figure_curies_in_trapi_results  \\\n",
       "0   {MESH:D008687, NCBIGene:3716, NCBIGene:51135, ...   \n",
       "1   {NCBIGene:5571, MESH:D008687, NCBIGene:200186,...   \n",
       "2   {MESH:D008687, NCBIGene:5228, NCBIGene:2277, N...   \n",
       "3   {NCBIGene:2475, MESH:D008687, NCBIGene:7249, N...   \n",
       "4   {NCBIGene:5575, NCBIGene:488, NCBIGene:5573, N...   \n",
       "5   {NCBIGene:5580, NCBIGene:3643, NCBIGene:3630, ...   \n",
       "6   {NCBIGene:5290, NCBIGene:5293, MESH:D008687, N...   \n",
       "7   {NCBIGene:5588, NCBIGene:5581, NCBIGene:5582, ...   \n",
       "8   {NCBIGene:7124, NCBIGene:5605, NCBIGene:64127,...   \n",
       "9   {NCBIGene:56731, NCBIGene:7046, NCBIGene:6774,...   \n",
       "10  {NCBIGene:2932, NCBIGene:7472, NCBIGene:51384,...   \n",
       "11  {NCBIGene:5146, NCBIGene:5150, NCBIGene:5141, ...   \n",
       "12  {NCBIGene:3265, NCBIGene:8431, NCBIGene:2885, ...   \n",
       "13  {MESH:D008687, NCBIGene:10859, NCBIGene:5047, ...   \n",
       "14  {NCBIGene:26291, NCBIGene:1352, MESH:D008687, ...   \n",
       "\n",
       "                                     trapi_result_set  trapi_result_count  \\\n",
       "0   {449_JAK2_type 2 diabetes mellitus_Metformin, ...                   5   \n",
       "1   {623_PRKAB1_type 2 diabetes mellitus_Metformin...                   9   \n",
       "2   {50_SIRT1_type 2 diabetes mellitus_Metformin, ...                   9   \n",
       "3   {902_CRTC1_type 2 diabetes mellitus_Metformin,...                   9   \n",
       "4   {1261_PRKACA_type 2 diabetes mellitus_Metformi...                  20   \n",
       "5   {515_MAPK1_type 2 diabetes mellitus_Metformin,...                  11   \n",
       "6   {503_PIK3CA_type 2 diabetes mellitus_Metformin...                   8   \n",
       "7   {438_MAPK9_type 2 diabetes mellitus_Metformin,...                  17   \n",
       "8   {960_MAP2K1_type 2 diabetes mellitus_Metformin...                  12   \n",
       "9   {801_PDGFRB_type 2 diabetes mellitus_Metformin...                   8   \n",
       "10  {1010_AXIN1_type 2 diabetes mellitus_Metformin...                  27   \n",
       "11  {686_STAT1_type 2 diabetes mellitus_Metformin,...                  26   \n",
       "12  {752_GHRL_type 2 diabetes mellitus_Metformin, ...                  12   \n",
       "13  {1085_PAEP_type 2 diabetes mellitus_Metformin,...                  10   \n",
       "14  {750_FGF21_type 2 diabetes mellitus_Metformin,...                   8   \n",
       "\n",
       "                                            curie_set  \\\n",
       "0   {MESH:D008687, NCBIGene:3716, NCBIGene:51135, ...   \n",
       "1   {NCBIGene:5571, MESH:D008687, NCBIGene:200186,...   \n",
       "2   {NCBIGene:5571, NCBIGene:2277, NCBIGene:5228, ...   \n",
       "3   {NCBIGene:5571, NCBIGene:7249, NCBIGene:51422,...   \n",
       "4   {NCBIGene:5575, NCBIGene:488, NCBIGene:5573, N...   \n",
       "5   {MESH:C043561, NCBIGene:3630, MESH:D006943, NC...   \n",
       "6   {NCBIGene:5571, NCBIGene:23533, NCBIGene:51422...   \n",
       "7   {NCBIGene:5581, NCBIGene:5582, MESH:C068811, N...   \n",
       "8   {NCBIGene:7124, NCBIGene:5605, NCBIGene:948, N...   \n",
       "9   {MESH:C448039, MESH:D011392, NCBIGene:7048, NC...   \n",
       "10  {NCBIGene:2932, MESH:D006528, NCBIGene:7472, M...   \n",
       "11  {NCBIGene:5575, NCBIGene:5141, NCBIGene:5150, ...   \n",
       "12  {NCBIGene:3036, NCBIGene:5571, NCBIGene:282974...   \n",
       "13  {MESH:D000596, MESH:C107135, NCBIGene:5047, ME...   \n",
       "14  {NCBIGene:26291, NCBIGene:1352, MESH:D008687, ...   \n",
       "\n",
       "                                    curies_to_exclude  len_curies_to_exclude  \\\n",
       "0   {NCBIGene:3716, NCBIGene:51135, NCBIGene:7297,...                      5   \n",
       "1   {NCBIGene:5571, NCBIGene:200186, NCBIGene:5562...                      9   \n",
       "2   {NCBIGene:5228, NCBIGene:2277, NCBIGene:3569, ...                      9   \n",
       "3   {NCBIGene:2475, NCBIGene:7249, NCBIGene:23373,...                      9   \n",
       "4   {NCBIGene:5575, NCBIGene:5465, NCBIGene:487, N...                     20   \n",
       "5   {NCBIGene:5580, NCBIGene:3643, NCBIGene:3630, ...                     11   \n",
       "6   {NCBIGene:5290, NCBIGene:23533, NCBIGene:14685...                      8   \n",
       "7   {NCBIGene:5588, NCBIGene:5581, NCBIGene:5582, ...                     17   \n",
       "8   {NCBIGene:7124, NCBIGene:5605, NCBIGene:64127,...                     12   \n",
       "9   {NCBIGene:56731, NCBIGene:7046, NCBIGene:6774,...                      8   \n",
       "10  {NCBIGene:2932, NCBIGene:7472, NCBIGene:8312, ...                     27   \n",
       "11  {NCBIGene:5146, NCBIGene:5150, NCBIGene:5141, ...                     26   \n",
       "12  {NCBIGene:3265, NCBIGene:8431, NCBIGene:2885, ...                     12   \n",
       "13  {NCBIGene:10859, NCBIGene:5047, NCBIGene:3099,...                     10   \n",
       "14  {NCBIGene:26291, NCBIGene:1352, NCBIGene:9927,...                      8   \n",
       "\n",
       "    remaining_figure_count  remaining_trapi_curie_count  \\\n",
       "0                      298                         1454   \n",
       "1                      297                         1449   \n",
       "2                      296                         1440   \n",
       "3                      295                         1431   \n",
       "4                      294                         1422   \n",
       "5                      293                         1402   \n",
       "6                      292                         1391   \n",
       "7                      291                         1383   \n",
       "8                      290                         1366   \n",
       "9                      289                         1354   \n",
       "10                     288                         1346   \n",
       "11                     287                         1319   \n",
       "12                     286                         1293   \n",
       "13                     285                         1281   \n",
       "14                     284                         1271   \n",
       "\n",
       "    pathway_figure_id_count  \n",
       "0                         1  \n",
       "1                         2  \n",
       "2                         3  \n",
       "3                         4  \n",
       "4                         5  \n",
       "5                         6  \n",
       "6                         7  \n",
       "7                         8  \n",
       "8                         9  \n",
       "9                        10  \n",
       "10                       11  \n",
       "11                       12  \n",
       "12                       13  \n",
       "13                       14  \n",
       "14                       15  "
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "display(pathway_figure_df)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Overview of Pathway Figure Results\n",
    "\n",
    "The figures and tables below summarize the intersections and groupings of your TRAPI results with published pathway figures from the PFOCR daatbase.\n",
    "\n",
    "### TRAPI results grouped by pathway figures\n",
    "\n",
    "Each pathway figure in order of significance with metadata and a table of the TRAPI results containing pathway-intersecting CURIEs (beyond the \"required\" CURIEs). Click 'o' to toggle between 3 output modes."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Total number of pathway figure results: 15\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<hr>"
      ],
      "text/plain": [
       "<IPython.core.display.HTML object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<img src=\"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5709209/bin/nihms897483f1.jpg\"/>"
      ],
      "text/plain": [
       "<IPython.core.display.Image object>"
      ]
     },
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    {
     "data": {
      "text/html": [
       "<h3>1.&nbsp;Anti-ageing mechanisms of metformin</h3>&nbsp;&nbsp;&nbsp;&nbsp; * <a href=\"https://pfocr.wikipathways.org/figures/PMC5709209__nihms897483f1.html\" target=\"_blank\">Link to pathway figure at PFOCR</a> * <a href=\"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5709209\" target=\"_blank\">Link to article at PMC</a>"
      ],
      "text/plain": [
       "<IPython.core.display.HTML object>"
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     },
     "metadata": {},
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    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Number of TRAPI results represented by this pathway: 5\n",
      "Number of CURIEs represented by this pathway: 6\n",
      "Query-specified CURIEs in this pathway: {'MESH:D008687'}\n"
     ]
    },
    {
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       "  <tbody>\n",
       "    <tr>\n",
       "      <th>332</th>\n",
       "      <td>79.25000</td>\n",
       "      <td>BTE</td>\n",
       "      <td>0.605989</td>\n",
       "      <td>JAK1</td>\n",
       "      <td>type 2 diabetes mellitus</td>\n",
       "      <td>Metformin</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>449</th>\n",
       "      <td>71.93750</td>\n",
       "      <td>BTE</td>\n",
       "      <td>0.556972</td>\n",
       "      <td>JAK2</td>\n",
       "      <td>type 2 diabetes mellitus</td>\n",
       "      <td>Metformin</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>455</th>\n",
       "      <td>71.56250</td>\n",
       "      <td>BTE</td>\n",
       "      <td>0.555859</td>\n",
       "      <td>TYK2</td>\n",
       "      <td>type 2 diabetes mellitus</td>\n",
       "      <td>Metformin</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>488</th>\n",
       "      <td>69.50000</td>\n",
       "      <td>BTE</td>\n",
       "      <td>0.540519</td>\n",
       "      <td>IRAK4</td>\n",
       "      <td>type 2 diabetes mellitus</td>\n",
       "      <td>Metformin</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1293</th>\n",
       "      <td>10.96875</td>\n",
       "      <td>BTE</td>\n",
       "      <td>0.090659</td>\n",
       "      <td>JAK3</td>\n",
       "      <td>type 2 diabetes mellitus</td>\n",
       "      <td>Metformin</td>\n",
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      ],
      "text/plain": [
       "      normalized_score resource_id     score node_name.n0  \\\n",
       "332           79.25000         BTE  0.605989         JAK1   \n",
       "449           71.93750         BTE  0.556972         JAK2   \n",
       "455           71.56250         BTE  0.555859         TYK2   \n",
       "488           69.50000         BTE  0.540519        IRAK4   \n",
       "1293          10.96875         BTE  0.090659         JAK3   \n",
       "\n",
       "                  node_name.n1 node_name.n2  \n",
       "332   type 2 diabetes mellitus    Metformin  \n",
       "449   type 2 diabetes mellitus    Metformin  \n",
       "455   type 2 diabetes mellitus    Metformin  \n",
       "488   type 2 diabetes mellitus    Metformin  \n",
       "1293  type 2 diabetes mellitus    Metformin  "
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    {
     "data": {
      "text/html": [
       "<h3>2.&nbsp;Metformin and Cancer</h3>&nbsp;&nbsp;&nbsp;&nbsp; * <a href=\"https://pfocr.wikipathways.org/figures/PMC4160009__RevDiabeticStud-10-228-001.html\" target=\"_blank\">Link to pathway figure at PFOCR</a> * <a href=\"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4160009\" target=\"_blank\">Link to article at PMC</a>"
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    {
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     "output_type": "stream",
     "text": [
      "Number of TRAPI results represented by this pathway: 9\n",
      "Number of CURIEs represented by this pathway: 10\n",
      "Query-specified CURIEs in this pathway: {'MESH:D008687'}\n"
     ]
    },
    {
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       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>99.93750</td>\n",
       "      <td>BTE</td>\n",
       "      <td>0.976018</td>\n",
       "      <td>STK11</td>\n",
       "      <td>type 2 diabetes mellitus</td>\n",
       "      <td>Metformin</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>174</th>\n",
       "      <td>89.12500</td>\n",
       "      <td>BTE</td>\n",
       "      <td>0.751683</td>\n",
       "      <td>PRKAA1</td>\n",
       "      <td>type 2 diabetes mellitus</td>\n",
       "      <td>Metformin</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>239</th>\n",
       "      <td>85.06250</td>\n",
       "      <td>BTE</td>\n",
       "      <td>0.691569</td>\n",
       "      <td>PRKAG1</td>\n",
       "      <td>type 2 diabetes mellitus</td>\n",
       "      <td>Metformin</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>608</th>\n",
       "      <td>61.96875</td>\n",
       "      <td>BTE</td>\n",
       "      <td>0.244919</td>\n",
       "      <td>PRKAA2</td>\n",
       "      <td>type 2 diabetes mellitus</td>\n",
       "      <td>Metformin</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>621</th>\n",
       "      <td>60.71875</td>\n",
       "      <td>BTE</td>\n",
       "      <td>0.201740</td>\n",
       "      <td>PRKAG2</td>\n",
       "      <td>type 2 diabetes mellitus</td>\n",
       "      <td>Metformin</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>622</th>\n",
       "      <td>60.71875</td>\n",
       "      <td>BTE</td>\n",
       "      <td>0.201740</td>\n",
       "      <td>PRKAB2</td>\n",
       "      <td>type 2 diabetes mellitus</td>\n",
       "      <td>Metformin</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>623</th>\n",
       "      <td>60.71875</td>\n",
       "      <td>BTE</td>\n",
       "      <td>0.201740</td>\n",
       "      <td>PRKAB1</td>\n",
       "      <td>type 2 diabetes mellitus</td>\n",
       "      <td>Metformin</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>671</th>\n",
       "      <td>58.90625</td>\n",
       "      <td>BTE</td>\n",
