notebooks/bte_clustering.ipynb

{
 "cells": [
  {
   "cell_type": "raw",
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "UZZqPBS0UppM",
    "outputId": "281abfa6-b935-4d99-a267-e237900aa5dc"
   },
   "source": [
    "!pip install numpy pandas requests requests_cache SetSimilaritySearch"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "id": "uG7JfetZjcXH"
   },
   "outputs": [],
   "source": [
    "from copy import copy,deepcopy\n",
    "from itertools import combinations\n",
    "import json\n",
    "import textwrap\n",
    "\n",
    "\n",
    "from fisher import pvalue_npy\n",
    "import matplotlib.pyplot as plt\n",
    "import networkx as nx\n",
    "import pandas as pd"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "id": "rzoUUSDeVz0a"
   },
   "outputs": [],
   "source": [
    "import requests\n",
    "import requests_cache\n",
    "\n",
    "\n",
    "requests_cache.install_cache(\n",
    "    \"pfocr_cache\",\n",
    "    allowable_methods=['GET', 'POST'],\n",
    "    stale_if_error=False,\n",
    ")\n",
    "#requests_cache.remove_expired_responses()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "j9DiSC9NU_Fa"
   },
   "source": [
    "# BTE: Cluster TRAPI Results by Selected PFOCR Figures\n",
    "\n",
    "From discussion with Andrew, we want to first identify a set of figures that can serve to cluster the TRAPI results. We can call these the \"cluster figures\".\n",
    "\n",
    "**Identify Cluster Figures**\n",
    "\n",
    "We identify the top cluster figures by iteratively running Fisher's Exact Test to identify PFOCR figures that are most similar to all the CURIEs from the BTE TRAPI results. To get each cluster figure, we do the following:\n",
    "\n",
    "1. Run Fisher's Exact Test for the CURIEs from each figure vs. the set of the unique CURIEs found from all the TRAPI results, limiting each  set of CURIEs to those with categories found in both PFOCR and BTE TRAPI, e.g., \"disease\" is found in PFOCR and \"biolink:Disease\" is found in BTE TRAPI\n",
    "2. Select the figure with the lowest p-value as a cluster figure\n",
    "3. Exclude that figure and the CURIEs in it\n",
    "4. Repeat to get the next cluster figure, continuing until:\n",
    "    - no more figures match the remaining BTE TRAPI results\n",
    "    - we've found some set number of figures (20)\n",
    "    - we run out of BTE TRAPI results\n",
    "    - we run out of PFOCR figures\n",
    "\n",
    "**Cluster BTE TRAPI Results by Selected PFOCR Figures (Cluster Figures)**\n",
    "\n",
    "Once we have a set of cluster figures, we use them to cluster the BTE TRAPI results. We do this by running Fisher's Exact Test to identify which Cluster Figure each BTE TRAPI result is most similar to. We don't actually cluster the TRAPI results by figure, however, because if a result is highly relevant to two figures, it can show up in both. It's more accurate to think of the figure as being a different view of the TRAPI results.\n",
    "\n",
    "**View TRAPI Results by PFOCR Figure**\n",
    "\n",
    "With BTE TRAPI results \"clustered\" (not actually clustered, because we show the same result multiple times for different figures if it's closely related to both) by PFOCR figure, we display the top figures, each with their most closely related BTE TRAPI results."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "TxW41muraAFL"
   },
   "source": [
    "## Get BTE TRAPI Results\n",
    "\n",
    "imatinib (MESH:D000068877) - Gene - leukemia (MESH:D007938)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "N71NYHWeVCj9",
    "outputId": "d8c2c9ae-9750-455c-ff61-b357b4ff0b2d",
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "200\n",
      "got response from cache\n"
     ]
    }
   ],
   "source": [
    "query = {\n",
    "    \"message\": {\n",
    "        \"query_graph\": {\n",
    "            \"edges\": {\n",
    "                \"e0\": {\n",
    "                    \"subject\": \"n0\",\n",
    "                    \"object\": \"n1\",\n",
    "                    \"predicates\": [\n",
    "                        \"biolink:related_to\"\n",
    "                    ]\n",
    "                },\n",
    "                \"e1\": {\n",
    "                    \"subject\": \"n1\",\n",
    "                    \"object\": \"n2\",\n",
    "                    \"predicates\": [\n",
    "                        \"biolink:related_to\"\n",
    "                    ]\n",
    "                }\n",
    "            },\n",
    "            \"nodes\": {\n",
    "                \"n0\": {\n",
    "                    \"ids\": [\n",
    "                        \"MESH:D000068877\"\n",
    "                    ],\n",
    "                    \"categories\": [\n",
    "                        \"biolink:Drug\"\n",
    "                    ]\n",
    "                },\n",
    "                \"n1\": {\n",
    "                    \"categories\": [\n",
    "                        \"biolink:Gene\"\n",
    "                    ],\n",
    "                },\n",
    "                \"n2\": {\n",
    "                    \"ids\": [\n",
    "                        \"MESH:D007938\"\n",
    "                    ],\n",
    "                    \"categories\": [\n",
    "                        \"biolink:Disease\"\n",
    "                    ]\n",