       "      <td>0.179841</td>\n",
       "      <td>CRTC2</td>\n",
       "      <td>type 2 diabetes mellitus</td>\n",
       "      <td>Metformin</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>788</th>\n",
       "      <td>45.84375</td>\n",
       "      <td>BTE</td>\n",
       "      <td>0.135525</td>\n",
       "      <td>PRKAG3</td>\n",
       "      <td>type 2 diabetes mellitus</td>\n",
       "      <td>Metformin</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "     normalized_score resource_id     score node_name.n0  \\\n",
       "1            99.93750         BTE  0.976018        STK11   \n",
       "174          89.12500         BTE  0.751683       PRKAA1   \n",
       "239          85.06250         BTE  0.691569       PRKAG1   \n",
       "608          61.96875         BTE  0.244919       PRKAA2   \n",
       "621          60.71875         BTE  0.201740       PRKAG2   \n",
       "622          60.71875         BTE  0.201740       PRKAB2   \n",
       "623          60.71875         BTE  0.201740       PRKAB1   \n",
       "671          58.90625         BTE  0.179841        CRTC2   \n",
       "788          45.84375         BTE  0.135525       PRKAG3   \n",
       "\n",
       "                 node_name.n1 node_name.n2  \n",
       "1    type 2 diabetes mellitus    Metformin  \n",
       "174  type 2 diabetes mellitus    Metformin  \n",
       "239  type 2 diabetes mellitus    Metformin  \n",
       "608  type 2 diabetes mellitus    Metformin  \n",
       "621  type 2 diabetes mellitus    Metformin  \n",
       "622  type 2 diabetes mellitus    Metformin  \n",
       "623  type 2 diabetes mellitus    Metformin  \n",
       "671  type 2 diabetes mellitus    Metformin  \n",
       "788  type 2 diabetes mellitus    Metformin  "
      ]
     },
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     "data": {
      "text/html": [
       "<h3>3.&nbsp;Metformin prevents aggressive ovarian cancer growth driven by high-energy diet: similarity with calorie restriction</h3>&nbsp;&nbsp;&nbsp;&nbsp; * <a href=\"https://pfocr.wikipathways.org/figures/PMC4484428__oncotarget-06-10908-g008.html\" target=\"_blank\">Link to pathway figure at PFOCR</a> * <a href=\"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4484428\" target=\"_blank\">Link to article at PMC</a>"
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     "output_type": "stream",
     "text": [
      "Number of TRAPI results represented by this pathway: 9\n",
      "Number of CURIEs represented by this pathway: 10\n",
      "Query-specified CURIEs in this pathway: {'MESH:D008687'}\n"
     ]
    },
    {
     "data": {
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       "  <tbody>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>99.81250</td>\n",
       "      <td>BTE</td>\n",
       "      <td>0.967913</td>\n",
       "      <td>CCL2</td>\n",
       "      <td>type 2 diabetes mellitus</td>\n",
       "      <td>Metformin</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>29</th>\n",
       "      <td>98.18750</td>\n",
       "      <td>BTE</td>\n",
       "      <td>0.905940</td>\n",
       "      <td>VEGFA</td>\n",
       "      <td>type 2 diabetes mellitus</td>\n",
       "      <td>Metformin</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50</th>\n",
       "      <td>96.87500</td>\n",
       "      <td>BTE</td>\n",
       "      <td>0.869876</td>\n",
       "      <td>SIRT1</td>\n",
       "      <td>type 2 diabetes mellitus</td>\n",
       "      <td>Metformin</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>133</th>\n",
       "      <td>91.68750</td>\n",
       "      <td>BTE</td>\n",
       "      <td>0.781224</td>\n",
       "      <td>PGF</td>\n",
       "      <td>type 2 diabetes mellitus</td>\n",
       "      <td>Metformin</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>305</th>\n",
       "      <td>80.93750</td>\n",
       "      <td>BTE</td>\n",
       "      <td>0.620209</td>\n",
       "      <td>IL6</td>\n",
       "      <td>type 2 diabetes mellitus</td>\n",
       "      <td>Metformin</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>607</th>\n",
       "      <td>61.96875</td>\n",
       "      <td>BTE</td>\n",
       "      <td>0.244919</td>\n",
       "      <td>IGF1</td>\n",
       "      <td>type 2 diabetes mellitus</td>\n",
       "      <td>Metformin</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>642</th>\n",
       "      <td>58.90625</td>\n",
       "      <td>BTE</td>\n",
       "      <td>0.179841</td>\n",
       "      <td>VEGFB</td>\n",
       "      <td>type 2 diabetes mellitus</td>\n",
       "      <td>Metformin</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>680</th>\n",
       "      <td>54.65625</td>\n",
       "      <td>BTE</td>\n",
       "      <td>0.157762</td>\n",
       "      <td>VEGFC</td>\n",
       "      <td>type 2 diabetes mellitus</td>\n",
       "      <td>Metformin</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>681</th>\n",
       "      <td>54.65625</td>\n",
       "      <td>BTE</td>\n",
       "      <td>0.157762</td>\n",
       "      <td>VEGFD</td>\n",
       "      <td>type 2 diabetes mellitus</td>\n",
       "      <td>Metformin</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "     normalized_score resource_id     score node_name.n0  \\\n",
       "3            99.81250         BTE  0.967913         CCL2   \n",
       "29           98.18750         BTE  0.905940        VEGFA   \n",
       "50           96.87500         BTE  0.869876        SIRT1   \n",
       "133          91.68750         BTE  0.781224          PGF   \n",
       "305          80.93750         BTE  0.620209          IL6   \n",
       "607          61.96875         BTE  0.244919         IGF1   \n",
       "642          58.90625         BTE  0.179841        VEGFB   \n",
       "680          54.65625         BTE  0.157762        VEGFC   \n",
       "681          54.65625         BTE  0.157762        VEGFD   \n",
       "\n",
       "                 node_name.n1 node_name.n2  \n",
       "3    type 2 diabetes mellitus    Metformin  \n",
       "29   type 2 diabetes mellitus    Metformin  \n",
       "50   type 2 diabetes mellitus    Metformin  \n",
       "133  type 2 diabetes mellitus    Metformin  \n",
       "305  type 2 diabetes mellitus    Metformin  \n",
       "607  type 2 diabetes mellitus    Metformin  \n",
       "642  type 2 diabetes mellitus    Metformin  \n",
       "680  type 2 diabetes mellitus    Metformin  \n",
       "681  type 2 diabetes mellitus    Metformin  "
      ]
     },
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    {
     "data": {
      "text/html": [
       "<h3>4.&nbsp;Direct and indirect mechanisms of action of metformin in cancer cells</h3>&nbsp;&nbsp;&nbsp;&nbsp; * <a href=\"https://pfocr.wikipathways.org/figures/PMC6110909__fendo-09-00472-g0001.html\" target=\"_blank\">Link to pathway figure at PFOCR</a> * <a href=\"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6110909\" target=\"_blank\">Link to article at PMC</a>"
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     "output_type": "stream",
     "text": [
      "Number of TRAPI results represented by this pathway: 9\n",
      "Number of CURIEs represented by this pathway: 10\n",
      "Query-specified CURIEs in this pathway: {'MESH:D008687'}\n"
     ]
    },
    {
     "data": {
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       "  <tbody>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>99.43750</td>\n",
       "      <td>BTE</td>\n",
       "      <td>0.947447</td>\n",
       "      <td>MTOR</td>\n",
       "      <td>type 2 diabetes mellitus</td>\n",
       "      <td>Metformin</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>99.06250</td>\n",
       "      <td>BTE</td>\n",
       "      <td>0.931805</td>\n",
       "      <td>SLC22A1</td>\n",
       "      <td>type 2 diabetes mellitus</td>\n",
       "      <td>Metformin</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>111</th>\n",
       "      <td>93.06250</td>\n",
       "      <td>BTE</td>\n",
       "      <td>0.802013</td>\n",
       "      <td>TP53</td>\n",
       "      <td>type 2 diabetes mellitus</td>\n",
       "      <td>Metformin</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>319</th>\n",
       "      <td>80.06250</td>\n",
       "      <td>BTE</td>\n",
       "      <td>0.613373</td>\n",
       "      <td>TSC2</td>\n",
       "      <td>type 2 diabetes mellitus</td>\n",
       "      <td>Metformin</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>354</th>\n",
       "      <td>77.87500</td>\n",
       "      <td>BTE</td>\n",
       "      <td>0.599380</td>\n",
       "      <td>DDIT4</td>\n",
       "      <td>type 2 diabetes mellitus</td>\n",
       "      <td>Metformin</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>495</th>\n",
       "      <td>69.06250</td>\n",
       "      <td>BTE</td>\n",
       "      <td>0.539257</td>\n",
       "      <td>TSC1</td>\n",
       "      <td>type 2 diabetes mellitus</td>\n",
       "      <td>Metformin</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>792</th>\n",
       "      <td>45.84375</td>\n",
       "      <td>BTE</td>\n",
       "      <td>0.135525</td>\n",
       "      <td>RPTOR</td>\n",
       "      <td>type 2 diabetes mellitus</td>\n",
       "      <td>Metformin</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>902</th>\n",
       "      <td>45.84375</td>\n",
       "      <td>BTE</td>\n",
       "      <td>0.135525</td>\n",
       "      <td>CRTC1</td>\n",
       "      <td>type 2 diabetes mellitus</td>\n",
       "      <td>Metformin</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1308</th>\n",
       "      <td>10.96875</td>\n",
       "      <td>BTE</td>\n",
       "      <td>0.090659</td>\n",
       "      <td>RHEB</td>\n",
       "      <td>type 2 diabetes mellitus</td>\n",
       "      <td>Metformin</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "      normalized_score resource_id     score node_name.n0  \\\n",
       "9             99.43750         BTE  0.947447         MTOR   \n",
       "15            99.06250         BTE  0.931805      SLC22A1   \n",
       "111           93.06250         BTE  0.802013         TP53   \n",
       "319           80.06250         BTE  0.613373         TSC2   \n",
       "354           77.87500         BTE  0.599380        DDIT4   \n",
       "495           69.06250         BTE  0.539257         TSC1   \n",
       "792           45.84375         BTE  0.135525        RPTOR   \n",
       "902           45.84375         BTE  0.135525        CRTC1   \n",
       "1308          10.96875         BTE  0.090659         RHEB   \n",
       "\n",
       "                  node_name.n1 node_name.n2  \n",
       "9     type 2 diabetes mellitus    Metformin  \n",
       "15    type 2 diabetes mellitus    Metformin  \n",
       "111   type 2 diabetes mellitus    Metformin  \n",
       "319   type 2 diabetes mellitus    Metformin  \n",
       "354   type 2 diabetes mellitus    Metformin  \n",
       "495   type 2 diabetes mellitus    Metformin  \n",
       "792   type 2 diabetes mellitus    Metformin  \n",
       "902   type 2 diabetes mellitus    Metformin  \n",
       "1308  type 2 diabetes mellitus    Metformin  "
      ]
     },
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      "text/html": [
       "<h3>5.&nbsp;Intracellular effects of soluble mediators are mainly mediated through the interaction with specific receptors that link extracellular signals up to modulation of gene expression</h3>&nbsp;&nbsp;&nbsp;&nbsp; * <a href=\"https://pfocr.wikipathways.org/figures/PMC3659509__MI2013-750540.001.html\" target=\"_blank\">Link to pathway figure at PFOCR</a> * <a href=\"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC3659509\" target=\"_blank\">Link to article at PMC</a>"
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    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Number of TRAPI results represented by this pathway: 20\n",
      "Number of CURIEs represented by this pathway: 21\n",
      "Query-specified CURIEs in this pathway: {'MESH:D008687'}\n"
     ]
    },
    {
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>normalized_score</th>\n",
       "      <th>resource_id</th>\n",
       "      <th>score</th>\n",
       "      <th>node_name.n0</th>\n",
       "      <th>node_name.n1</th>\n",
       "      <th>node_name.n2</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>43</th>\n",
       "      <td>97.31250</td>\n",
       "      <td>BTE</td>\n",
       "      <td>0.881961</td>\n",
       "      <td>EGFR</td>\n",
       "      <td>type 2 diabetes mellitus</td>\n",
       "      <td>Metformin</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>69</th>\n",
       "      <td>95.68750</td>\n",
       "      <td>BTE</td>\n",
       "      <td>0.841712</td>\n",
       "      <td>FOXO1</td>\n",
       "      <td>type 2 diabetes mellitus</td>\n",
       "      <td>Metformin</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>221</th>\n",
       "      <td>86.18750</td>\n",
       "      <td>BTE</td>\n",
       "      <td>0.710895</td>\n",
       "      <td>Glutathione</td>\n",
       "      <td>type 2 diabetes mellitus</td>\n",
       "      <td>Metformin</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>249</th>\n",
       "      <td>84.43750</td>\n",
       "      <td>BTE</td>\n",
       "      <td>0.684047</td>\n",
       "      <td>PPARA</td>\n",
       "      <td>type 2 diabetes mellitus</td>\n",
       "      <td>Metformin</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>266</th>\n",
       "      <td>83.37500</td>\n",
       "      <td>BTE</td>\n",
       "      <td>0.667797</td>\n",
       "      <td>AKT2</td>\n",
       "      <td>type 2 diabetes mellitus</td>\n",
       "      <td>Metformin</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>368</th>\n",
       "      <td>77.00000</td>\n",
       "      <td>BTE</td>\n",
       "      <td>0.594584</td>\n",
       "      <td>GLP1R</td>\n",
       "      <td>type 2 diabetes mellitus</td>\n",
       "      <td>Metformin</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>401</th>\n",
       "      <td>74.93750</td>\n",
       "      <td>BTE</td>\n",
       "      <td>0.578090</td>\n",
       "      <td>AKT1</td>\n",
       "      <td>type 2 diabetes mellitus</td>\n",
       "      <td>Metformin</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>604</th>\n",
       "      <td>62.25000</td>\n",
       "      <td>BTE</td>\n",
       "      <td>0.287147</td>\n",
       "      <td>PDX1</td>\n",
       "      <td>type 2 diabetes mellitus</td>\n",
       "      <td>Metformin</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>618</th>\n",
       "      <td>61.59375</td>\n",
       "      <td>BTE</td>\n",
       "      <td>0.223439</td>\n",
       "      <td>ATP2A2</td>\n",
       "      <td>type 2 diabetes mellitus</td>\n",
       "      <td>Metformin</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>658</th>\n",
       "      <td>58.90625</td>\n",
       "      <td>BTE</td>\n",
       "      <td>0.179841</td>\n",
       "      <td>ATP2A3</td>\n",
       "      <td>type 2 diabetes mellitus</td>\n",
       "      <td>Metformin</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>691</th>\n",
       "      <td>54.65625</td>\n",
       "      <td>BTE</td>\n",
       "      <td>0.157762</td>\n",
       "      <td>AKT3</td>\n",
       "      <td>type 2 diabetes mellitus</td>\n",
       "      <td>Metformin</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>779</th>\n",
       "      <td>45.84375</td>\n",
       "      <td>BTE</td>\n",
       "      <td>0.135525</td>\n",
       "      <td>MAFA</td>\n",
       "      <td>type 2 diabetes mellitus</td>\n",
       "      <td>Metformin</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>961</th>\n",
       "      <td>31.03125</td>\n",
       "      <td>BTE</td>\n",
       "      <td>0.113150</td>\n",
       "      <td>PRKAR1A</td>\n",
       "      <td>type 2 diabetes mellitus</td>\n",
       "      <td>Metformin</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1201</th>\n",
       "      <td>31.03125</td>\n",
       "      <td>BTE</td>\n",
       "      <td>0.113150</td>\n",
       "      <td>ATP2A1</td>\n",
       "      <td>type 2 diabetes mellitus</td>\n",
       "      <td>Metformin</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1256</th>\n",
       "      <td>10.96875</td>\n",
       "      <td>BTE</td>\n",
       "      <td>0.090659</td>\n",
       "      <td>PRKAR1B</td>\n",
       "      <td>type 2 diabetes mellitus</td>\n",
       "      <td>Metformin</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1261</th>\n",
       "      <td>10.96875</td>\n",
       "      <td>BTE</td>\n",
       "      <td>0.090659</td>\n",
       "      <td>PRKACA</td>\n",
       "      <td>type 2 diabetes mellitus</td>\n",
       "      <td>Metformin</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1267</th>\n",
       "      <td>10.96875</td>\n",
       "      <td>BTE</td>\n",
       "      <td>0.090659</td>\n",
       "      <td>PRKAR2A</td>\n",
       "      <td>type 2 diabetes mellitus</td>\n",
       "      <td>Metformin</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1268</th>\n",
       "      <td>10.96875</td>\n",
       "      <td>BTE</td>\n",
       "      <td>0.090659</td>\n",
       "      <td>PRKAR2B</td>\n",
       "      <td>type 2 diabetes mellitus</td>\n",
       "      <td>Metformin</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1271</th>\n",
       "      <td>10.96875</td>\n",
       "      <td>BTE</td>\n",
       "      <td>0.090659</td>\n",
       "      <td>PRKACG</td>\n",
       "      <td>type 2 diabetes mellitus</td>\n",
       "      <td>Metformin</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1276</th>\n",
       "      <td>10.96875</td>\n",
       "      <td>BTE</td>\n",
       "      <td>0.090659</td>\n",
       "      <td>PRKACB</td>\n",
       "      <td>type 2 diabetes mellitus</td>\n",
       "      <td>Metformin</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "      normalized_score resource_id     score node_name.n0  \\\n",
       "43            97.31250         BTE  0.881961         EGFR   \n",
       "69            95.68750         BTE  0.841712        FOXO1   \n",
       "221           86.18750         BTE  0.710895  Glutathione   \n",
       "249           84.43750         BTE  0.684047        PPARA   \n",
       "266           83.37500         BTE  0.667797         AKT2   \n",
       "368           77.00000         BTE  0.594584        GLP1R   \n",
       "401           74.93750         BTE  0.578090         AKT1   \n",
       "604           62.25000         BTE  0.287147         PDX1   \n",
       "618           61.59375         BTE  0.223439       ATP2A2   \n",
       "658           58.90625         BTE  0.179841       ATP2A3   \n",
       "691           54.65625         BTE  0.157762         AKT3   \n",
       "779           45.84375         BTE  0.135525         MAFA   \n",
       "961           31.03125         BTE  0.113150      PRKAR1A   \n",
       "1201          31.03125         BTE  0.113150       ATP2A1   \n",
       "1256          10.96875         BTE  0.090659      PRKAR1B   \n",
       "1261          10.96875         BTE  0.090659       PRKACA   \n",
       "1267          10.96875         BTE  0.090659      PRKAR2A   \n",
       "1268          10.96875         BTE  0.090659      PRKAR2B   \n",
       "1271          10.96875         BTE  0.090659       PRKACG   \n",
       "1276          10.96875         BTE  0.090659       PRKACB   \n",
       "\n",
       "                  node_name.n1 node_name.n2  \n",
       "43    type 2 diabetes mellitus    Metformin  \n",
       "69    type 2 diabetes mellitus    Metformin  \n",
       "221   type 2 diabetes mellitus    Metformin  \n",
       "249   type 2 diabetes mellitus    Metformin  \n",
       "266   type 2 diabetes mellitus    Metformin  \n",
       "368   type 2 diabetes mellitus    Metformin  \n",
       "401   type 2 diabetes mellitus    Metformin  \n",
       "604   type 2 diabetes mellitus    Metformin  \n",
       "618   type 2 diabetes mellitus    Metformin  \n",
       "658   type 2 diabetes mellitus    Metformin  \n",
       "691   type 2 diabetes mellitus    Metformin  \n",
       "779   type 2 diabetes mellitus    Metformin  \n",
       "961   type 2 diabetes mellitus    Metformin  \n",
       "1201  type 2 diabetes mellitus    Metformin  \n",
       "1256  type 2 diabetes mellitus    Metformin  \n",
       "1261  type 2 diabetes mellitus    Metformin  \n",
       "1267  type 2 diabetes mellitus    Metformin  \n",
       "1268  type 2 diabetes mellitus    Metformin  \n",
       "1271  type 2 diabetes mellitus    Metformin  \n",
       "1276  type 2 diabetes mellitus    Metformin  "
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     "data": {
      "text/html": [
       "<h3>6.&nbsp;Critical pathways of the potential Alpha-glucosidase inhibitors from RS</h3>&nbsp;&nbsp;&nbsp;&nbsp; * <a href=\"https://pfocr.wikipathways.org/figures/PMC7076893__12906_2020_2871_Fig8_HTML.html\" target=\"_blank\">Link to pathway figure at PFOCR</a> * <a href=\"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7076893\" target=\"_blank\">Link to article at PMC</a>"
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    {
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     "output_type": "stream",
     "text": [
      "Number of TRAPI results represented by this pathway: 11\n",
      "Number of CURIEs represented by this pathway: 12\n",
      "Query-specified CURIEs in this pathway: {'MESH:D003924'}\n"
     ]
    },
    {
     "data": {
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       "  <thead>\n",
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       "      <th></th>\n",
       "      <th>normalized_score</th>\n",
       "      <th>resource_id</th>\n",
       "      <th>score</th>\n",
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       "  <tbody>\n",
       "    <tr>\n",
       "      <th>41</th>\n",
       "      <td>97.43750</td>\n",
       "      <td>BTE</td>\n",
       "      <td>0.886040</td>\n",
       "      <td>SLC2A4</td>\n",
       "      <td>type 2 diabetes mellitus</td>\n",
       "      <td>Metformin</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>44</th>\n",
       "      <td>97.25000</td>\n",
       "      <td>BTE</td>\n",
       "      <td>0.881111</td>\n",
       "      <td>INS</td>\n",
       "      <td>type 2 diabetes mellitus</td>\n",
       "      <td>Metformin</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>53</th>\n",
       "      <td>96.68750</td>\n",
       "      <td>BTE</td>\n",
       "      <td>0.865481</td>\n",
       "      <td>INSR</td>\n",
       "      <td>type 2 diabetes mellitus</td>\n",
       "      <td>Metformin</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>158</th>\n",
       "      <td>90.12500</td>\n",
       "      <td>BTE</td>\n",
       "      <td>0.758083</td>\n",
       "      <td>PPARG</td>\n",
       "      <td>type 2 diabetes mellitus</td>\n",
       "      <td>Metformin</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>371</th>\n",
       "      <td>76.81250</td>\n",
       "      <td>BTE</td>\n",
       "      <td>0.593991</td>\n",
       "      <td>PDPK1</td>\n",
       "      <td>type 2 diabetes mellitus</td>\n",
       "      <td>Metformin</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>400</th>\n",
       "      <td>75.00000</td>\n",
       "      <td>BTE</td>\n",
       "      <td>0.578141</td>\n",
       "      <td>MAPK3</td>\n",
       "      <td>type 2 diabetes mellitus</td>\n",
       "      <td>Metformin</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>515</th>\n",
       "      <td>67.81250</td>\n",
       "      <td>BTE</td>\n",
       "      <td>0.527567</td>\n",
       "      <td>MAPK1</td>\n",
       "      <td>type 2 diabetes mellitus</td>\n",
       "      <td>Metformin</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>619</th>\n",
       "      <td>60.71875</td>\n",
       "      <td>BTE</td>\n",
       "      <td>0.201740</td>\n",
       "      <td>PPARD</td>\n",
       "      <td>type 2 diabetes mellitus</td>\n",
       "      <td>Metformin</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>783</th>\n",
       "      <td>45.84375</td>\n",
       "      <td>BTE</td>\n",
       "      <td>0.135525</td>\n",
       "      <td>MAK</td>\n",
       "      <td>type 2 diabetes mellitus</td>\n",
       "      <td>Metformin</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>958</th>\n",
       "      <td>31.03125</td>\n",
       "      <td>BTE</td>\n",
       "      <td>0.113150</td>\n",
       "      <td>PRKCD</td>\n",
       "      <td>type 2 diabetes mellitus</td>\n",
       "      <td>Metformin</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>973</th>\n",
       "      <td>31.03125</td>\n",
       "      <td>BTE</td>\n",
       "      <td>0.113150</td>\n",
       "      <td>RXRA</td>\n",
       "      <td>type 2 diabetes mellitus</td>\n",
       "      <td>Metformin</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "     normalized_score resource_id     score node_name.n0  \\\n",
       "41           97.43750         BTE  0.886040       SLC2A4   \n",
       "44           97.25000         BTE  0.881111          INS   \n",
       "53           96.68750         BTE  0.865481         INSR   \n",
       "158          90.12500         BTE  0.758083        PPARG   \n",
       "371          76.81250         BTE  0.593991        PDPK1   \n",
       "400          75.00000         BTE  0.578141        MAPK3   \n",
       "515          67.81250         BTE  0.527567        MAPK1   \n",
       "619          60.71875         BTE  0.201740        PPARD   \n",
       "783          45.84375         BTE  0.135525          MAK   \n",
       "958          31.03125         BTE  0.113150        PRKCD   \n",
       "973          31.03125         BTE  0.113150         RXRA   \n",
       "\n",
       "                 node_name.n1 node_name.n2  \n",
       "41   type 2 diabetes mellitus    Metformin  \n",
       "44   type 2 diabetes mellitus    Metformin  \n",
       "53   type 2 diabetes mellitus    Metformin  \n",
       "158  type 2 diabetes mellitus    Metformin  \n",
       "371  type 2 diabetes mellitus    Metformin  \n",
       "400  type 2 diabetes mellitus    Metformin  \n",
       "515  type 2 diabetes mellitus    Metformin  \n",
       "619  type 2 diabetes mellitus    Metformin  \n",
       "783  type 2 diabetes mellitus    Metformin  \n",
       "958  type 2 diabetes mellitus    Metformin  \n",
       "973  type 2 diabetes mellitus    Metformin  "
      ]
     },
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     "output_type": "display_data"
    },
    {
     "data": {
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    },
    {
     "data": {
      "text/html": [
       "<img src=\"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC3884529/bin/neo1512_1379_fig006.jpg\"/>"
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    },
    {
     "data": {
      "text/html": [
       "<h3>7.&nbsp;Proposed schematic</h3>&nbsp;&nbsp;&nbsp;&nbsp; * <a href=\"https://pfocr.wikipathways.org/figures/PMC3884529__neo1512_1379_fig006.html\" target=\"_blank\">Link to pathway figure at PFOCR</a> * <a href=\"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC3884529\" target=\"_blank\">Link to article at PMC</a>"
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     "metadata": {},
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    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Number of TRAPI results represented by this pathway: 8\n",
      "Number of CURIEs represented by this pathway: 9\n",
      "Query-specified CURIEs in this pathway: {'MESH:D008687'}\n"
     ]
    },
    {
     "data": {
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>normalized_score</th>\n",
       "      <th>resource_id</th>\n",
       "      <th>score</th>\n",
       "      <th>node_name.n0</th>\n",
       "      <th>node_name.n1</th>\n",
       "      <th>node_name.n2</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>169</th>\n",
       "      <td>89.43750</td>\n",
       "      <td>BTE</td>\n",
       "      <td>0.755432</td>\n",
       "      <td>PIK3CB</td>\n",
       "      <td>type 2 diabetes mellitus</td>\n",