    "                }\n",
    "            }\n",
    "        }\n",
    "    }\n",
    "}\n",
    "\n",
    "trapi_response = requests.post(\"http://localhost:3000/v1/query\", json=query)\n",
    "#trapi_response = requests.post(\"https://api.bte.ncats.io/v1/query\", json=query)\n",
    "#trapi_response = requests.post(\"https://arax.ncats.io/api/rtxkg2/v1.2/query\", json=query)\n",
    "#trapi_response = requests.post(\"https://arax.ncats.io/api/arax/v1.2/query\", json=query)\n",
    "print(trapi_response.status_code)\n",
    "if trapi_response.status_code != 200:\n",
    "    raise Exception(trapi_response.text)\n",
    "if trapi_response.from_cache:\n",
    "    print(f\"got response from cache\")\n",
    "\n",
    "trapi_message = trapi_response.json()[\"message\"]\n",
    "\n",
    "if len(trapi_message[\"results\"]) == 0:\n",
    "    raise Exception(f'No results returned')"
   ]
  },
  {
   "cell_type": "raw",
   "metadata": {},
   "source": [
    "print(json.dumps(query))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Analyze this GraphML file in Cytoscape: ./bte_sleeve_query_template.graphml\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "{('n0',\n",
       "  'n1'): Text(-0.4994117647751195, -0.18684058718022722, 'biolink:related_to'),\n",
       " ('n1', 'n2'): Text(0.5, 0.1870610157612546, 'biolink:related_to')}"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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vz/PPP09W1uULOKjqj4iI5Nm0aRPDhg1j794/2LHDBCZXV0e3qiK4AG2AHUDZ37AyWxERISMjgyeffJJu3brxxx9/YLWa5585OdUlTNQEllAegRY0QEpEpNqzZ7N/FFhw2MfHh4kT38bd/QAwDVPTtaryBCZRnmUG1Y0sIlJNZWRk8MILL/Duu+8W+rx37968//7750cs5wLtgV+5VKGCyqt8u4/tFGxFRKqh4rLZt99+m8jISCyWgrVtfwc6AWkV3MqK4ANsB1qW61WqS2e8iIhw8bNZu969e5OQkMDgwYMvCLRgAtGXVL16t17AV5R3oAVltiIi1caVZbNF2Qr0BM6WZzMrSG1gPXCFK2xcJWW2IiJV3NVls0XpCqyj8me4XlRkoAVltiIiVVrps9mi7ADuAE5TuQZNuQB1MF3H7Sv8yiIiUsWUXTZblPaYQUVtMNNmKgNPTHu3U9GBFhRsRUSqnE2bNtGuXbtCU3p8fHyIiYnh888/v6gIwdVpiclwJwG1gKsN3OXNgmnfJEx7y38wVJGtUDeyiEjVULJ5s+UhEQgHknGuxS88gWbAYuB6h7ZEma2ISBVQMdlsca6ncJbr6MUJ3SiczTo20IKCrYhIpZeVlcWaNWvK4dnslXAFJgLxwEjM1JqKfp7ref66I8+3YyLluSrUlVA3sohIJWG1WnFxKTpHOnv2LB07duTIkSOlHGlcVjKAhcBrwGEgk/IZueyCyWIbAS8AETjjoC0FWxERJ2ez2bDZbMUGWoDc3Fz2799PzZo1y7nL+ErZgO+AGcBKTBevC5B+ftuVsmDmyVqBHKAPMAHojPMO0lKwFRGpNOLj43n11VcJCQmhRYsW3HfffYWyXZvN5uBs9nKswG/Aj8C3wBZgF6ar1/6c13b+ZSE/eOZgCiK0BkKBEOAm4Foqy9NQRz/FFhGRImRlZVGzZs2891u2bGHUqFE88cQTeHh4MHbsWAICArj55pvzAq5zB1owgfG6868B5z+zB+BDmK7mc0AWpr6sB6aLOIDKFFiLomArIuJkDhw4QHx8PF27diUzMxOr1cqxY8cYMGAATzzxBADZ2dmMGjWKuLi4S3YvO7+CAbjqqsz/QiIiVZKbmxtbtmwhPDyc22+/nczMTPbt28emTZsA83x2xIgRNGrUiMTERAe3VkpCwVZExMn4+/vz559/kpCQwLPPPkvz5s15+umnSUpKYubMmbi6uhIXF4fFYiEoKMjRzZUS0AApEREncOHgpsOHD7NkyRJOnDhB79696dSpE99//z0TJ07E39+fn3/+mWeeeYaoqCjHNVpKTMFWRMRB5s2bh9VqJSoqqlCwtf+8a9cuZs+ezTXXXMPf//53APbt20daWhr+/v40bNjQkc2XK6BuZBERBzh37hyurq4sXryYlJQULBYLVqtZ9MEedFu3bk2XLl1ITk7mnnvuoXv37lgsFoKDg2nYsCG5ubmOvAW5AspsRUQcZOfOnbz77rv4+PjwxhtvFNpmz25zc3NJTExk+fLlREVF0aRJEwe1VkpDwVZExAFeeukl1q1bR8uWLdmzZw/PPvss4eHhl1ySEcxIZFdX51jvV0pO82xFRMqZPUDas9XDhw8TFxdHbGwsWVlZfPrpp3z00UeEhIQQGBiYF3DtuVDBgVOVe05t9aV/NRGRcmS1WvMy0UOHDgGQnp5OXFwcNpsNHx8f7r77bry9vZk1axZgAmpOTg4WiwWLxcL333/PlClTyM3NrQSrRElRFGxFRMqRi4sLP/30E7169WLSpElMmzYNV1dXBg0axGuvvQZAYGAgNWvWZO3atXz99deAWdgCTHfzCy+8wIgRI9R9XImpG1lEpAxlZmZSq1atvPfHjh0jOjqayZMnc+bMGcaNG0eDBg2IiIhg+PDh+Pr6kpWVRWpqKu+//z6dOnUCzOCp0aNHExUVxcaNG5XRVnLKbEVEykhaWhpvvfUWVquVPXv2sHXrVrKysvDz8+P48eO88MILREVFMXLkSDp27MgHH3xARkYG8fHxzJo1Ky/QZmRk8OOPP7J06VIee+wxBdoqQKORRUTK0Pjx41mxYgXe3t5ER0dz7733Ehoais1mY82aNfj7+wPw2Wef0adPH9zd3fOOtVqtec9ppWpRZisiUgoXLixx4403kpmZSWRkJA8//DA2m41hw4bRqlUrLBYLCQkJhIWF8c033xQKqpWnTJ5cDWW2IiJXqeCc2FWrVtGmTRuaNWvGtm3bGDJkCKtXr6ZFixZkZGQwbdo0EhIS2LdvH2PHjmX48OEObr1UJAVbEZErdPToUfz8/ABISEhg9OjRNGvWjGPHjhEVFUVERAQTJkwgKSmJZcuWkZSUROPGjfHw8OD06dPUqVMH4LILWEjVoWArInIFXnnlFfbv38+bb76Jh4cH48ePp2/fvvTq1Yt27drRoEEDoqOjueOOO+jatStNmzblq6++YvHixYSGhgIKstWRgq2ISAnYA+TRo0eJjIwkKiqKRx99lPT0dPbt28fAgQMZNGgQqamp7N27l1mzZpGdnc3WrVvp3LkzjRo1cvQtiAPpTysRkUuw2WyFlk/08/Nj+PDhzJ07l8TERLy8vNiyZQt33nknEyZM4O6772bTpk0sWrQIX19fHnjgARo1aqQKPdWcgq2IyCVYLBZcXFxISEjgjTfeYM+ePYSHhxMcHMysWbPIysqiadOmrFixgtWrV/POO+8wdOhQBg4cmLcKFKDVn6o5dSOLiFzgwgIAb775JvPnz6d///7Ex8cTEhJCZGQk/fr1Y/To0fTq1Yu5c+eyfPlyHn74YR577LG882gqj4AyWxGRQuyL/VssFlJSUkhOTubUqVPExcXRsmVL4uLiaNCgAXXq1GHMmDHMmjUrbzrPypUr8wKtfYEKEVCwFREpxN7du2zZMgYNGsTp06dZt24doaGhxMTE8MknnxAeHk5KSgr9+vWjQ4cOnD59GgAPDw+sViugUnhSmLqRRUQKyM3N5aGHHsLNzY1//OMfdOzYkcmTJ7N161a++OILAH744QfmzJnDlClTaNCggYNbLJWB/vQSkWqrqFzD1dWVnj17EhcXl7f4RK9evWjYsCERERFER0czfPhwOnbsqEArJabMVkSqvXPnzuHh4UFsbCzt2rWjXr169O7dmxtvvDGv5mxaWhqrV68mKSmJ4cOHExAQ4OBWS2WiYCsi1cqOHTvYvXs3/fv3B+CDDz7AZrMxcuRIoqOjiYiIoG3btuzfv5+77rqLOXPmEBYWdtF5cnNzNZ1HSkzdyCJSraSmpjJ27FiSk5MB+O6772jVqhUAU6dOpW3btlitVq655hrGjBnD0KFDycnJKXQOm82mQCtXRMFWRKo8m82W9+revTuRkZGMGzeO3Nxc9u7dS/PmzQHIzs4GzEjizMxMnn76aWbOnFlocQpAU3rkiqkbWUSqtIKL/p88eRJfX18AgoODueWWW3B1dSUiIoJu3brlHfPWW2/h5ubGk08+6YgmSxWkYCsiVVZOTk5eVjp16lTWrVvHgw8+yPjx49m5cyfdu3enS5cu+Pr6kp6ejo+PDz179iQ8PFzzZKVM6f9NIlJl2HMH+whiNzc3/vzzT8aMGUNqaiqTJ09mw4YNzJw5k+DgYMaNG4e7uzsxMTHMmDGDsLAwevTokRdo7QtUiJSW2+V3ERGpHKxWK66urvj6+uatS7xx40Y+/vhjvvrqK2666SbS09NZuXIlGzduJDo6mqCgIN577z0ef/zxvGe39mOV3UpZUTeyiFQJCxYsYMWKFXzyySdYrVb69u3LpEmT6NKlC0OGDMHDw4P//ve/APzrX//i0KFDREdHc+rUKXx9fbVAhZQr/dkmIlVC3759iYuL47PPPsPFxYVbb72VESNGAPDcc89x/PhxFi5cCJA3x/b48eO0atWKBg0aqMtYypUyWxGptOwLS9hHHC9evDhvHWNfX1+6du1KSEgIM2bMYP78+cTExDB79myCgoLIysqiZs2ajr4FqSaU2YpIpWR/Pguwc+dOsrOzCQ8PJyQkJG/KzuLFi/nwww/58ccfGThwIO3bt+fQoUMA1KxZs8i1kUXKgzJbEalUsrOzqVGjBgCJiYkMGzYMb29v6taty6hRo7j99tvp0KEDL774IhEREbz++utMnz6dEydOaIlFcRhltiJSKWRlZTF27Fj++OMPwHQhz549m5EjR7J+/Xq6devGkiVL+PXXX3nrrbd49dVXOXHiBM8//zxz584FUKAVh1GwFRGnl52dTc2aNXnppZdo2LAhhw8fxtXVlW+//Za6desC8Mgjj9CiRQtiY2Pp2bMnzZo1ywuyffv2dWDrRTTPVkSc2JEjR2jYsGFet7Gfnx9RUVHUr1+fadOmERUVxTfffEPPnj3x8/OjRo0aHDhwAIDly5dftKaxiKMosxURp7R79266du1KRkYGKSkp3HPPPUydOpXg4GB++eUXvvnmG9q2bUtOTg6PP/44v/32G2vWrMlbmMJeLCA3N9eRtyECaICUiDixZ555hvj4ePz8/Ljzzjs5duwYR44cYf369YSFhTFu3Djq1q3LlClTOHr0KKGhoTz11FOObrbIRRRsRcRppaamcv/99+Pn58fSpUvJyMggPj6emTNncvDgQbp27crLL7+Mm5tboVHKBSv9iDgD/b9RRJxWvXr1ePbZZ9m8eTPHjh3D09OTBg0a4O3tzfjx49m2bVvevNkaNWrkzZtVoBVno8xWpNKyAr8BB4FM4BzwF+AOeAC1gMbAtVSmv6v/+usv3N3dC3329NNPs2vXLtauXUtiYiIDBgzgyy+/xNPT86J9RZyRgq1IpWAPrD8A3wJbgF1ADcA+d9R2/mU5/wLIBbKB1kAoEALcjDMG4PT0dCZOnEhCQgKff/45np6eedvS0tIIDg7G39+fxo0bc8899zBq1Cggv0KPiDNTsBVxWjZgGzADWIWZqecCpJ/fdqUsgBcmcOcAvYEJwK3kB2fHiI2NZdiwYSQnJwMwZ84cHn30UWrVqpW3z6pVq1i1ahVTp06lXr16jmqqyFVRsBVxOhnAfGA6cBjTRVweFWlcMF3NjYDngUcBz0seUdbs2ey7775b6POHHnqITz75pNgVnzQASiobBVsRp5GEyWLnkZ/BVhR7xjsYk+22KPcrXpjNAvj4+PD2228TGRlZbNewAq1URgq2Ig6XC7wOTMU8X81xYFvcMM+BozHZbtmvJVxcNtu7d2/ef/99AgICyvyaIo6mYCviUIlAOJCM6T52Fp5AM2AxcH2ZnfVqs1mRyk59MSIOkQtMA24CEnCuQAumPQmY9k3DtPfqpaen8+STT3LHHXcUCrS9e/cmISGBwYMHK9BKlabMVqTC/Q48iPNls8WxZ7nLgJZXfLSyWREFW5EK9hPQHThN+YwwLi8uQB3gK6B9iY7Qs1mRfAq2IhVmC9CLih1lXNa8gLVA10vupWxWpDA9sxWpEFuAu6ncgRZM+3sCW4veqmezIkVSZitS7n4CbqPyB9qCvIDNFOxSVjYrUjwFW5Fy9TtmLeJTjm5IOfABtpOe7q9nsyKXoWArUm5yMJlfIpVrMFRJuZCeHkT79jaSkvbmfapsVuRiemYrUm6mA3upmoEWzH0l8/DDe/M+0bNZkaIpsxUpF4mYBSEyHd2Qcnf2LNxxhzdPPPGuslmRYijYipQ5e/fxr1xdKbzKxWqF3NzrqFEjgfJYS1mkKlA3skiZm45ZHarqB1oAFxeoUeMA5r5FpCjKbEXKVBJwI9Wh+/hitYB4KqI8n0hlo8xWpEzNwJTJq46ygTcd3QgRp6TMVqTMZAB+wFlHN8SBagNHMcULRMROma1ImVkAVPeRuBZgkaMbIeJ0FGxFyoQNeB1Hlsz76y94+GFo2hQsFoiNdUQrMoDXqC6Dw0RKSsFWpExsAw47uhGEhsLHH4O/vyNb8SfwnSMbIOJ0FGxFysQMKupZbdOmMGMGtGsHdetC//5w7hy4u8PTT5uA6+rQ6a6ZmO9DROwUbEVKzQqsoiK7ThcvhrVrITkZfv4Z5s6tsEuXgBVYSdVdplLkyinYipTab4BbhV7xqacgIADq1YO+fWHHjgq9fAm4Yb4XEQEFW5Ey8AMV/Z9SwWeytWtDutOVynUBfnR0I0SchoKtSKl9S9UqDF8W0jHfi4iAgq1IGdiCs0x1ycoyg6XATAU6dw4cs2yNDfO9iAgo2IqUkhXY5ehG5LnuOqhVCw4ehLvvNj/v2+eo1uxCg6REDC3XKFIqu4GbUTdyUTwxz22vc3RDRBxOma1IqRxENVyL4wYccnQjRJyCgq1IqVTHUnpXQt+PCCjYipTSOZxlcJTzsWG+HxFRsBUplb9QsC2ODchydCNEnIKCrchVatq0KRs27OLCsnqbN5tRwSURFQXR0Vd+XGwsNGlS0pY6igWo6ehGiDgFBVuRUnHnwmAbFga7d1/5ma72uJI4cwbGjzdFDDw94ZprTDm+78q1OI8F8CjPC4hUGgq2IqXi7ugGXFZWFnTvDvHxsHIlnD4NiYkQEQFr1pT31WuV9wVEKgUFW5FS2L79T9q0OY2vLwwdalZsurCLNzERunUDHx+44Qb43/+KPteFxxVXSq8o77wDbdrAgQMXb/voI/P58uXQtq0pv+fpaTLbyZPz99u1C3r0MMUNrrvOVBayi4qCJ56A3r3B2xs6d4akpMsdmwMEFPPNiVQvCrYipTB//nrWratBUhLs2QNTpxbenp1tqvL07AlHj8K//w0DB5a8u7gkpfSmTDGfb9qUH6x9fGDL+dUSN2wwq0l5ehZ/nYwMEywHDDDtXLQIxoyBX3/N32fRInj5ZTh5Elq2hEmTLndsNnBtyW5UpIpTsBUphbFjxxIYeD316pngs3Bh4e3btpmKPC+8YIq7d+8OffpcvF9xLlVKz2Yzz2HXr4evvoIGDfK3paWZIvIAx48XrhK0Y4cJxnXq5A/IWrnSZNJDh4KbG3ToAA89BEuW5B/34INwyy1m+8CB+W0p/th66FeMiFGxRThFqpjAwEAgFPiZoCAbhy5YMOnQIQgMBJcCMScoyKxdXBIXltIreP60NHj/ffjkE9PNXJz69eHPP/Pft29vjt2wAUaMMJ/t22cGS/n45O+XkwORkcW3xV7Wr/hj/1aiexSpDvRnp0gppKSkACGAF/v3myy0oIAASEkBa4H1+Pfvh8aNS39tX1+TVQ4dClu3Fr/fnXea7Dcjo/h9AgPh9ttNELa/0tPhP/+5fDuKPtab//xn4pXcjkiVpmArUgozZ87kwIEmpKbm8s9/mkFMBXXubLLA6dPN89vYWPj8czMSuCx06wbz50O/fvD990XvM3gwNGpkuoF/+QVyc81Aqx9+yN+nTx/zzPmjj0w7s7Nh+3YzuOtyij42m8REn7K4RZEqQcFWpBQGDBhAz56P07z5WVq0yF+gws7d3QTXNWvgb38zA4fmzYPWrcuuDT16wIcfmme6cXHmMy8vs0gGgIeHeabbpo0ZTWx/Vrt9e/6IY29vk/0uWmSycX9/eP55M23ocoo+NousLP/LHyxSTajEnkiZeAhYjuq3gvkb/kHgU0c3RMRpKLMVKRMT0AIOdrUw34eI2CmzFSkTNqAV8LujG+IErgV2c+EyliLVmTJbkTJhAZ4DvBzdEAfzAp5HgVakMGW2ImUmA/ADzjq6IQ5UGzh2/n9FxE6ZrUiZ8QQGU33XinEDhqBAK3IxZbYiZSoJm+1GLJZMRzfEAWoB8UALRzdExOkosxUpQ7GxKcyY4ZG3lGH1URt4EQVakaIpsxUpA+np6UycOJF3330XFxezSH+bNqacXdXnArQBdgDV4oZFrpiCrUgpxcbGMmzYMJKTk/M+u+UWb7ZuPYebW7YDW1ZRagFxQBkuiyVSxagbWeQqpaen8+STT3LHHXcUCrS9e/dm2bJduLlNxgyaqso8Md3HCrQil6LMVuQqFJXN+vj48PbbbxMZGYnFYgFygfbAr1TNZRzVfSxSUspsRa7ApbLZhIQEBg8efD7QgglAy4A6jmhqBaiDuT8FWpHLUWYrUkIly2aL8hNwG1CVhih7AZsxmbuIXI4yW5HLuLJstigdgLVUncUeamPup72D2yFSeSizFbmEq89mi7IF6EXlznC9MIG2q6MbIlKpKLMVKULps9mihGK6Xn2ofP/puWDavRkFWpErp8xW5AJlm80W5XdMcfVkTPECZ+cJNMMMhmrp4LaIVE6V7c9rkXJTPtlsUVpipstMwiwI4azl6CyY9k3CtFeBVuRqKbMVoSKy2eIkAuE4X5Zrz2YXA9c7uC0ilZ8yW6nWMjMzKyibLc71FM5yHV2ez43C2awCrUhZUGYr1VpqairNmjXj9OnTQEVls8VJAt4EYjBduBWZ6XoCNkw92mdQ9R6RsqXMVqo8q7X4pRJr1arFv//9b6Ais9nitABmAUeB/3f+vSfl95+py/nztwTePn/dWSjQipQ9ZbZSZdlsNmw2Gy4ulw5WZ8+eZevWrdx1110OCrLFsQHfATOAlZguXhfMPN2r+c/WgpknawVygD7ABKAzzjtIS6RqULCVKi8+Pp5XX32VkJAQWrRowX333YfVar1sEHYuVuA34EfgW8wCGbsw6xLbn/Pazr8s5AfPHExBhNaYeb4hwE3AtahjS6TiOHo0hkiZysrKombNmnnvt2zZwqhRo3jiiSfw8PBg7NixBAQEcPPNN1eygOsCXHf+NeD8Z/YAfAjIBM4BWUBNwAMz0CkABVYRx1OwlSrjwIEDxMfH07VrVzIzM7FarRw7dowBAwbwxBNPAJCdnc2oUaOIi4urRIG2OAUDsIg4s8r+20Ykj5ubG1u2bCE8PJzbb7+dzMxM9u3bx6ZNmwDIzc1lxIgRNGrUiMTERAe3VkSqEwVbqTL8/f35888/SUhI4Nlnn6V58+Y8/fTTJCUlMXPmTFxdXYmLi8NisRAUFOTo5opINaIBUlKp2Wy2QiOIDx8+zJIlSzhx4gS9e/emU6dOfP/990ycOBF/f39+/vlnnnnmGaKiohzXaBGpdhRspdKZN28eVquVqKioQsHW/vOuXbuYPXs211xzDX//+98B2LdvH2lpafj7+9OwYUNHNl9EqiF1I0ulcu7cOVxdXVm8eDEpKSlYLJa8RSvsQbd169Z06dKF5ORk7rnnHrp3747FYiE4OJiGDRuSm5vryFsQkWpIma1UOjt37uTdd9/Fx8eHN954o9A2e3abm5tLYmIiy5cvJyoqiiZNmjiotSIiCrZSybz00kusW7eOli1bsmfPHp599lnCw8MvO2c2NzcXV1fXCmypiEg+zbMVp2UPkPZs9fDhw8TFxREbG0tWVhaffvopH330ESEhIQQGBuYFXPvfjwUHTlX+ObUiUpnpN5A4JavVmpeJHjp0CDDF3ePi4rDZbPj4+HD33Xfj7e3NrFmzABNQc3JysFgsWCwWvv/+e6ZMmUJubq6TrXksItWNgq04JRcXF3766Sd69erFpEmTmDZtGq6urgwaNIjXXnsNgMDAQGrWrMnatWv5+uuvAbOwBZju5hdeeIGRI0eq+1hEHE7dyOIUMjMzqVWrVt77Y8eOER0dzeTJkzlz5gzjxo2jQYMGREREMHz4cHx9fcnKyiI1NZX333+fTp06AWbw1OjRoxk6dCgbN25URisiTkGZrThcWloab731FlarlT179rB161aysrLw8/Pj+PHjvPDCC0RFRTFy5Eg6duzIBx98QEZGBvHx8cyaNSsv0GZkZBAXF8fSpUsZPXq0Aq2IOA2NRhanMH78eFasWIG3tzfR0dHce++9hIaGYrPZWLNmDf7+/gB89tln9OnTB3d397xjrVZr3nNaERFnpMxWHOLChSVuvPFGMjMziYyM5OGHH8ZmszFs2DBatWqFxWIhISGBsLAwvvnmm0JB1T4CWYFWRJyZMlupcAXnxK5atYo2bdrQrFkztm3bxpAhQ1i9ejUtWrQgIyODadOmkZCQwL59+xg7dizDhw93cOtFRK6cgq1UmKNHj+Ln5wdAQkICo0ePplmzZhw7doyoqCgiIiKYMGECSUlJLFu2jKSkJBo3boyHhwenT5+mTp06AJWs6LuIiIKtVJBXXnmF/fv38+abb+Lh4cH48ePp27cvvXr1ol27djRo0IDo6GjuuOMOunbtStOmTfnqq69YvHgxoaGhgIKsiFReCrZSruwB8ujRo0RGRhIVFcWjjz5Keno6+/btY+DAgQwaNIjU1FT27t3LrFmzyM7OZuvWrXTu3JlGjRo5+hZEREpNaYKUC5vNVmj5RD8/P4YPH87cuXNJTEzEy8uLLVu2cOeddzJhwgTuvvtuNm3axKJFi/D19eWBBx6gUaNGqtAjIlWCgq2UC4vFgouLCwkJCbzxxhvs2bOH8PBwgoODmTVrFllZWTRt2pQVK1awevVq3nnnHYYOHcrAgQPzVoECtPqTiFQJ6kaWMnNhAYA333yT+fPn079/f+Lj4wkJCSEyMpJ+/foxevRoevXqxdy5c1m+fDkPP/wwjz32WN55NJVHRKoSZbZSJuyL/VssFlJSUkhOTubUqVPExcXRsmVL4uLiaNCgAXXq1GHMmDHMmjUrbzrPypUr8wKtfYEKEZGqRMFWyoS9u3fZsmUMGjSI06dPs27dOkJDQ4mJieGTTz4hPDyclJQU+vXrR4cOHTh9+jQAHh4eWK1WQKXwRKRqUjeylInc3Fweeugh3Nzc+Mc//kHHjh2ZPHkyW7du5YsvvgDghx9+YM6cOUyZMoUGDRo4uMUiIhVHaYRcsaL+PnN1daVnz57ExcXlLT7Rq1cvGjZsSEREBNHR0QwfPpyOHTsq0IpItaPMVq7auXPn8PDwIDY2lnbt2lGvXj169+7NjTfemFdzNi0tjdWrV5OUlMTw4cMJCAhwcKtFRCqegq2UyI4dO9i9ezf9+/cH4IMPPsBmszFy5Eiio6OJiIigbdu27N+/n7vuuos5c+YQFhZ20Xlyc3M1nUdEqh11I0uJpKamMnbsWJKTkwH47rvvaNWqFQBTp06lbdu2WK1WrrnmGsaMGcPQoUPJyckpdA6bzaZAKyLVkoKtFMtms+W9unfvTmRkJOPGjSM3N5e9e/fSvHlzALKzswEzkjgzM5Onn36amTNnFlqcAtCUHhGpttSNLEUquOj/yZMn8fX1BSA4OJhbbrkFV1dXIiIi6NatW94xb731Fm5ubjz55JOOaLKIiNNSsJWL5OTk5GWlU6dOZd26dTz44IOMHz+enTt30r17d7p06YKvry/p6en4+PjQs2dPwsPDNU9WRKQI+s0oeVN57COI3dzc+PPPPxkzZgypqalMnjyZDRs2MHPmTIKDgxk3bhzu7u7ExMQwY8YMwsLC6NGjR16gtS9QISIihtvld5Gqzmq14urqiq+vb966xBs3buTjjz/mq6++4qabbiI9PZ2VK1eyceNGoqOjCQoK4r333uPxxx/Pe3ZrP1bZrYhIYepGruYWLFjAihUr+OSTT7BarfTt25dJkybRpUsXhgwZgoeHB//9738B+Ne//sWhQ4eIjo7m1KlT+Pr6aoEKEZESUApSzfXt25e4uDg+++wzXFxcuPXWWxkxYgQAzz33HMePH2fhwoUAeXNsjx8/TqtWrWjQoIG6jEVESkCZbTVkX1jCPuJ48eLFeesY+/r60rVrV0JCQpgxYwbz588nJiaG2bNnExQURFZWFjVr1nT0LYiIVCrKbKsZ+/NZgJ07d5KdnU14eDghISF5U3YWL17Mhx9+yI8//sjAgQNp3749hw4dAqBmzZpFro0sIiLFU2ZbTWRnZ1OjRg0AEhMTGTZsGN7e3tStW5dRo0Zx++2306FDB1588UUiIiJ4/fXXmT59OidOnNASiyIipaTMtorLyspi7Nix/PHHH4DpQp49ezYjR45k/fr1dOvWjSVLlvDrr7/y1ltv8eqrr3LixAmef/555s6dC6BAKyJSSgq2VVh2djY1a9bkpZdeomHDhhw+fBhXV1e+/fZb6tatC8AjjzxCixYtiI2NpWfPnjRr1iwvyPbt29eBrRcRqTo0z7YKOnLkCA0bNszrNvbz8yMqKor69eszbdo0oqKi+Oabb+jZsyd+fn7UqFGDAwcOALB8+fKL1jQWEZHSUWZbxezevZuuXbuSkZFBSkoK99xzD1OnTiU4OJhffvmFb775hrZt25KTk8Pjjz/Ob7/9xpo1a/IWprAXC8jNzXXkbYiIVCkaIFUFPfPMM8THx+Pn58edd97JsWPHOHLkCOvXrycsLIxx48ZRt25dpkyZwtGjRwkNDeWpp55ydLNFRKosBdsqKDU1lfvvvx8/Pz+WLl1KRkYG8fHxzJw5k4MHD9K1a1defvll3NzcCo1SLljpR0REyo5+s1ZB9erV49lnn2Xz5s0cO3YMT09PGjRogLe3N+PHj2fbtm1582Zr1KiRN29WgVZEpHxUw8zWCvwGHAQygXPAX4A74AHUAhoD11KZ/hbJysoCKLS609NPP82uXbtYu3YtiYmJDBgwgC+//BJPT0/c3d0d1VQRkWqnigdbe2D9AfgW2ALsAmoA9rmjtvMvy/kXQC6QDbQGQoEQ4GacNQDHxsYyfPhwlixZQrt27fJGE6elpREcHIy/vz+NGzfmnnvuYdSoUUB+hR4RESl/VTDY2oBtwAxgFWZ2kwuQfn7blbIAXpjAnQP0BiYAt5IfnB0jPT2diRMn8u677wLQsmVLduzYgaenZ94+q1atYtWqVUydOpV69eo5qqkiItVaFQq2GcB8YDpwGNNFXB4VaVwwXc2NgOeBRwHPSx5RHmJjYxk2bBjJycl5n/n4+LB+/XpuvvnmIrNWDYASEXGMKvCbNwl4HPADnjn/PoPyCbScP28G8Dsw7vx1Hz9/3fKXnp7Ok08+yR133FEo0Pbu3ZuEhAQ6deqkQCsi4mQqcWabC7wOTMU8X81xYFvcMM+BozHZbvmsJVxcNvv2228TGRmpZ7AiIk6qkgbbRCAcSMZkmc7CE2gGLAauL7OzXvhs1q537968//77BAQElNm1RESk7FWyfsVcYBpwE5CAcwVaMO1JwLRvGqa9pRMbG0u7du0KBVofHx9iYmL4/PPPFWhFRCqBSpTZ/g48iPNls8WxZ7nLgJZXfLSyWRGRqqOSBNufgO7Aacpv4FN5cAHqAF8B7Ut8lJ7NiohULZWgG3kLcBuQRuUKtGDamwaEAVsvu/flRhoPHjxYgVZEpBJy8sx2C3A3cNbRDSkDtYH1QNcityqbFRGpupw4s/0J6EXVCLRg7uMeYEehT5XNiohUfU6a2f6OWYv4lKMbUg58gO1AS2WzIiLVhBMG2xzMYKJEKt8z2pJwITe3NePGdePf/55VaItGGouIVE1ujm7AxaYDe6magRbAyrlzidSu/WveJ8pmRUSqNifLbBMxC0JkVviVN26EJ56A/fuhc2eYOxeCgsrvemfPwk03QYsWymZFRKo6Jwq29u7jX7m6UnhX7/hxaNECPvgA+vaFF1+EzZth27byu2ZuLpw+3Rgfn71YLE7YwSAiImXGiUYjT8esDlV+gbZpU5gxA9q1g7p1oX9/OHcOPvsMbrgBHnkEPDxg8mTYuRN27Sq3puDqCr6+aVgsb5TfRURExCk4SbBNwlTvKf9pPosXw9q1kJwMP/9suosTEiA4OH8fT0+T6SYklHdrMoBXqajyfCIi4hhOEmxnYMrklb+nnoKAAKhXz3QZ79gB6ekm0y2obl04c6YiWpQNvFkRFxIREQdxgmCbAcyjourR+vvn/1y7tgm0Xl5w+nTh/U6fBm/vimhRDhBD5SiuICIiV8MJgu0CwLHTXW64wTyjtcvIgKQk83nFsACLKupiIiJSwRwcbG3A6zg6q3vwQfjlF1i61AyYmjLFDKJq3bqiWpABvEZFj8IWEZGK4eBguw047NgmAA0amEA7aRL4+sJ338GiCk80/wS+q+iLiohIBXDwPNuHMMXVldGZv3seBD51dENERKSMOTDYWjFl57Icc3mnVBMz/ckJHqWLiEiZceBv9d9wyqWZHcoN872IiEhV4sBg+4NjL++UXIAfHd0IEREpYw6Mdt8C6Y67vFNKx3wvIiJSlTgw2G5BA6MuZMN8LyIiUpVcNtg2bdoUd3d3jh8/XujzDh06YLFY2Lt3L1FRUbi7u+Pl5ZX3Ci6w2PCcOXNo3bo13t7eNGzYkHvv7cWZM4kAREVBdHTha+7dCxYL5BSzqFRUFLi7mxWevL2hbVuYOBFOnSq834IFpkyepyc88ACkpuZvS00182s9Pc0+CxaU3bHHjsGAAWbJR19fGDiw8LH9+0P9+vC3v5ltBVev2rHjV8LCwqhbty5NmjTh1Vdfzds2f/78Qt9x7dq1sVgs/Pij6Xru1atXoe3u7u7ceOONRX+JIiJSYUqU2TZr1oyFCxfmvY+Pj+fs2cJFA5577jnS09PzXjvPL8m0adMm/vGPf7Bw4ULOnDlDYmIi/fvfDtQoVcOfe86sXXzsGPzf/5lyeF27mtWfwBQRGD0aPvoIjhwxSzOOGZN//BNPmIB95AjMnw+PP55feKA0xwL062eWhdy/H44ehQkT8rdFR8PJk6YQQlKSOcfkyfnbBwzI5rbbbiA1NZVNmzYxa9Ys/ve//wEwcODAQt/xrFmzaN68OR07dgRgzZo1hbZ36dKFRx55pFTfs4iIlF6Jgm1kZCTz5s3Lex8TE8PgwYNLdIHt27cTEhJChw4dAKhXrx5DhtyCt3fZjET28IBOneB//4MTJ0zgBRME+/aF224zax+/+qoppXfmjAnIS5eaz7y8IDQU7rvPBNfSHrt+PaSkwBtvmMy2Rg04f+uACbIPPAB16pjtDz5YOFDv3Wtj4MDOuLq60qJFC0JDQ0kopvyQ/d/BYrl4ucu9e/eyefPmEv87iYhI+SlRsL311ls5ffo0iYmJ5ObmsmjRIgYNGlSiC3Tu3Jl169bx8ssvs3XrVrKysoDMK2rkggVm+cRL8faGHj1M0Xe4uGxeixYmG92zx7zc3KBVq/ztwcGFM9urPXbbNrjuOhgyxHQVd+oEmzbl7/vEE7BypcluT540gbtXr/ztTz9dk3nzVpOdnc3u3bv59ttvueuuuy6633379vH1118XG0znzZtHWFgYTZs2vfQXJyIi5a7EA6Ts2e0XX3zB9ddfT+PGjQttnzFjBj4+PnmvIUOGABAWFsZnn31GXFwcvXv3pn79+owf/x65udYCx4KPT/7rwsA6YICpPXs5AQH5z1YvVTYvPd1klkVtK+2xBw6Y7PaOO+DwYXjmGbj/frA/8u7YEf76ywTi+vVNEfmCXdR9+rjy6adbqFWrFq1bt2b48OF06tTponu1B9NmzZoV+V3MmzePqKioYr4pERGpSFcUbBcsWMDcuXOLzKYmTJhAWlpa3ismJiZvW69evfj8889JTU1lxYoVzJ37FR988FeBYyEtLf9VksBalIMHTZ1auHTZvMuV1CvNsbVqQdOmMHy46UKOiIDAQNi61WwPDzdZ8Zkz5rgWLcDeSZCaCvfcc5aXXnqAc+fOkZKSwrp165g1a9ZF9zpv3ry8P2gutGXLFg4fPszDDz98iW9LREQqSomDbVBQEM2aNWP16tX069fv6i7m4sKdd95J9+438ssv1ssfcAXS02HDBggLM+8vLJv3xx+QlWUCXatWZqTzbwUWa9q5M7+kXmmObdfOjKQuqOD7HTvM4CtPTxO4H3sMVq/Ov46rKwwe3AM3NzeaNGlCREQEq+07nLd161YOHTpUbDCNiYmhX79+eHl5XeZbExGRinBF82znzJnDl19+iaenZ4mPWbFiBYsWLeLkyZPYbDa+//57Nm36lVtvLZsBUllZ8OOPZtCRry8MHWo+HzgQPv/cPMPNyICXXjKjhL29TaDr1898lpFhss4VKyAysvTHPvigeRYbEwO5ufDpp6ZruWtXs71TJ/jgA8jMNK/338/vNm/VCmw2WLBgG1arlcOHD/PJJ5/Q7oJ+9ZiYGB566CG8i6hun5mZyeLFi9WFLCLiTGyXERQUZPviiy8u+jw7O9sG2JKTk21Dhgyx1ahRw+bp6Zn3ql+/vs1ms9k2bdpk6969u61+/fo2Ly8v27XXXmt7/fWRNputrs1mwzZkCLZJk7DZbPmv5GRsgC0727z/+GNsbdrkbx8yBFuNGti8vLB5epptzz2H7Xw8z3vNn48tMBBb7drY7rsP24kT+dtOnMB2//1mW2Cg2besjv36a2xt25q23XSTeW/f9scf2Pr0wVavHjZfX2x3341tz5787Rs3etpuvvk6W506dWwNGza0jRgxwpaRkZH3vWdmZtrq1q1r27BhQ5H/XgsWLLBdc801NqvVerl/WhERqSAOqvqzG7gZLddYFE/M+sjXObohIiJSRhwUbFVer3geQAYq0iAiUnU46De6C9DaMZd2eq1RoBURqVoc+Fs9FLh45aPqzQKEOboRIiJSxhwYbEMATU0pzAu41dGNEBGRMuawYNu06XNs2JB90eebN5vlDkuiYMWgKzkuNhaaNCnZvhXLCtzk6EaIiEgZc2BmWwPIvejTsDDYvfvKz3a1x11ObCy4uJgFKLy8TJAOD4ft28v+WpADXFseJxYREQdy8Eiczo5vQgkEBJgVqs6cMYUGWrc2wX3jxqL3L64O76W5AH2oDN+HiIhcGYf+Zt++vS1t2tjyVn46d+7iLt7EROjWzRQouOEGU0qvKBce17SpKXDQrp0pFNC/vzl/Ud55B9q0MSs9XYrFYq4xZQqMGAHPP19428yZcO215rV3r/msYODt1s2sHgVmdalnnjEF5Js1g3ffdcNiWUrO1UVqERFxYg4NtvPnb2bdumtISjKl66ZOLbw9O9vUle3Z0xRh//e/zVKKJe0uXrwY1q41NWR//hnmzr14nylTzOebNuUHax8f2LLl0ufu1w/i4vKL1QMsXw7ffQe//nr5ts2eDWvWmLWS4+Jg+XLXEt2TiIhUPg4NtmPHjiUwcBL16nkxaRIsXFh4+7Ztpvv2hRdMPdnu3aFPn4v3K85TT5ku4Hr1TNDesSN/m80G48ebcnhffQUNGuRvS0szReEvJSDAnCMtLf+ziRPNtWrVunzbFi+Gv//dBHhfXy9eeOHxkt2UiIhUOg4NtoGBgcAAwEpQEBw6VHj7oUOmPJ1LgVYGBZlSeiXh75//c+3aJnDbpaWZIgATJ15cu7YkDh403cQ+PvmfBQaW/Hj7vRlWAgMjr7wRIiJSKTg02KakpGDWAh7M/v0uBAQU3h4QACkpYC1QjW//frigbv1V8fWFlSvNs2J7rdkrsWyZKQRfsABSwVJ69s/Pns3/7PDh/J8bNbI/I3YDhpCScvzKGyEiIpWCQ4PtzJkzOXDgAKmpI/jnP80gpoI6dzYZ6fTp5vltbKwpfRcRUTbX79YN5s83z1+///7y+9tsJqN95RUz0GnatOL3bdDA/FHw8cdmMNSHH0JSUv728HB4+204eNCNtLSRvP7666W+HxERcU4ODbYDBgygZ8+eNG9+Jy1aBBMdXbvQdnd3E1zXrDGjdseMgXnzzNSbstKjhwmEffuagUpg5tNu3py/z6FD+fNsO3WC+HgT+Hv2vPS5Z8+GN96A+vUhIQG6dMnfNnIk9OzpRrt2Fjp06Me9996Lm5sbrq4aKCUiUtU4qOpPUXKB9kAC4CRNKlcuQBtgB+DKmjVreOyxx9i3b59jmyUiImXOiVZQcAUWY0rMVX2Zme6sXj2WnBwbBw8e5JVXXuHBBx90dLNERKQcOFFmazft/CvjcjtWYp6cPfsst9++kl27dlGrVi169+7N22+/TZ06dRzdOBERKWNOGGzt3cm/Yhbmr2oKdx+LiEjV50TdyHauwDKgqmZ4dTD3p0ArIlJdOGGwBWgJfEnVq3frBXyFuT8REakunDTYAnQA1gK1L7djJVEbcz/tHdwOERGpaE74zFZERKRqceLMVkREpGpQsBURESlnCrYiIiLlTMFWRESknCnYioiIlLP/D8qn2vXqJQiQAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "from collections import defaultdict\n",
    "\n",
    "\n",
    "G = nx.Graph()\n",
    "query_node_labels = {}\n",
    "for k, v in query['message']['query_graph']['nodes'].items():\n",
    "    node_ids = v.get('ids', [])\n",
    "    node_categories = v.get('categories', [])\n",
    "    G.add_node(k, categories=', '.join(node_categories))\n",
    "    query_node_labels[k] = '\\n'.join([k] + node_ids + node_categories)\n",
    "    #G.add_node(k, color='red')\n",
    "\n",
    "query_edge_labels = {}\n",
    "q_node_id_keys = set(['object', 'subject'])\n",
    "for q_edge_id,edge_v in query['message']['query_graph']['edges'].items():\n",
    "    for predicate in edge_v['predicates']:\n",
    "        G.add_edge(edge_v['subject'], edge_v['object'], predicate=predicate)\n",
    "    #G.add_edge(edge_v['subject'], edge_v['object'], predicates=', '.join(edge_v['predicates']))\n",
    "    #G.add_edge(edge_v['subject'], edge_v['object'], predicates=[1,2,3])\n",
    "    #G.add_edge(edge_v['subject'], edge_v['object'])\n",
    "    query_edge_labels[(edge_v['subject'], edge_v['object'])] = '\\n'.join(edge_v['predicates'])\n",
    "    \n",
    "query_template_f = './bte_sleeve_query_template.graphml'\n",
    "print(f'Analyze this GraphML file in Cytoscape: {query_template_f}')\n",
    "nx.write_graphml(G, query_template_f)\n",
    "\n",
    "options = {\n",
    "    'node_color': 'yellow',\n",
    "    'node_size': 5000,\n",
    "    'edge_color': 'black',\n",
    "    'width': 3,\n",
    "}\n",
    "query_template_layout = nx.spring_layout(G)\n",
    "nx.draw(G, pos=query_template_layout, **options)\n",
    "nx.draw_networkx_labels(G, query_template_layout, labels=query_node_labels, font_size=12,\n",
    "                        font_color='k', font_family='sans-serif',\n",