       "      <td>Metformin</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>300</th>\n",
       "      <td>81.25000</td>\n",
       "      <td>BTE</td>\n",
       "      <td>0.623737</td>\n",
       "      <td>PIK3CG</td>\n",
       "      <td>type 2 diabetes mellitus</td>\n",
       "      <td>Metformin</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>503</th>\n",
       "      <td>68.56250</td>\n",
       "      <td>BTE</td>\n",
       "      <td>0.536398</td>\n",
       "      <td>PIK3CA</td>\n",
       "      <td>type 2 diabetes mellitus</td>\n",
       "      <td>Metformin</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>969</th>\n",
       "      <td>31.03125</td>\n",
       "      <td>BTE</td>\n",
       "      <td>0.113150</td>\n",
       "      <td>PIK3CD</td>\n",
       "      <td>type 2 diabetes mellitus</td>\n",
       "      <td>Metformin</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1258</th>\n",
       "      <td>10.96875</td>\n",
       "      <td>BTE</td>\n",
       "      <td>0.090659</td>\n",
       "      <td>PIK3R5</td>\n",
       "      <td>type 2 diabetes mellitus</td>\n",
       "      <td>Metformin</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1259</th>\n",
       "      <td>10.96875</td>\n",
       "      <td>BTE</td>\n",
       "      <td>0.090659</td>\n",
       "      <td>PIK3R3</td>\n",
       "      <td>type 2 diabetes mellitus</td>\n",
       "      <td>Metformin</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1263</th>\n",
       "      <td>10.96875</td>\n",
       "      <td>BTE</td>\n",
       "      <td>0.090659</td>\n",
       "      <td>PIK3R6</td>\n",
       "      <td>type 2 diabetes mellitus</td>\n",
       "      <td>Metformin</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1275</th>\n",
       "      <td>10.96875</td>\n",
       "      <td>BTE</td>\n",
       "      <td>0.090659</td>\n",
       "      <td>PIK3R4</td>\n",
       "      <td>type 2 diabetes mellitus</td>\n",
       "      <td>Metformin</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "      normalized_score resource_id     score node_name.n0  \\\n",
       "169           89.43750         BTE  0.755432       PIK3CB   \n",
       "300           81.25000         BTE  0.623737       PIK3CG   \n",
       "503           68.56250         BTE  0.536398       PIK3CA   \n",
       "969           31.03125         BTE  0.113150       PIK3CD   \n",
       "1258          10.96875         BTE  0.090659       PIK3R5   \n",
       "1259          10.96875         BTE  0.090659       PIK3R3   \n",
       "1263          10.96875         BTE  0.090659       PIK3R6   \n",
       "1275          10.96875         BTE  0.090659       PIK3R4   \n",
       "\n",
       "                  node_name.n1 node_name.n2  \n",
       "169   type 2 diabetes mellitus    Metformin  \n",
       "300   type 2 diabetes mellitus    Metformin  \n",
       "503   type 2 diabetes mellitus    Metformin  \n",
       "969   type 2 diabetes mellitus    Metformin  \n",
       "1258  type 2 diabetes mellitus    Metformin  \n",
       "1259  type 2 diabetes mellitus    Metformin  \n",
       "1263  type 2 diabetes mellitus    Metformin  \n",
       "1275  type 2 diabetes mellitus    Metformin  "
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
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    {
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    {
     "data": {
      "text/html": [
       "<h3>8.&nbsp;The inhibitory effect of metformin in PMA-induced migration and invasion</h3>&nbsp;&nbsp;&nbsp;&nbsp; * <a href=\"https://pfocr.wikipathways.org/figures/PMC2936028__bph0160-1195-f8.html\" target=\"_blank\">Link to pathway figure at PFOCR</a> * <a href=\"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC2936028\" target=\"_blank\">Link to article at PMC</a>"
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    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Number of TRAPI results represented by this pathway: 17\n",
      "Number of CURIEs represented by this pathway: 18\n",
      "Query-specified CURIEs in this pathway: {'MESH:D008687'}\n"
     ]
    },
    {
     "data": {
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       "  <thead>\n",
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       "      <th></th>\n",
       "      <th>normalized_score</th>\n",
       "      <th>resource_id</th>\n",
       "      <th>score</th>\n",
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       "      <th>node_name.n1</th>\n",
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       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>35</th>\n",
       "      <td>97.81250</td>\n",
       "      <td>BTE</td>\n",
       "      <td>0.897734</td>\n",
       "      <td>JUN</td>\n",
       "      <td>type 2 diabetes mellitus</td>\n",
       "      <td>Metformin</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>128</th>\n",
       "      <td>92.00000</td>\n",
       "      <td>BTE</td>\n",
       "      <td>0.785946</td>\n",
       "      <td>MAPK8</td>\n",
       "      <td>type 2 diabetes mellitus</td>\n",
       "      <td>Metformin</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>359</th>\n",
       "      <td>77.56250</td>\n",
       "      <td>BTE</td>\n",
       "      <td>0.597963</td>\n",
       "      <td>MMP9</td>\n",
       "      <td>type 2 diabetes mellitus</td>\n",
       "      <td>Metformin</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>361</th>\n",
       "      <td>77.43750</td>\n",
       "      <td>BTE</td>\n",
       "      <td>0.596226</td>\n",
       "      <td>MAPK10</td>\n",
       "      <td>type 2 diabetes mellitus</td>\n",
       "      <td>Metformin</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>367</th>\n",
       "      <td>77.06250</td>\n",
       "      <td>BTE</td>\n",
       "      <td>0.594616</td>\n",
       "      <td>PRKCA</td>\n",
       "      <td>type 2 diabetes mellitus</td>\n",
       "      <td>Metformin</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>438</th>\n",
       "      <td>72.62500</td>\n",
       "      <td>BTE</td>\n",
       "      <td>0.561892</td>\n",
       "      <td>MAPK9</td>\n",
       "      <td>type 2 diabetes mellitus</td>\n",
       "      <td>Metformin</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>453</th>\n",
       "      <td>71.68750</td>\n",
       "      <td>BTE</td>\n",
       "      <td>0.556071</td>\n",
       "      <td>FOS</td>\n",
       "      <td>type 2 diabetes mellitus</td>\n",
       "      <td>Metformin</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>513</th>\n",
       "      <td>67.93750</td>\n",
       "      <td>BTE</td>\n",
       "      <td>0.528329</td>\n",
       "      <td>FOSL2</td>\n",
       "      <td>type 2 diabetes mellitus</td>\n",
       "      <td>Metformin</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>639</th>\n",
       "      <td>60.06250</td>\n",
       "      <td>BTE</td>\n",
       "      <td>0.201740</td>\n",
       "      <td>PRKCB</td>\n",
       "      <td>type 2 diabetes mellitus</td>\n",
       "      <td>Metformin</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>677</th>\n",
       "      <td>54.65625</td>\n",
       "      <td>BTE</td>\n",
       "      <td>0.157762</td>\n",
       "      <td>PRKCZ</td>\n",
       "      <td>type 2 diabetes mellitus</td>\n",
       "      <td>Metformin</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>778</th>\n",
       "      <td>45.84375</td>\n",
       "      <td>BTE</td>\n",
       "      <td>0.135525</td>\n",
       "      <td>PRKCE</td>\n",
       "      <td>type 2 diabetes mellitus</td>\n",
       "      <td>Metformin</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>963</th>\n",
       "      <td>31.03125</td>\n",
       "      <td>BTE</td>\n",
       "      <td>0.113150</td>\n",
       "      <td>PRKCI</td>\n",
       "      <td>type 2 diabetes mellitus</td>\n",
       "      <td>Metformin</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1250</th>\n",
       "      <td>10.96875</td>\n",
       "      <td>BTE</td>\n",
       "      <td>0.090659</td>\n",
       "      <td>PRKD3</td>\n",
       "      <td>type 2 diabetes mellitus</td>\n",
       "      <td>Metformin</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1251</th>\n",
       "      <td>10.96875</td>\n",
       "      <td>BTE</td>\n",
       "      <td>0.090659</td>\n",
       "      <td>PRKCH</td>\n",
       "      <td>type 2 diabetes mellitus</td>\n",
       "      <td>Metformin</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1254</th>\n",
       "      <td>10.96875</td>\n",
       "      <td>BTE</td>\n",
       "      <td>0.090659</td>\n",
       "      <td>PRKCG</td>\n",
       "      <td>type 2 diabetes mellitus</td>\n",
       "      <td>Metformin</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1278</th>\n",
       "      <td>10.96875</td>\n",
       "      <td>BTE</td>\n",
       "      <td>0.090659</td>\n",
       "      <td>PRKCQ</td>\n",
       "      <td>type 2 diabetes mellitus</td>\n",
       "      <td>Metformin</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1438</th>\n",
       "      <td>10.96875</td>\n",
       "      <td>BTE</td>\n",
       "      <td>0.090659</td>\n",
       "      <td>FOSL1</td>\n",
       "      <td>type 2 diabetes mellitus</td>\n",
       "      <td>Metformin</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "      normalized_score resource_id     score node_name.n0  \\\n",
       "35            97.81250         BTE  0.897734          JUN   \n",
       "128           92.00000         BTE  0.785946        MAPK8   \n",
       "359           77.56250         BTE  0.597963         MMP9   \n",
       "361           77.43750         BTE  0.596226       MAPK10   \n",
       "367           77.06250         BTE  0.594616        PRKCA   \n",
       "438           72.62500         BTE  0.561892        MAPK9   \n",
       "453           71.68750         BTE  0.556071          FOS   \n",
       "513           67.93750         BTE  0.528329        FOSL2   \n",
       "639           60.06250         BTE  0.201740        PRKCB   \n",
       "677           54.65625         BTE  0.157762        PRKCZ   \n",
       "778           45.84375         BTE  0.135525        PRKCE   \n",
       "963           31.03125         BTE  0.113150        PRKCI   \n",
       "1250          10.96875         BTE  0.090659        PRKD3   \n",
       "1251          10.96875         BTE  0.090659        PRKCH   \n",
       "1254          10.96875         BTE  0.090659        PRKCG   \n",
       "1278          10.96875         BTE  0.090659        PRKCQ   \n",
       "1438          10.96875         BTE  0.090659        FOSL1   \n",
       "\n",
       "                  node_name.n1 node_name.n2  \n",
       "35    type 2 diabetes mellitus    Metformin  \n",
       "128   type 2 diabetes mellitus    Metformin  \n",
       "359   type 2 diabetes mellitus    Metformin  \n",
       "361   type 2 diabetes mellitus    Metformin  \n",
       "367   type 2 diabetes mellitus    Metformin  \n",
       "438   type 2 diabetes mellitus    Metformin  \n",
       "453   type 2 diabetes mellitus    Metformin  \n",
       "513   type 2 diabetes mellitus    Metformin  \n",
       "639   type 2 diabetes mellitus    Metformin  \n",
       "677   type 2 diabetes mellitus    Metformin  \n",
       "778   type 2 diabetes mellitus    Metformin  \n",
       "963   type 2 diabetes mellitus    Metformin  \n",
       "1250  type 2 diabetes mellitus    Metformin  \n",
       "1251  type 2 diabetes mellitus    Metformin  \n",
       "1254  type 2 diabetes mellitus    Metformin  \n",
       "1278  type 2 diabetes mellitus    Metformin  \n",
       "1438  type 2 diabetes mellitus    Metformin  "
      ]
     },
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    {
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     "data": {
      "text/html": [
       "<h3>9.&nbsp;Therapeutic targeting of trained immunity</h3>&nbsp;&nbsp;&nbsp;&nbsp; * <a href=\"https://pfocr.wikipathways.org/figures/PMC7069501__nihms-1022857-f0003.html\" target=\"_blank\">Link to pathway figure at PFOCR</a> * <a href=\"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7069501\" target=\"_blank\">Link to article at PMC</a>"
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    {
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     "output_type": "stream",
     "text": [
      "Number of TRAPI results represented by this pathway: 12\n",
      "Number of CURIEs represented by this pathway: 13\n",
      "Query-specified CURIEs in this pathway: {'MESH:D008687'}\n"
     ]
    },
    {
     "data": {
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       "  <tbody>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>99.00000</td>\n",
       "      <td>BTE</td>\n",
       "      <td>0.929857</td>\n",
       "      <td>CD36</td>\n",
       "      <td>type 2 diabetes mellitus</td>\n",
       "      <td>Metformin</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>32</th>\n",
       "      <td>98.00000</td>\n",
       "      <td>BTE</td>\n",
       "      <td>0.900643</td>\n",
       "      <td>TNF</td>\n",
       "      <td>type 2 diabetes mellitus</td>\n",
       "      <td>Metformin</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>90</th>\n",
       "      <td>94.37500</td>\n",
       "      <td>BTE</td>\n",
       "      <td>0.818889</td>\n",
       "      <td>IL1B</td>\n",
       "      <td>type 2 diabetes mellitus</td>\n",
       "      <td>Metformin</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>139</th>\n",
       "      <td>91.31250</td>\n",
       "      <td>BTE</td>\n",
       "      <td>0.776992</td>\n",
       "      <td>NLRP3</td>\n",
       "      <td>type 2 diabetes mellitus</td>\n",
       "      <td>Metformin</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>347</th>\n",
       "      <td>78.31250</td>\n",
       "      <td>BTE</td>\n",
       "      <td>0.600117</td>\n",
       "      <td>NFKB1</td>\n",
       "      <td>type 2 diabetes mellitus</td>\n",
       "      <td>Metformin</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>385</th>\n",
       "      <td>75.93750</td>\n",