    "                        font_weight='normal', alpha=None, bbox=None, horizontalalignment='center',\n",
    "                        verticalalignment='center', ax=None, clip_on=False)\n",
    "nx.draw_networkx_edge_labels(G, pos=query_template_layout,\n",
    "                             edge_labels=query_edge_labels,\n",
    "                             label_pos=0.5, \n",
    "                             font_size=10, font_color='k', font_family='sans-serif',\n",
    "                             font_weight='normal', alpha=None, bbox=None, horizontalalignment='center',\n",
    "                             verticalalignment='center', ax=None, rotate=True, clip_on=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "# NOTE: we're including categories specified in the query template\n",
    "# in the next cell, and in a cell further down, we're also including\n",
    "# any categories from the TRAPI results.\n",
    "#\n",
    "# TODO: Some categories are supersets of others. Should we handle\n",
    "# this systematically?\n",
    "\n",
    "curie_categories = set()\n",
    "for v in query[\"message\"][\"query_graph\"][\"nodes\"].values():\n",
    "    if \"categories\" in v:\n",
    "        for category in v[\"categories\"]:\n",
    "            curie_categories.add(category)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "XRfyhW_Is73C",
    "outputId": "76ae9c51-0587-4f1e-91ac-f8d728ce6878"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "all CURIE prefixes found:\n",
      "{'OMIM', 'PUBCHEM.COMPOUND', 'MEDDRA', 'HGNC', 'ENSEMBL', 'DOID', 'HP', 'MESH', 'CAS', 'MONDO', 'NCBIGene', 'SNOMEDCT', 'UMLS', 'CHEBI', 'UniProtKB', 'PR', 'ICD0', 'INCHIKEY', 'CHEMBL.COMPOUND', 'ICD10', 'ICD9', 'NCIT', 'UNII'}\n",
      "\n",
      "unified CURIE prefixes found:\n",
      "{'MESH', 'NCBIGene'}\n",
      "\n",
      "failed to unify 0 CURIEs\n"
     ]
    }
   ],
   "source": [
    "# for genes/gene products, chemicals and diseases\n",
    "preferred_prefixes = set([\"NCBIGene\", \"MESH\"])\n",
    "trapi_results_unified_curies = set()\n",
    "unification_failed_curies = set()\n",
    "unified_prefixes = set()\n",
    "all_prefixes = set()\n",
    "curie_to_name = dict()\n",
    "curie_to_categories = dict()\n",
    "curie_to_unified_curie = dict()\n",
    "for k, v in trapi_message[\"knowledge_graph\"][\"nodes\"].items():\n",
    "    name = v[\"name\"]\n",
    "    categories = v[\"categories\"]\n",
    "    curie_categories |= set(categories)\n",
    "\n",
    "    for a in v[\"attributes\"]:\n",
    "        if a[\"attribute_type_id\"] == \"biolink:xref\":\n",
    "            curies = a[\"value\"]\n",
    "\n",
    "            # k should always be one of the curies\n",
    "            if not k in curies:\n",
    "                raise Exception(f\"key {k} not in {curies}\")\n",
    "\n",
    "            unified_curie = None\n",
    "            intersecting_trapi_results_unified_curies = trapi_results_unified_curies.intersection(set(curies))\n",
    "            if len(intersecting_trapi_results_unified_curies) > 1:\n",
    "                raise Exception(f\"matching multiple: {k} to {list(intersecting_trapi_results_unified_curies)}\")\n",
    "            elif len(intersecting_trapi_results_unified_curies) == 1:\n",
    "                unified_curie = list(intersecting_trapi_results_unified_curies)[0]\n",
    "            else:\n",
    "                # get curie for preferred prefix. usually this is k, but not always.\n",
    "                for curie in curies:\n",
    "                    [prefix, identifier] = curie.split(\":\")\n",
    "                    if prefix in preferred_prefixes:\n",
    "                        unified_curie = curie\n",
    "                        trapi_results_unified_curies.add(unified_curie)\n",
    "                        break\n",
    "\n",
    "            if not unified_curie:\n",
    "                if k in curie_to_unified_curie:\n",
    "                    unified_curie = curie_to_unified_curie[k]\n",
    "                else:\n",
    "                    unification_failed_curies.add(k)\n",
    "                    break\n",
    "\n",
    "                #raise Exception(f\"failed to find a unified curie for {k} in {curies}\")\n",
    "\n",
    "            [unified_prefix, unified_identifier] = unified_curie.split(\":\")\n",
    "            unified_prefixes.add(unified_prefix)\n",
    "\n",
    "            for curie in curies:\n",
    "                [prefix, identifier] = curie.split(\":\")\n",
    "                all_prefixes.add(prefix)\n",
    "                if not curie in curie_to_unified_curie:\n",
    "                    curie_to_unified_curie[curie] = unified_curie\n",
    "                if not curie in curie_to_name:\n",
    "                    curie_to_name[curie] = name\n",
    "                    curie_to_categories[curie] = categories\n",
    "                elif curie_to_name[curie] != name:\n",
    "                    print(f\"curie {curie} has multiple primary names: {curie_to_name[curie]} and {name}\")\n",
    "                    #raise Exception(f\"curie{curie} has multiple names: {curie_to_name[curie]} and {name}\")\n",
    "\n",
    "\n",
    "#print(f'curie_categories: {curie_categories}')\n",
    "#print(\"\")\n",
    "#print(f\"curie_to_name key count: {len(curie_to_name.keys())}\")\n",
    "#print(f\"curie_to_unified_curie key count: {len(curie_to_unified_curie.keys())}\")\n",
    "#print(\"\")\n",
    "print(\"all CURIE prefixes found:\")\n",
    "print(all_prefixes)\n",
    "print(\"\")\n",
    "print(\"unified CURIE prefixes found:\")\n",
    "print(unified_prefixes)\n",
    "print(\"\")\n",
    "print(f\"failed to unify {len(unification_failed_curies)} CURIEs\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [],
   "source": [
    "columns = []\n",
    "q_node_id_keys = set([\"object\", \"subject\"])\n",
    "q_node_ids = []\n",
    "q_edge_ids = []\n",
    "for q_edge_id,edge_v in query[\"message\"][\"query_graph\"][\"edges\"].items():\n",
    "    q_edge_ids.append(q_edge_id)\n",
    "    q_node_id_found = False\n",
    "    for k,v in edge_v.items():\n",
    "        if (k in q_node_id_keys) and (type(v) is str):\n",
    "            if v not in columns:\n",
    "                q_node_ids.append(v)\n",
    "                columns.append(v)\n",
    "            if not q_node_id_found:\n",
    "                q_node_id_found = True\n",
    "                columns.append(q_edge_id)\n",
    "\n",
    "query_nodes_with_ids = set()\n",
    "for k, v in query['message']['query_graph']['nodes'].items():\n",
    "    node_ids = v.get('ids', [])\n",
    "    if node_ids:\n",
    "        query_nodes_with_ids.add(k)\n",
    "\n",
    "for q_node_id in q_node_ids:\n",
    "    columns.append(f\"{q_node_id}_original_curie\")\n",
    "    columns.append(f\"{q_node_id}_unified_curie\")\n",
    "\n",
    "columns.append(\"unified_curie_set\")\n",
    "\n",
    "trapi_result_columns = []\n",
    "for i in range(len(q_node_ids)):\n",
    "    trapi_result_columns.append(q_node_ids[i])\n",
    "    if i < len(q_edge_ids):\n",
    "        trapi_result_columns.append(q_edge_ids[i])\n",
    "\n",
    "unified_curie_columns = []\n",
    "for q_node_id in q_node_ids:\n",
    "    unified_curie_columns.append(f\"{q_node_id}_unified_curie\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
    "id": "Wjqcf5E4Yk_h"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "warning: predicate direction(s) may be switched\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>n0</th>\n",
       "      <th>e0</th>\n",
       "      <th>n1</th>\n",
       "      <th>e1</th>\n",
       "      <th>n2</th>\n",
       "      <th>n0_original_curie</th>\n",
       "      <th>n0_unified_curie</th>\n",
       "      <th>n1_original_curie</th>\n",
       "      <th>n1_unified_curie</th>\n",
       "      <th>n2_original_curie</th>\n",
       "      <th>n2_unified_curie</th>\n",
       "      <th>unified_curie_set</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Imatinib mesylate</td>\n",
       "      <td>response_affected_by</td>\n",
       "      <td>ABL1</td>\n",
       "      <td>affects_risk_for</td>\n",
       "      <td>leukemia</td>\n",
       "      <td>PUBCHEM.COMPOUND:123596</td>\n",
       "      <td>MESH:D000068877</td>\n",
       "      <td>NCBIGene:25</td>\n",
       "      <td>NCBIGene:25</td>\n",
       "      <td>MONDO:0005059</td>\n",
       "      <td>MESH:D007938</td>\n",
       "      <td>{MESH:D007938, NCBIGene:25, MESH:D000068877}</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Imatinib mesylate</td>\n",
       "      <td>response_affected_by</td>\n",
       "      <td>ABL1</td>\n",
       "      <td>gene_associated_with_condition</td>\n",
       "      <td>leukemia</td>\n",
       "      <td>PUBCHEM.COMPOUND:123596</td>\n",
       "      <td>MESH:D000068877</td>\n",
       "      <td>NCBIGene:25</td>\n",
       "      <td>NCBIGene:25</td>\n",
       "      <td>MONDO:0005059</td>\n",
       "      <td>MESH:D007938</td>\n",
       "      <td>{MESH:D007938, NCBIGene:25, MESH:D000068877}</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Imatinib mesylate</td>\n",
       "      <td>response_affected_by</td>\n",
       "      <td>ABL1</td>\n",
       "      <td>contributes_to</td>\n",
       "      <td>leukemia</td>\n",
       "      <td>PUBCHEM.COMPOUND:123596</td>\n",
       "      <td>MESH:D000068877</td>\n",
       "      <td>NCBIGene:25</td>\n",
       "      <td>NCBIGene:25</td>\n",
       "      <td>MONDO:0005059</td>\n",
       "      <td>MESH:D007938</td>\n",
       "      <td>{MESH:D007938, NCBIGene:25, MESH:D000068877}</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Imatinib mesylate</td>\n",
       "      <td>response_affected_by</td>\n",
       "      <td>ABL1</td>\n",
       "      <td>related_to</td>\n",
       "      <td>leukemia</td>\n",
       "      <td>PUBCHEM.COMPOUND:123596</td>\n",
       "      <td>MESH:D000068877</td>\n",
       "      <td>NCBIGene:25</td>\n",
       "      <td>NCBIGene:25</td>\n",
       "      <td>MONDO:0005059</td>\n",
       "      <td>MESH:D007938</td>\n",
       "      <td>{MESH:D007938, NCBIGene:25, MESH:D000068877}</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Imatinib mesylate</td>\n",
       "      <td>response_affected_by</td>\n",
       "      <td>ABL1</td>\n",
       "      <td>correlated_with</td>\n",
       "      <td>leukemia</td>\n",
       "      <td>PUBCHEM.COMPOUND:123596</td>\n",
       "      <td>MESH:D000068877</td>\n",
       "      <td>NCBIGene:25</td>\n",