       "      <td>BTE</td>\n",
       "      <td>0.584007</td>\n",
       "      <td>RELB</td>\n",
       "      <td>type 2 diabetes mellitus</td>\n",
       "      <td>Metformin</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>612</th>\n",
       "      <td>61.59375</td>\n",
       "      <td>BTE</td>\n",
       "      <td>0.223439</td>\n",
       "      <td>RELA</td>\n",
       "      <td>type 2 diabetes mellitus</td>\n",
       "      <td>Metformin</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>960</th>\n",
       "      <td>31.03125</td>\n",
       "      <td>BTE</td>\n",
       "      <td>0.113150</td>\n",
       "      <td>MAP2K1</td>\n",
       "      <td>type 2 diabetes mellitus</td>\n",
       "      <td>Metformin</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>972</th>\n",
       "      <td>31.03125</td>\n",
       "      <td>BTE</td>\n",
       "      <td>0.113150</td>\n",
       "      <td>MAP2K2</td>\n",
       "      <td>type 2 diabetes mellitus</td>\n",
       "      <td>Metformin</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>987</th>\n",
       "      <td>31.03125</td>\n",
       "      <td>BTE</td>\n",
       "      <td>0.113150</td>\n",
       "      <td>NFKB2</td>\n",
       "      <td>type 2 diabetes mellitus</td>\n",
       "      <td>Metformin</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>988</th>\n",
       "      <td>31.03125</td>\n",
       "      <td>BTE</td>\n",
       "      <td>0.113150</td>\n",
       "      <td>REL</td>\n",
       "      <td>type 2 diabetes mellitus</td>\n",
       "      <td>Metformin</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1169</th>\n",
       "      <td>31.03125</td>\n",
       "      <td>BTE</td>\n",
       "      <td>0.113150</td>\n",
       "      <td>NOD2</td>\n",
       "      <td>type 2 diabetes mellitus</td>\n",
       "      <td>Metformin</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "      normalized_score resource_id     score node_name.n0  \\\n",
       "16            99.00000         BTE  0.929857         CD36   \n",
       "32            98.00000         BTE  0.900643          TNF   \n",
       "90            94.37500         BTE  0.818889         IL1B   \n",
       "139           91.31250         BTE  0.776992        NLRP3   \n",
       "347           78.31250         BTE  0.600117        NFKB1   \n",
       "385           75.93750         BTE  0.584007         RELB   \n",
       "612           61.59375         BTE  0.223439         RELA   \n",
       "960           31.03125         BTE  0.113150       MAP2K1   \n",
       "972           31.03125         BTE  0.113150       MAP2K2   \n",
       "987           31.03125         BTE  0.113150        NFKB2   \n",
       "988           31.03125         BTE  0.113150          REL   \n",
       "1169          31.03125         BTE  0.113150         NOD2   \n",
       "\n",
       "                  node_name.n1 node_name.n2  \n",
       "16    type 2 diabetes mellitus    Metformin  \n",
       "32    type 2 diabetes mellitus    Metformin  \n",
       "90    type 2 diabetes mellitus    Metformin  \n",
       "139   type 2 diabetes mellitus    Metformin  \n",
       "347   type 2 diabetes mellitus    Metformin  \n",
       "385   type 2 diabetes mellitus    Metformin  \n",
       "612   type 2 diabetes mellitus    Metformin  \n",
       "960   type 2 diabetes mellitus    Metformin  \n",
       "972   type 2 diabetes mellitus    Metformin  \n",
       "987   type 2 diabetes mellitus    Metformin  \n",
       "988   type 2 diabetes mellitus    Metformin  \n",
       "1169  type 2 diabetes mellitus    Metformin  "
      ]
     },
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       "<img src=\"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4775314/bin/nihms754130f3.jpg\"/>"
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    {
     "data": {
      "text/html": [
       "<h3>10.&nbsp;Untapped Potential of Tyrosine-based G Protein Signaling</h3>&nbsp;&nbsp;&nbsp;&nbsp; * <a href=\"https://pfocr.wikipathways.org/figures/PMC4775314__nihms754130f3.html\" target=\"_blank\">Link to pathway figure at PFOCR</a> * <a href=\"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4775314\" target=\"_blank\">Link to article at PMC</a>"
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    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Number of TRAPI results represented by this pathway: 8\n",
      "Number of CURIEs represented by this pathway: 9\n",
      "Query-specified CURIEs in this pathway: {'MESH:D003924'}\n"
     ]
    },
    {
     "data": {
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>normalized_score</th>\n",
       "      <th>resource_id</th>\n",
       "      <th>score</th>\n",
       "      <th>node_name.n0</th>\n",
       "      <th>node_name.n1</th>\n",
       "      <th>node_name.n2</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>99.56250</td>\n",
       "      <td>BTE</td>\n",
       "      <td>0.951357</td>\n",
       "      <td>TLR4</td>\n",
       "      <td>type 2 diabetes mellitus</td>\n",
       "      <td>Metformin</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>105</th>\n",
       "      <td>93.43750</td>\n",
       "      <td>BTE</td>\n",
       "      <td>0.806441</td>\n",
       "      <td>STAT3</td>\n",
       "      <td>type 2 diabetes mellitus</td>\n",
       "      <td>Metformin</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>271</th>\n",
       "      <td>83.06250</td>\n",
       "      <td>BTE</td>\n",
       "      <td>0.655347</td>\n",
       "      <td>CCDC88A</td>\n",
       "      <td>type 2 diabetes mellitus</td>\n",
       "      <td>Metformin</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>801</th>\n",
       "      <td>45.84375</td>\n",
       "      <td>BTE</td>\n",
       "      <td>0.135525</td>\n",
       "      <td>PDGFRB</td>\n",
       "      <td>type 2 diabetes mellitus</td>\n",
       "      <td>Metformin</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>802</th>\n",
       "      <td>45.84375</td>\n",
       "      <td>BTE</td>\n",
       "      <td>0.135525</td>\n",
       "      <td>TGFBR3</td>\n",
       "      <td>type 2 diabetes mellitus</td>\n",
       "      <td>Metformin</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>999</th>\n",
       "      <td>31.03125</td>\n",
       "      <td>BTE</td>\n",
       "      <td>0.113150</td>\n",
       "      <td>SLC2A4RG</td>\n",
       "      <td>type 2 diabetes mellitus</td>\n",
       "      <td>Metformin</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1313</th>\n",
       "      <td>10.96875</td>\n",
       "      <td>BTE</td>\n",
       "      <td>0.090659</td>\n",
       "      <td>PDGFRA</td>\n",
       "      <td>type 2 diabetes mellitus</td>\n",
       "      <td>Metformin</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1314</th>\n",
       "      <td>10.96875</td>\n",
       "      <td>BTE</td>\n",
       "      <td>0.090659</td>\n",
       "      <td>TGFBR1</td>\n",
       "      <td>type 2 diabetes mellitus</td>\n",
       "      <td>Metformin</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "      normalized_score resource_id     score node_name.n0  \\\n",
       "7             99.56250         BTE  0.951357         TLR4   \n",
       "105           93.43750         BTE  0.806441        STAT3   \n",
       "271           83.06250         BTE  0.655347      CCDC88A   \n",
       "801           45.84375         BTE  0.135525       PDGFRB   \n",
       "802           45.84375         BTE  0.135525       TGFBR3   \n",
       "999           31.03125         BTE  0.113150     SLC2A4RG   \n",
       "1313          10.96875         BTE  0.090659       PDGFRA   \n",
       "1314          10.96875         BTE  0.090659       TGFBR1   \n",
       "\n",
       "                  node_name.n1 node_name.n2  \n",
       "7     type 2 diabetes mellitus    Metformin  \n",
       "105   type 2 diabetes mellitus    Metformin  \n",
       "271   type 2 diabetes mellitus    Metformin  \n",
       "801   type 2 diabetes mellitus    Metformin  \n",
       "802   type 2 diabetes mellitus    Metformin  \n",
       "999   type 2 diabetes mellitus    Metformin  \n",
       "1313  type 2 diabetes mellitus    Metformin  \n",
       "1314  type 2 diabetes mellitus    Metformin  "
      ]
     },
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     "output_type": "display_data"
    },
    {
     "data": {
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       "<img src=\"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6721652/bin/cells-08-00826-g003.jpg\"/>"
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    {
     "data": {
      "text/html": [
       "<h3>11.&nbsp;Canonical WNT pathway components associated with disease</h3>&nbsp;&nbsp;&nbsp;&nbsp; * <a href=\"https://pfocr.wikipathways.org/figures/PMC6721652__cells-08-00826-g003.html\" target=\"_blank\">Link to pathway figure at PFOCR</a> * <a href=\"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6721652\" target=\"_blank\">Link to article at PMC</a>"
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     "metadata": {},
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    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Number of TRAPI results represented by this pathway: 27\n",
      "Number of CURIEs represented by this pathway: 28\n",
      "Query-specified CURIEs in this pathway: {'MESH:D003924'}\n"
     ]
    },
    {
     "data": {
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>normalized_score</th>\n",
       "      <th>resource_id</th>\n",
       "      <th>score</th>\n",
       "      <th>node_name.n0</th>\n",
       "      <th>node_name.n1</th>\n",
       "      <th>node_name.n2</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>325</th>\n",
       "      <td>79.68750</td>\n",
       "      <td>BTE</td>\n",
       "      <td>0.608821</td>\n",
       "      <td>GSK3B</td>\n",
       "      <td>type 2 diabetes mellitus</td>\n",
       "      <td>Metformin</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>331</th>\n",
       "      <td>79.31250</td>\n",
       "      <td>BTE</td>\n",
       "      <td>0.606281</td>\n",
       "      <td>WNT8A</td>\n",
       "      <td>type 2 diabetes mellitus</td>\n",
       "      <td>Metformin</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>346</th>\n",
       "      <td>78.37500</td>\n",
       "      <td>BTE</td>\n",
       "      <td>0.600998</td>\n",
       "      <td>WNT5B</td>\n",
       "      <td>type 2 diabetes mellitus</td>\n",
       "      <td>Metformin</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>372</th>\n",
       "      <td>76.75000</td>\n",
       "      <td>BTE</td>\n",
       "      <td>0.593826</td>\n",
       "      <td>WNT8B</td>\n",
       "      <td>type 2 diabetes mellitus</td>\n",
       "      <td>Metformin</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>415</th>\n",
       "      <td>74.06250</td>\n",
       "      <td>BTE</td>\n",
       "      <td>0.574814</td>\n",
       "      <td>WNT9B</td>\n",
       "      <td>type 2 diabetes mellitus</td>\n",
       "      <td>Metformin</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>426</th>\n",
       "      <td>73.37500</td>\n",
       "      <td>BTE</td>\n",
       "      <td>0.567227</td>\n",
       "      <td>WNT9A</td>\n",
       "      <td>type 2 diabetes mellitus</td>\n",
       "      <td>Metformin</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>429</th>\n",
       "      <td>73.18750</td>\n",
       "      <td>BTE</td>\n",
       "      <td>0.565333</td>\n",
       "      <td>WNT7B</td>\n",
       "      <td>type 2 diabetes mellitus</td>\n",
       "      <td>Metformin</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>448</th>\n",
       "      <td>72.00000</td>\n",
       "      <td>BTE</td>\n",
       "      <td>0.558402</td>\n",
       "      <td>WNT2B</td>\n",
       "      <td>type 2 diabetes mellitus</td>\n",
       "      <td>Metformin</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>472</th>\n",
       "      <td>70.50000</td>\n",
       "      <td>BTE</td>\n",
       "      <td>0.548672</td>\n",
       "      <td>WNT3A</td>\n",
       "      <td>type 2 diabetes mellitus</td>\n",
       "      <td>Metformin</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>480</th>\n",
       "      <td>70.00000</td>\n",
       "      <td>BTE</td>\n",
       "      <td>0.544751</td>\n",
       "      <td>WNT16</td>\n",
       "      <td>type 2 diabetes mellitus</td>\n",
       "      <td>Metformin</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>534</th>\n",
       "      <td>66.62500</td>\n",
       "      <td>BTE</td>\n",
       "      <td>0.519758</td>\n",
       "      <td>WNT3</td>\n",
       "      <td>type 2 diabetes mellitus</td>\n",
       "      <td>Metformin</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>539</th>\n",
       "      <td>66.31250</td>\n",
       "      <td>BTE</td>\n",
       "      <td>0.516950</td>\n",
       "      <td>WNT6</td>\n",
       "      <td>type 2 diabetes mellitus</td>\n",
       "      <td>Metformin</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>546</th>\n",
       "      <td>65.87500</td>\n",
       "      <td>BTE</td>\n",
       "      <td>0.511789</td>\n",
       "      <td>WNT2</td>\n",
       "      <td>type 2 diabetes mellitus</td>\n",
       "      <td>Metformin</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>553</th>\n",
       "      <td>65.43750</td>\n",
       "      <td>BTE</td>\n",
       "      <td>0.507104</td>\n",
       "      <td>WNT10A</td>\n",
       "      <td>type 2 diabetes mellitus</td>\n",
       "      <td>Metformin</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>568</th>\n",
       "      <td>64.50000</td>\n",
       "      <td>BTE</td>\n",
       "      <td>0.497230</td>\n",
       "      <td>WNT11</td>\n",
       "      <td>type 2 diabetes mellitus</td>\n",
       "      <td>Metformin</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>571</th>\n",
       "      <td>64.31250</td>\n",
       "      <td>BTE</td>\n",
       "      <td>0.495418</td>\n",
       "      <td>WNT4</td>\n",
       "      <td>type 2 diabetes mellitus</td>\n",
       "      <td>Metformin</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>573</th>\n",
       "      <td>64.18750</td>\n",
       "      <td>BTE</td>\n",
       "      <td>0.491640</td>\n",
       "      <td>WNT7A</td>\n",
       "      <td>type 2 diabetes mellitus</td>\n",
       "      <td>Metformin</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>577</th>\n",
       "      <td>63.93750</td>\n",
       "      <td>BTE</td>\n",
       "      <td>0.490108</td>\n",
       "      <td>WNT10B</td>\n",
       "      <td>type 2 diabetes mellitus</td>\n",
       "      <td>Metformin</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>583</th>\n",
       "      <td>63.56250</td>\n",
       "      <td>BTE</td>\n",
       "      <td>0.484834</td>\n",
       "      <td>WNT1</td>\n",
       "      <td>type 2 diabetes mellitus</td>\n",
       "      <td>Metformin</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>590</th>\n",
       "      <td>63.12500</td>\n",
       "      <td>BTE</td>\n",
       "      <td>0.474085</td>\n",
       "      <td>WNT5A</td>\n",
       "      <td>type 2 diabetes mellitus</td>\n",
       "      <td>Metformin</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>594</th>\n",
       "      <td>62.87500</td>\n",
       "      <td>BTE</td>\n",
       "      <td>0.468933</td>\n",
       "      <td>APC</td>\n",
       "      <td>type 2 diabetes mellitus</td>\n",
       "      <td>Metformin</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>682</th>\n",
       "      <td>54.65625</td>\n",
       "      <td>BTE</td>\n",
       "      <td>0.157762</td>\n",
       "      <td>CTNNB1</td>\n",
       "      <td>type 2 diabetes mellitus</td>\n",
       "      <td>Metformin</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>689</th>\n",
       "      <td>54.65625</td>\n",
       "      <td>BTE</td>\n",
       "      <td>0.157762</td>\n",
       "      <td>GSK3A</td>\n",
       "      <td>type 2 diabetes mellitus</td>\n",
       "      <td>Metformin</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>808</th>\n",
       "      <td>45.84375</td>\n",
       "      <td>BTE</td>\n",
       "      <td>0.135525</td>\n",
       "      <td>LRP6</td>\n",
       "      <td>type 2 diabetes mellitus</td>\n",
       "      <td>Metformin</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1009</th>\n",
       "      <td>31.03125</td>\n",
       "      <td>BTE</td>\n",
       "      <td>0.113150</td>\n",
       "      <td>AXIN2</td>\n",
       "      <td>type 2 diabetes mellitus</td>\n",
       "      <td>Metformin</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1010</th>\n",
       "      <td>31.03125</td>\n",
       "      <td>BTE</td>\n",
       "      <td>0.113150</td>\n",
       "      <td>AXIN1</td>\n",
       "      <td>type 2 diabetes mellitus</td>\n",
       "      <td>Metformin</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1321</th>\n",
       "      <td>10.96875</td>\n",
       "      <td>BTE</td>\n",
       "      <td>0.090659</td>\n",
       "      <td>CSNK1A1</td>\n",
       "      <td>type 2 diabetes mellitus</td>\n",
       "      <td>Metformin</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "      normalized_score resource_id     score node_name.n0  \\\n",
       "325           79.68750         BTE  0.608821        GSK3B   \n",
       "331           79.31250         BTE  0.606281        WNT8A   \n",
       "346           78.37500         BTE  0.600998        WNT5B   \n",
       "372           76.75000         BTE  0.593826        WNT8B   \n",
       "415           74.06250         BTE  0.574814        WNT9B   \n",
       "426           73.37500         BTE  0.567227        WNT9A   \n",
       "429           73.18750         BTE  0.565333        WNT7B   \n",
       "448           72.00000         BTE  0.558402        WNT2B   \n",
       "472           70.50000         BTE  0.548672        WNT3A   \n",
       "480           70.00000         BTE  0.544751        WNT16   \n",
       "534           66.62500         BTE  0.519758         WNT3   \n",
       "539           66.31250         BTE  0.516950         WNT6   \n",
       "546           65.87500         BTE  0.511789         WNT2   \n",
       "553           65.43750         BTE  0.507104       WNT10A   \n",
       "568           64.50000         BTE  0.497230        WNT11   \n",
       "571           64.31250         BTE  0.495418         WNT4   \n",
       "573           64.18750         BTE  0.491640        WNT7A   \n",
       "577           63.93750         BTE  0.490108       WNT10B   \n",
       "583           63.56250         BTE  0.484834         WNT1   \n",
       "590           63.12500         BTE  0.474085        WNT5A   \n",
       "594           62.87500         BTE  0.468933          APC   \n",
       "682           54.65625         BTE  0.157762       CTNNB1   \n",
       "689           54.65625         BTE  0.157762        GSK3A   \n",
       "808           45.84375         BTE  0.135525         LRP6   \n",
       "1009          31.03125         BTE  0.113150        AXIN2   \n",
       "1010          31.03125         BTE  0.113150        AXIN1   \n",
       "1321          10.96875         BTE  0.090659      CSNK1A1   \n",
       "\n",
       "                  node_name.n1 node_name.n2  \n",
       "325   type 2 diabetes mellitus    Metformin  \n",
       "331   type 2 diabetes mellitus    Metformin  \n",
       "346   type 2 diabetes mellitus    Metformin  \n",
       "372   type 2 diabetes mellitus    Metformin  \n",
       "415   type 2 diabetes mellitus    Metformin  \n",
       "426   type 2 diabetes mellitus    Metformin  \n",
       "429   type 2 diabetes mellitus    Metformin  \n",
       "448   type 2 diabetes mellitus    Metformin  \n",
       "472   type 2 diabetes mellitus    Metformin  \n",
       "480   type 2 diabetes mellitus    Metformin  \n",
       "534   type 2 diabetes mellitus    Metformin  \n",
       "539   type 2 diabetes mellitus    Metformin  \n",
       "546   type 2 diabetes mellitus    Metformin  \n",
       "553   type 2 diabetes mellitus    Metformin  \n",
       "568   type 2 diabetes mellitus    Metformin  \n",
       "571   type 2 diabetes mellitus    Metformin  \n",
       "573   type 2 diabetes mellitus    Metformin  \n",
       "577   type 2 diabetes mellitus    Metformin  \n",
       "583   type 2 diabetes mellitus    Metformin  \n",
       "590   type 2 diabetes mellitus    Metformin  \n",
       "594   type 2 diabetes mellitus    Metformin  \n",
       "682   type 2 diabetes mellitus    Metformin  \n",
       "689   type 2 diabetes mellitus    Metformin  \n",
       "808   type 2 diabetes mellitus    Metformin  \n",
       "1009  type 2 diabetes mellitus    Metformin  \n",
       "1010  type 2 diabetes mellitus    Metformin  \n",
       "1321  type 2 diabetes mellitus    Metformin  "
      ]
     },
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    },
    {
     "data": {
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       "<hr>"
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       "<img src=\"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6590912/bin/nihms-1530776-f0002.jpg\"/>"
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    {
     "data": {
      "text/html": [
       "<h3>12.&nbsp;Proposed mechanism by which TBI alters hypothalamic-pituitary growth hormone (GH) Axis</h3>&nbsp;&nbsp;&nbsp;&nbsp; * <a href=\"https://pfocr.wikipathways.org/figures/PMC6590912__nihms-1530776-f0002.html\" target=\"_blank\">Link to pathway figure at PFOCR</a> * <a href=\"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6590912\" target=\"_blank\">Link to article at PMC</a>"
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       "<IPython.core.display.HTML object>"
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     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Number of TRAPI results represented by this pathway: 26\n",
      "Number of CURIEs represented by this pathway: 27\n",
      "Query-specified CURIEs in this pathway: {'MESH:D003924'}\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
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       "        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>normalized_score</th>\n",
       "      <th>resource_id</th>\n",
       "      <th>score</th>\n",
       "      <th>node_name.n0</th>\n",
       "      <th>node_name.n1</th>\n",
       "      <th>node_name.n2</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>398</th>\n",
       "      <td>75.12500</td>\n",
       "      <td>BTE</td>\n",
       "      <td>0.579421</td>\n",
       "      <td>PDE4B</td>\n",
       "      <td>type 2 diabetes mellitus</td>\n",
       "      <td>Metformin</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>470</th>\n",
       "      <td>70.62500</td>\n",
       "      <td>BTE</td>\n",
       "      <td>0.549339</td>\n",
       "      <td>STAT4</td>\n",
       "      <td>type 2 diabetes mellitus</td>\n",
       "      <td>Metformin</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>522</th>\n",
       "      <td>67.37500</td>\n",
       "      <td>BTE</td>\n",
       "      <td>0.524265</td>\n",
       "      <td>STAT5A</td>\n",
       "      <td>type 2 diabetes mellitus</td>\n",
       "      <td>Metformin</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>645</th>\n",
       "      <td>58.90625</td>\n",
       "      <td>BTE</td>\n",
       "      <td>0.179841</td>\n",
       "      <td>PDE3B</td>\n",
       "      <td>type 2 diabetes mellitus</td>\n",
       "      <td>Metformin</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>686</th>\n",
       "      <td>54.65625</td>\n",
       "      <td>BTE</td>\n",
       "      <td>0.157762</td>\n",
       "      <td>STAT1</td>\n",
       "      <td>type 2 diabetes mellitus</td>\n",
       "      <td>Metformin</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>976</th>\n",
       "      <td>31.03125</td>\n",
       "      <td>BTE</td>\n",
       "      <td>0.113150</td>\n",
       "      <td>PDE6A</td>\n",
       "      <td>type 2 diabetes mellitus</td>\n",
       "      <td>Metformin</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>977</th>\n",
       "      <td>31.03125</td>\n",
       "      <td>BTE</td>\n",
       "      <td>0.113150</td>\n",
       "      <td>PDE1C</td>\n",
       "      <td>type 2 diabetes mellitus</td>\n",
       "      <td>Metformin</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>978</th>\n",
       "      <td>31.03125</td>\n",
       "      <td>BTE</td>\n",
       "      <td>0.113150</td>\n",
       "      <td>PDE4D</td>\n",
       "      <td>type 2 diabetes mellitus</td>\n",
       "      <td>Metformin</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>979</th>\n",
       "      <td>31.03125</td>\n",
       "      <td>BTE</td>\n",
       "      <td>0.113150</td>\n",
       "      <td>STAT5B</td>\n",
       "      <td>type 2 diabetes mellitus</td>\n",
       "      <td>Metformin</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>980</th>\n",
       "      <td>31.03125</td>\n",
       "      <td>BTE</td>\n",
       "      <td>0.113150</td>\n",
       "      <td>STAT2</td>\n",
       "      <td>type 2 diabetes mellitus</td>\n",
       "      <td>Metformin</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>981</th>\n",
       "      <td>31.03125</td>\n",
       "      <td>BTE</td>\n",
       "      <td>0.113150</td>\n",
       "      <td>PDE3A</td>\n",
       "      <td>type 2 diabetes mellitus</td>\n",
       "      <td>Metformin</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>982</th>\n",
       "      <td>31.03125</td>\n",
       "      <td>BTE</td>\n",
       "      <td>0.113150</td>\n",
       "      <td>PDE8A</td>\n",
       "      <td>type 2 diabetes mellitus</td>\n",
       "      <td>Metformin</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>983</th>\n",
       "      <td>31.03125</td>\n",
       "      <td>BTE</td>\n",
       "      <td>0.113150</td>\n",
       "      <td>PDE6B</td>\n",
       "      <td>type 2 diabetes mellitus</td>\n",
       "      <td>Metformin</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1288</th>\n",
       "      <td>10.96875</td>\n",
       "      <td>BTE</td>\n",
       "      <td>0.090659</td>\n",
       "      <td>STAT6</td>\n",
       "      <td>type 2 diabetes mellitus</td>\n",
       "      <td>Metformin</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1289</th>\n",
       "      <td>10.96875</td>\n",
       "      <td>BTE</td>\n",
       "      <td>0.090659</td>\n",
       "      <td>PDE4A</td>\n",
       "      <td>type 2 diabetes mellitus</td>\n",
       "      <td>Metformin</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1290</th>\n",
       "      <td>10.96875</td>\n",
       "      <td>BTE</td>\n",
       "      <td>0.090659</td>\n",
       "      <td>PDE11A</td>\n",
       "      <td>type 2 diabetes mellitus</td>\n",
       "      <td>Metformin</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1291</th>\n",
       "      <td>10.96875</td>\n",
       "      <td>BTE</td>\n",
       "      <td>0.090659</td>\n",
       "      <td>PDE7A</td>\n",
       "      <td>type 2 diabetes mellitus</td>\n",
       "      <td>Metformin</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1292</th>\n",
       "      <td>10.96875</td>\n",
       "      <td>BTE</td>\n",
       "      <td>0.090659</td>\n",
       "      <td>PDE8B</td>\n",
       "      <td>type 2 diabetes mellitus</td>\n",
       "      <td>Metformin</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1294</th>\n",
       "      <td>10.96875</td>\n",
       "      <td>BTE</td>\n",
       "      <td>0.090659</td>\n",
       "      <td>PDE6C</td>\n",
       "      <td>type 2 diabetes mellitus</td>\n",
       "      <td>Metformin</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1295</th>\n",
       "      <td>10.96875</td>\n",
       "      <td>BTE</td>\n",
       "      <td>0.090659</td>\n",
       "      <td>PDE1A</td>\n",
       "      <td>type 2 diabetes mellitus</td>\n",
       "      <td>Metformin</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1296</th>\n",
       "      <td>10.96875</td>\n",
       "      <td>BTE</td>\n",
       "      <td>0.090659</td>\n",
       "      <td>PDE2A</td>\n",
       "      <td>type 2 diabetes mellitus</td>\n",
       "      <td>Metformin</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1297</th>\n",
       "      <td>10.96875</td>\n",
       "      <td>BTE</td>\n",
       "      <td>0.090659</td>\n",
       "      <td>PDE7B</td>\n",
       "      <td>type 2 diabetes mellitus</td>\n",
       "      <td>Metformin</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1298</th>\n",
       "      <td>10.96875</td>\n",
       "      <td>BTE</td>\n",
       "      <td>0.090659</td>\n",
       "      <td>PDE10A</td>\n",
       "      <td>type 2 diabetes mellitus</td>\n",
       "      <td>Metformin</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1299</th>\n",
       "      <td>10.96875</td>\n",
       "      <td>BTE</td>\n",
       "      <td>0.090659</td>\n",
       "      <td>PDE4C</td>\n",
       "      <td>type 2 diabetes mellitus</td>\n",
       "      <td>Metformin</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1300</th>\n",
       "      <td>10.96875</td>\n",
       "      <td>BTE</td>\n",
       "      <td>0.090659</td>\n",
       "      <td>PDE1B</td>\n",
       "      <td>type 2 diabetes mellitus</td>\n",
       "      <td>Metformin</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1301</th>\n",
       "      <td>10.96875</td>\n",
       "      <td>BTE</td>\n",
       "      <td>0.090659</td>\n",
       "      <td>PDE9A</td>\n",
       "      <td>type 2 diabetes mellitus</td>\n",
       "      <td>Metformin</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "      normalized_score resource_id     score node_name.n0  \\\n",
       "398           75.12500         BTE  0.579421        PDE4B   \n",
       "470           70.62500         BTE  0.549339        STAT4   \n",
       "522           67.37500         BTE  0.524265       STAT5A   \n",
       "645           58.90625         BTE  0.179841        PDE3B   \n",
       "686           54.65625         BTE  0.157762        STAT1   \n",
       "976           31.03125         BTE  0.113150        PDE6A   \n",
       "977           31.03125         BTE  0.113150        PDE1C   \n",
       "978           31.03125         BTE  0.113150        PDE4D   \n",
       "979           31.03125         BTE  0.113150       STAT5B   \n",
       "980           31.03125         BTE  0.113150        STAT2   \n",
       "981           31.03125         BTE  0.113150        PDE3A   \n",
       "982           31.03125         BTE  0.113150        PDE8A   \n",
       "983           31.03125         BTE  0.113150        PDE6B   \n",
       "1288          10.96875         BTE  0.090659        STAT6   \n",
       "1289          10.96875         BTE  0.090659        PDE4A   \n",
       "1290          10.96875         BTE  0.090659       PDE11A   \n",
       "1291          10.96875         BTE  0.090659        PDE7A   \n",
       "1292          10.96875         BTE  0.090659        PDE8B   \n",
       "1294          10.96875         BTE  0.090659        PDE6C   \n",
       "1295          10.96875         BTE  0.090659        PDE1A   \n",
       "1296          10.96875         BTE  0.090659        PDE2A   \n",
       "1297          10.96875         BTE  0.090659        PDE7B   \n",
       "1298          10.96875         BTE  0.090659       PDE10A   \n",
       "1299          10.96875         BTE  0.090659        PDE4C   \n",
       "1300          10.96875         BTE  0.090659        PDE1B   \n",
       "1301          10.96875         BTE  0.090659        PDE9A   \n",
       "\n",
       "                  node_name.n1 node_name.n2  \n",
       "398   type 2 diabetes mellitus    Metformin  \n",
       "470   type 2 diabetes mellitus    Metformin  \n",
       "522   type 2 diabetes mellitus    Metformin  \n",
       "645   type 2 diabetes mellitus    Metformin  \n",
       "686   type 2 diabetes mellitus    Metformin  \n",
       "976   type 2 diabetes mellitus    Metformin  \n",
       "977   type 2 diabetes mellitus    Metformin  \n",
       "978   type 2 diabetes mellitus    Metformin  \n",
       "979   type 2 diabetes mellitus    Metformin  \n",
       "980   type 2 diabetes mellitus    Metformin  \n",
       "981   type 2 diabetes mellitus    Metformin  \n",
       "982   type 2 diabetes mellitus    Metformin  \n",
       "983   type 2 diabetes mellitus    Metformin  \n",
       "1288  type 2 diabetes mellitus    Metformin  \n",
       "1289  type 2 diabetes mellitus    Metformin  \n",
       "1290  type 2 diabetes mellitus    Metformin  \n",
       "1291  type 2 diabetes mellitus    Metformin  \n",
       "1292  type 2 diabetes mellitus    Metformin  \n",
       "1294  type 2 diabetes mellitus    Metformin  \n",
       "1295  type 2 diabetes mellitus    Metformin  \n",
       "1296  type 2 diabetes mellitus    Metformin  \n",
       "1297  type 2 diabetes mellitus    Metformin  \n",
       "1298  type 2 diabetes mellitus    Metformin  \n",
       "1299  type 2 diabetes mellitus    Metformin  \n",
       "1300  type 2 diabetes mellitus    Metformin  \n",
       "1301  type 2 diabetes mellitus    Metformin  "
      ]
     },
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       "<img src=\"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC3618249/bin/1472-6947-13-S1-S4-3.jpg\"/>"
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     "data": {
      "text/html": [
       "<h3>13.&nbsp;combination drug simulation result on T2D model</h3>&nbsp;&nbsp;&nbsp;&nbsp; * <a href=\"https://pfocr.wikipathways.org/figures/PMC3618249__1472-6947-13-S1-S4-3.html\" target=\"_blank\">Link to pathway figure at PFOCR</a> * <a href=\"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC3618249\" target=\"_blank\">Link to article at PMC</a>"
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    {
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     "output_type": "stream",
     "text": [
      "Number of TRAPI results represented by this pathway: 12\n",
      "Number of CURIEs represented by this pathway: 13\n",
      "Query-specified CURIEs in this pathway: {'MESH:D008687'}\n"
     ]
    },
    {
     "data": {
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>normalized_score</th>\n",
       "      <th>resource_id</th>\n",
       "      <th>score</th>\n",
       "      <th>node_name.n0</th>\n",
       "      <th>node_name.n1</th>\n",
       "      <th>node_name.n2</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>36</th>\n",
       "      <td>97.75000</td>\n",
       "      <td>BTE</td>\n",
       "      <td>0.896298</td>\n",
       "      <td>ADIPOQ</td>\n",
       "      <td>type 2 diabetes mellitus</td>\n",
       "      <td>Metformin</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>54</th>\n",
       "      <td>96.62500</td>\n",
       "      <td>BTE</td>\n",
       "      <td>0.861465</td>\n",
       "      <td>IRS2</td>\n",
       "      <td>type 2 diabetes mellitus</td>\n",
       "      <td>Metformin</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>291</th>\n",
       "      <td>81.81250</td>\n",
       "      <td>BTE</td>\n",
       "      <td>0.635893</td>\n",
       "      <td>NR0B2</td>\n",
       "      <td>type 2 diabetes mellitus</td>\n",
       "      <td>Metformin</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>326</th>\n",
       "      <td>79.62500</td>\n",
       "      <td>BTE</td>\n",
       "      <td>0.608582</td>\n",
       "      <td>GRB14</td>\n",
       "      <td>type 2 diabetes mellitus</td>\n",
       "      <td>Metformin</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>509</th>\n",
       "      <td>68.18750</td>\n",
       "      <td>BTE</td>\n",
       "      <td>0.534193</td>\n",
       "      <td>SOST</td>\n",
       "      <td>type 2 diabetes mellitus</td>\n",
       "      <td>Metformin</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>641</th>\n",
       "      <td>58.90625</td>\n",
       "      <td>BTE</td>\n",
       "      <td>0.179841</td>\n",
       "      <td>HRAS</td>\n",
       "      <td>type 2 diabetes mellitus</td>\n",
       "      <td>Metformin</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>650</th>\n",
       "      <td>58.90625</td>\n",
       "      <td>BTE</td>\n",
       "      <td>0.179841</td>\n",
       "      <td>PCK2</td>\n",
       "      <td>type 2 diabetes mellitus</td>\n",
       "      <td>Metformin</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>657</th>\n",
       "      <td>58.90625</td>\n",
       "      <td>BTE</td>\n",
       "      <td>0.179841</td>\n",
       "      <td>AGRP</td>\n",
       "      <td>type 2 diabetes mellitus</td>\n",
       "      <td>Metformin</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>752</th>\n",
       "      <td>54.65625</td>\n",
       "      <td>BTE</td>\n",
       "      <td>0.157762</td>\n",
       "      <td>GHRL</td>\n",
       "      <td>type 2 diabetes mellitus</td>\n",
       "      <td>Metformin</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>777</th>\n",
       "      <td>45.84375</td>\n",
       "      <td>BTE</td>\n",
       "      <td>0.135525</td>\n",
       "      <td>GRB2</td>\n",
       "      <td>type 2 diabetes mellitus</td>\n",
       "      <td>Metformin</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>797</th>\n",
       "      <td>45.84375</td>\n",
       "      <td>BTE</td>\n",
       "      <td>0.135525</td>\n",
       "      <td>PTPN11</td>\n",
       "      <td>type 2 diabetes mellitus</td>\n",
       "      <td>Metformin</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>992</th>\n",
       "      <td>31.03125</td>\n",
       "      <td>BTE</td>\n",
       "      <td>0.113150</td>\n",
       "      <td>SH2B2</td>\n",
       "      <td>type 2 diabetes mellitus</td>\n",
       "      <td>Metformin</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "     normalized_score resource_id     score node_name.n0  \\\n",
       "36           97.75000         BTE  0.896298       ADIPOQ   \n",
       "54           96.62500         BTE  0.861465         IRS2   \n",
       "291          81.81250         BTE  0.635893        NR0B2   \n",
       "326          79.62500         BTE  0.608582        GRB14   \n",
       "509          68.18750         BTE  0.534193         SOST   \n",
       "641          58.90625         BTE  0.179841         HRAS   \n",
       "650          58.90625         BTE  0.179841         PCK2   \n",
       "657          58.90625         BTE  0.179841         AGRP   \n",
       "752          54.65625         BTE  0.157762         GHRL   \n",
       "777          45.84375         BTE  0.135525         GRB2   \n",
       "797          45.84375         BTE  0.135525       PTPN11   \n",
       "992          31.03125         BTE  0.113150        SH2B2   \n",
       "\n",
       "                 node_name.n1 node_name.n2  \n",
       "36   type 2 diabetes mellitus    Metformin  \n",
       "54   type 2 diabetes mellitus    Metformin  \n",
       "291  type 2 diabetes mellitus    Metformin  \n",
       "326  type 2 diabetes mellitus    Metformin  \n",
       "509  type 2 diabetes mellitus    Metformin  \n",
       "641  type 2 diabetes mellitus    Metformin  \n",
       "650  type 2 diabetes mellitus    Metformin  \n",
       "657  type 2 diabetes mellitus    Metformin  \n",
       "752  type 2 diabetes mellitus    Metformin  \n",
       "777  type 2 diabetes mellitus    Metformin  \n",
       "797  type 2 diabetes mellitus    Metformin  \n",
       "992  type 2 diabetes mellitus    Metformin  "
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<hr>"
      ],
      "text/plain": [
       "<IPython.core.display.HTML object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<img src=\"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6917641/bin/fonc-09-01404-g0001.jpg\"/>"
      ],
      "text/plain": [
       "<IPython.core.display.Image object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<h3>14.&nbsp;Drugs with clinical potential in cancer that modulate miRNAs implicated in cell metabolism</h3>&nbsp;&nbsp;&nbsp;&nbsp; * <a href=\"https://pfocr.wikipathways.org/figures/PMC6917641__fonc-09-01404-g0001.html\" target=\"_blank\">Link to pathway figure at PFOCR</a> * <a href=\"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6917641\" target=\"_blank\">Link to article at PMC</a>"
      ],
      "text/plain": [
       "<IPython.core.display.HTML object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Number of TRAPI results represented by this pathway: 10\n",
      "Number of CURIEs represented by this pathway: 11\n",
      "Query-specified CURIEs in this pathway: {'MESH:D008687'}\n"
     ]
    },
    {
     "data": {
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       "\n",
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       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>normalized_score</th>\n",
       "      <th>resource_id</th>\n",
       "      <th>score</th>\n",
       "      <th>node_name.n0</th>\n",
       "      <th>node_name.n1</th>\n",
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       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>24</th>\n",
       "      <td>98.50000</td>\n",
       "      <td>BTE</td>\n",
       "      <td>0.910506</td>\n",
       "      <td>IRS1</td>\n",
       "      <td>type 2 diabetes mellitus</td>\n",
       "      <td>Metformin</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>374</th>\n",
       "      <td>76.62500</td>\n",
       "      <td>BTE</td>\n",
       "      <td>0.591817</td>\n",
       "      <td>SP1</td>\n",
       "      <td>type 2 diabetes mellitus</td>\n",
       "      <td>Metformin</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>404</th>\n",
       "      <td>74.75000</td>\n",
       "      <td>BTE</td>\n",
       "      <td>0.577479</td>\n",
       "      <td>HK2</td>\n",
       "      <td>type 2 diabetes mellitus</td>\n",
       "      <td>Metformin</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>749</th>\n",
       "      <td>54.65625</td>\n",
       "      <td>BTE</td>\n",
       "      <td>0.157762</td>\n",
       "      <td>LILRB1</td>\n",
       "      <td>type 2 diabetes mellitus</td>\n",
       "      <td>Metformin</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1000</th>\n",
       "      <td>31.03125</td>\n",
       "      <td>BTE</td>\n",
       "      <td>0.113150</td>\n",
       "      <td>ESRRG</td>\n",
       "      <td>type 2 diabetes mellitus</td>\n",
       "      <td>Metformin</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1085</th>\n",
       "      <td>31.03125</td>\n",
       "      <td>BTE</td>\n",
       "      <td>0.113150</td>\n",
       "      <td>PAEP</td>\n",
       "      <td>type 2 diabetes mellitus</td>\n",
       "      <td>Metformin</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1088</th>\n",
       "      <td>31.03125</td>\n",
       "      <td>BTE</td>\n",
       "      <td>0.113150</td>\n",
       "      <td>PKM</td>\n",
       "      <td>type 2 diabetes mellitus</td>\n",
       "      <td>Metformin</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1105</th>\n",
       "      <td>31.03125</td>\n",
       "      <td>BTE</td>\n",
       "      <td>0.113150</td>\n",
       "      <td>SLC2A3</td>\n",
       "      <td>type 2 diabetes mellitus</td>\n",
       "      <td>Metformin</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1242</th>\n",
       "      <td>31.03125</td>\n",
       "      <td>BTE</td>\n",
       "      <td>0.113150</td>\n",
       "      <td>ZEB2</td>\n",
       "      <td>type 2 diabetes mellitus</td>\n",
       "      <td>Metformin</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1361</th>\n",
       "      <td>10.96875</td>\n",
       "      <td>BTE</td>\n",
       "      <td>0.090659</td>\n",
       "      <td>GABPA</td>\n",
       "      <td>type 2 diabetes mellitus</td>\n",
       "      <td>Metformin</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "      normalized_score resource_id     score node_name.n0  \\\n",
       "24            98.50000         BTE  0.910506         IRS1   \n",
       "374           76.62500         BTE  0.591817          SP1   \n",
       "404           74.75000         BTE  0.577479          HK2   \n",
       "749           54.65625         BTE  0.157762       LILRB1   \n",
       "1000          31.03125         BTE  0.113150        ESRRG   \n",
       "1085          31.03125         BTE  0.113150         PAEP   \n",
       "1088          31.03125         BTE  0.113150          PKM   \n",
       "1105          31.03125         BTE  0.113150       SLC2A3   \n",
       "1242          31.03125         BTE  0.113150         ZEB2   \n",
       "1361          10.96875         BTE  0.090659        GABPA   \n",
       "\n",
       "                  node_name.n1 node_name.n2  \n",
       "24    type 2 diabetes mellitus    Metformin  \n",
       "374   type 2 diabetes mellitus    Metformin  \n",
       "404   type 2 diabetes mellitus    Metformin  \n",
       "749   type 2 diabetes mellitus    Metformin  \n",
       "1000  type 2 diabetes mellitus    Metformin  \n",
       "1085  type 2 diabetes mellitus    Metformin  \n",
       "1088  type 2 diabetes mellitus    Metformin  \n",
       "1105  type 2 diabetes mellitus    Metformin  \n",
       "1242  type 2 diabetes mellitus    Metformin  \n",
       "1361  type 2 diabetes mellitus    Metformin  "
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<hr>"
      ],
      "text/plain": [
       "<IPython.core.display.HTML object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<img src=\"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6348405/bin/jcbn18-37f03.jpg\"/>"
      ],
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       "<IPython.core.display.Image object>"
      ]
     },
     "metadata": {},
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    },
    {
     "data": {
      "text/html": [
       "<h3>15.&nbsp;ATF4 is activated by the perturbation of mitochondrial function</h3>&nbsp;&nbsp;&nbsp;&nbsp; * <a href=\"https://pfocr.wikipathways.org/figures/PMC6348405__jcbn18-37f03.html\" target=\"_blank\">Link to pathway figure at PFOCR</a> * <a href=\"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6348405\" target=\"_blank\">Link to article at PMC</a>"
      ],
      "text/plain": [
       "<IPython.core.display.HTML object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Number of TRAPI results represented by this pathway: 8\n",
      "Number of CURIEs represented by this pathway: 9\n",
      "Query-specified CURIEs in this pathway: {'MESH:D008687'}\n"
     ]
    },
    {
     "data": {
      "text/html": [
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       "\n",
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       "        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>normalized_score</th>\n",
       "      <th>resource_id</th>\n",
       "      <th>score</th>\n",
       "      <th>node_name.n0</th>\n",
       "      <th>node_name.n1</th>\n",
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       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>315</th>\n",
       "      <td>80.31250</td>\n",
       "      <td>BTE</td>\n",
       "      <td>0.615882</td>\n",
       "      <td>ATF4</td>\n",
       "      <td>type 2 diabetes mellitus</td>\n",
       "      <td>Metformin</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>393</th>\n",
       "      <td>75.43750</td>\n",
       "      <td>BTE</td>\n",
       "      <td>0.581169</td>\n",
       "      <td>MFN2</td>\n",
       "      <td>type 2 diabetes mellitus</td>\n",
       "      <td>Metformin</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>572</th>\n",
       "      <td>64.25000</td>\n",
       "      <td>BTE</td>\n",
       "      <td>0.492810</td>\n",
       "      <td>FXN</td>\n",
       "      <td>type 2 diabetes mellitus</td>\n",
       "      <td>Metformin</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>670</th>\n",
       "      <td>58.90625</td>\n",
       "      <td>BTE</td>\n",
       "      <td>0.179841</td>\n",
       "      <td>SESN2</td>\n",
       "      <td>type 2 diabetes mellitus</td>\n",
       "      <td>Metformin</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>750</th>\n",
       "      <td>54.65625</td>\n",
       "      <td>BTE</td>\n",
       "      <td>0.157762</td>\n",
       "      <td>FGF21</td>\n",
       "      <td>type 2 diabetes mellitus</td>\n",
       "      <td>Metformin</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>896</th>\n",
       "      <td>45.84375</td>\n",
       "      <td>BTE</td>\n",
       "      <td>0.135525</td>\n",
       "      <td>ATG7</td>\n",
       "      <td>type 2 diabetes mellitus</td>\n",
       "      <td>Metformin</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1014</th>\n",
       "      <td>31.03125</td>\n",
       "      <td>BTE</td>\n",
       "      <td>0.113150</td>\n",
       "      <td>ALDH2</td>\n",
       "      <td>type 2 diabetes mellitus</td>\n",
       "      <td>Metformin</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1341</th>\n",
       "      <td>10.96875</td>\n",
       "      <td>BTE</td>\n",
       "      <td>0.090659</td>\n",
       "      <td>COX10</td>\n",
       "      <td>type 2 diabetes mellitus</td>\n",
       "      <td>Metformin</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "      normalized_score resource_id     score node_name.n0  \\\n",
       "315           80.31250         BTE  0.615882         ATF4   \n",
       "393           75.43750         BTE  0.581169         MFN2   \n",
       "572           64.25000         BTE  0.492810          FXN   \n",
       "670           58.90625         BTE  0.179841        SESN2   \n",
       "750           54.65625         BTE  0.157762        FGF21   \n",
       "896           45.84375         BTE  0.135525         ATG7   \n",
       "1014          31.03125         BTE  0.113150        ALDH2   \n",
       "1341          10.96875         BTE  0.090659        COX10   \n",
       "\n",
       "                  node_name.n1 node_name.n2  \n",
       "315   type 2 diabetes mellitus    Metformin  \n",
       "393   type 2 diabetes mellitus    Metformin  \n",
       "572   type 2 diabetes mellitus    Metformin  \n",
       "670   type 2 diabetes mellitus    Metformin  \n",
       "750   type 2 diabetes mellitus    Metformin  \n",
       "896   type 2 diabetes mellitus    Metformin  \n",
       "1014  type 2 diabetes mellitus    Metformin  \n",
       "1341  type 2 diabetes mellitus    Metformin  "
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "from IPython.display import display, Image\n",
    "from IPython.core.display import HTML \n",
    "\n",
    "print('Total number of pathway figure results:', len(pathway_figure_df.index))\n",
    "\n",
    "for index, row in pathway_figure_df.iterrows():\n",
    "    figid = row['figure_id']\n",
    "    fpmc,fname = figid.split(\"__\")\n",
    "    furl = \"https://www.ncbi.nlm.nih.gov/pmc/articles/\"+fpmc+\"/bin/\"+fname\n",
    "    pfocrurl = \"https://pfocr.wikipathways.org/figures/\"+figid.replace(\".jpg\",\".html\")\n",
    "    fdf = trapi_results_df[trapi_results_df['result_name'].isin(row['trapi_result_set'])]\n",
    "    \n",
    "    #time to display\n",
    "    display(HTML('<hr>'))\n",
    "    display(Image(url=furl))\n",
    "    display(HTML('<h3>'+str(index+1)+'.&nbsp;'+figure_metadata_df.loc[figid]['figure_title']+'</h3>'+\n",
    "                 '&nbsp;&nbsp;&nbsp;&nbsp; * <a href=\"'+pfocrurl+'\" target=\"_blank\">Link to pathway figure at PFOCR</a>'+\n",
    "                 ' * <a href=\"https://www.ncbi.nlm.nih.gov/pmc/articles/'+fpmc+'\" target=\"_blank\">Link to article at PMC</a>'))\n",
    "    print('Number of TRAPI results represented by this pathway:',len(row['trapi_result_set']))\n",
    "    print('Number of CURIEs represented by this pathway:',\n",
    "          len(row['figure_curies_in_trapi_results']))\n",
    "    print('Query-specified CURIEs in this pathway:',\n",
    "          row['curie_set'].intersection(user_specified_ids))\n",
    "    display(fdf[display_columns])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Matrix of TRAPI results by pathway figures\n",
    "\n",
    "A binary heatmap: rows are TRAPI result names sorted by original result number (e.g., 0-7), and columns are pathway figures sorted by significance (i.e., iteration order). Note: the query-specified nodes are excluded from the overlap logic, otherwise all results would overlap with all pathway figures."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 182,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Total number of TRAPI results represented by pathways: 191 out of 1600\n"
     ]
    }
   ],
   "source": [
    "#as heatmap, sorting rows by pathway figure groupings\n",
    "pathway_figure_df_tall = pathway_figure_df[[\"figure_id\",\"trapi_result_set\"]].explode(\"trapi_result_set\")\n",
    "rep_trapi_result_count = len(pathway_figure_df_tall['trapi_result_set'].unique())\n",
    "pathway_figure_df_tall = pathway_figure_df_tall.rename(columns={\"trapi_result_set\": \"TRAPI results\"})\n",
    "pathway_figure_df_tall['TRAPI results'] = pathway_figure_df_tall['TRAPI results'].str.split('_').str[1]\n",
    "pathway_figure_df_tall[\"value\"]=1\n",
    "df=pd.pivot_table(pathway_figure_df_tall, values=\"value\", index=[\"TRAPI results\"], columns=\"figure_id\", fill_value=0)\n",
    "df=df[pathway_figure_df['figure_id']]\n",
    "df=df.sort_values(by=list(df.columns))\n",
    "print('Total number of TRAPI results represented by pathways:', rep_trapi_result_count, 'out of', len(trapi_results_df.index))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 183,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "application/javascript": [
       "//disable scrolling for the plots below\n",
       "IPython.OutputArea.prototype._should_scroll = function(lines) {\n",
       "    return false;\n",
       "}\n"
      ],
      "text/plain": [
       "<IPython.core.display.Javascript object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "%%javascript\n",
    "//disable scrolling for the plots below\n",
    "IPython.OutputArea.prototype._should_scroll = function(lines) {\n",
    "    return false;\n",
    "}"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 184,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Pathway figures by TRAPI results, sorting rows by pathway figure groupings\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1080x2750.4 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt_w = len(pathway_figure_df.index)\n",
    "plt_h = rep_trapi_result_count * 0.2 #min(rep_trapi_result_count,plt_w*2)\n",
    "plt.figure(figsize = (plt_w,plt_h))\n",
    "sns.set()\n",
    "sns.heatmap(df, cmap=plt.get_cmap('binary'), cbar=False, yticklabels=True) \n",
    "print('Pathway figures by TRAPI results, sorting rows by pathway figure groupings')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 185,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Pathway figures by TRAPI results, sorting rows by result order\n"
     ]
    },
    {
     "data": {
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\n",
      "text/plain": [
       "<Figure size 1080x2750.4 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#as heatmap, sorting rows by original TRAPI result order\n",
    "sorted_result_bool=trapi_results_df['result_name'].str.split('_').str[1].isin(df.index)\n",
    "sorted_result_names=trapi_results_df.loc[sorted_result_bool,'result_name']\n",
    "sorted_result_names=sorted_result_names.str.split('_').str[1]\n",
    "sorted_result_index=pd.Index(sorted_result_names)\n",
    "df=df.reindex(sorted_result_index[::-1])\n",
    "plt_w = len(pathway_figure_df.index)\n",
    "plt_h = rep_trapi_result_count * 0.2 #min(rep_trapi_result_count,plt_w*2)\n",
    "plt.figure(figsize = (plt_w,plt_h))\n",
    "sns.set()\n",
    "sns.heatmap(df, cmap=plt.get_cmap('binary'), cbar=False, yticklabels=True) \n",
    "print('Pathway figures by TRAPI results, sorting rows by result order')\n",
    "plt.show()"
   ]
  }
 ],
 "metadata": {
  "colab": {
   "authorship_tag": "ABX9TyP6xHxRuuT/oO2JrYOIGJq6",
   "include_colab_link": true,
   "provenance": []
  },
  "kernelspec": {
   "display_name": "Python 3 (ipykernel)",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.9.7"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 1
}