       "      <td>NCBIGene:25</td>\n",
       "      <td>MONDO:0005059</td>\n",
       "      <td>MESH:D007938</td>\n",
       "      <td>{MESH:D007938, NCBIGene:25, MESH:D000068877}</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>74</th>\n",
       "      <td>Imatinib mesylate</td>\n",
       "      <td>response_decreased_by</td>\n",
       "      <td>CDK6</td>\n",
       "      <td>gene_associated_with_condition</td>\n",
       "      <td>leukemia</td>\n",
       "      <td>PUBCHEM.COMPOUND:123596</td>\n",
       "      <td>MESH:D000068877</td>\n",
       "      <td>NCBIGene:1021</td>\n",
       "      <td>NCBIGene:1021</td>\n",
       "      <td>MONDO:0005059</td>\n",
       "      <td>MESH:D007938</td>\n",
       "      <td>{MESH:D007938, NCBIGene:1021, MESH:D000068877}</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75</th>\n",
       "      <td>Imatinib mesylate</td>\n",
       "      <td>increases_activity_of</td>\n",
       "      <td>JUN</td>\n",
       "      <td>gene_associated_with_condition</td>\n",
       "      <td>leukemia</td>\n",
       "      <td>PUBCHEM.COMPOUND:123596</td>\n",
       "      <td>MESH:D000068877</td>\n",
       "      <td>NCBIGene:3725</td>\n",
       "      <td>NCBIGene:3725</td>\n",
       "      <td>MONDO:0005059</td>\n",
       "      <td>MESH:D007938</td>\n",
       "      <td>{NCBIGene:3725, MESH:D007938, MESH:D000068877}</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>76</th>\n",
       "      <td>Imatinib mesylate</td>\n",
       "      <td>increases_activity_of</td>\n",
       "      <td>JUN</td>\n",
       "      <td>contributes_to</td>\n",
       "      <td>leukemia</td>\n",
       "      <td>PUBCHEM.COMPOUND:123596</td>\n",
       "      <td>MESH:D000068877</td>\n",
       "      <td>NCBIGene:3725</td>\n",
       "      <td>NCBIGene:3725</td>\n",
       "      <td>MONDO:0005059</td>\n",
       "      <td>MESH:D007938</td>\n",
       "      <td>{NCBIGene:3725, MESH:D007938, MESH:D000068877}</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>77</th>\n",
       "      <td>Imatinib mesylate</td>\n",
       "      <td>increases_activity_of</td>\n",
       "      <td>CASP8</td>\n",
       "      <td>correlated_with</td>\n",
       "      <td>leukemia</td>\n",
       "      <td>PUBCHEM.COMPOUND:123596</td>\n",
       "      <td>MESH:D000068877</td>\n",
       "      <td>NCBIGene:841</td>\n",
       "      <td>NCBIGene:841</td>\n",
       "      <td>MONDO:0005059</td>\n",
       "      <td>MESH:D007938</td>\n",
       "      <td>{MESH:D007938, NCBIGene:841, MESH:D000068877}</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>78</th>\n",
       "      <td>Imatinib mesylate</td>\n",
       "      <td>increases_activity_of</td>\n",
       "      <td>CASP8</td>\n",
       "      <td>gene_associated_with_condition</td>\n",
       "      <td>leukemia</td>\n",
       "      <td>PUBCHEM.COMPOUND:123596</td>\n",
       "      <td>MESH:D000068877</td>\n",
       "      <td>NCBIGene:841</td>\n",
       "      <td>NCBIGene:841</td>\n",
       "      <td>MONDO:0005059</td>\n",
       "      <td>MESH:D007938</td>\n",
       "      <td>{MESH:D007938, NCBIGene:841, MESH:D000068877}</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>79 rows × 12 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                   n0                     e0     n1  \\\n",
       "0   Imatinib mesylate   response_affected_by   ABL1   \n",
       "1   Imatinib mesylate   response_affected_by   ABL1   \n",
       "2   Imatinib mesylate   response_affected_by   ABL1   \n",
       "3   Imatinib mesylate   response_affected_by   ABL1   \n",
       "4   Imatinib mesylate   response_affected_by   ABL1   \n",
       "..                ...                    ...    ...   \n",
       "74  Imatinib mesylate  response_decreased_by   CDK6   \n",
       "75  Imatinib mesylate  increases_activity_of    JUN   \n",
       "76  Imatinib mesylate  increases_activity_of    JUN   \n",
       "77  Imatinib mesylate  increases_activity_of  CASP8   \n",
       "78  Imatinib mesylate  increases_activity_of  CASP8   \n",
       "\n",
       "                                e1        n2        n0_original_curie  \\\n",
       "0                 affects_risk_for  leukemia  PUBCHEM.COMPOUND:123596   \n",
       "1   gene_associated_with_condition  leukemia  PUBCHEM.COMPOUND:123596   \n",
       "2                   contributes_to  leukemia  PUBCHEM.COMPOUND:123596   \n",
       "3                       related_to  leukemia  PUBCHEM.COMPOUND:123596   \n",
       "4                  correlated_with  leukemia  PUBCHEM.COMPOUND:123596   \n",
       "..                             ...       ...                      ...   \n",
       "74  gene_associated_with_condition  leukemia  PUBCHEM.COMPOUND:123596   \n",
       "75  gene_associated_with_condition  leukemia  PUBCHEM.COMPOUND:123596   \n",
       "76                  contributes_to  leukemia  PUBCHEM.COMPOUND:123596   \n",
       "77                 correlated_with  leukemia  PUBCHEM.COMPOUND:123596   \n",
       "78  gene_associated_with_condition  leukemia  PUBCHEM.COMPOUND:123596   \n",
       "\n",
       "   n0_unified_curie n1_original_curie n1_unified_curie n2_original_curie  \\\n",
       "0   MESH:D000068877       NCBIGene:25      NCBIGene:25     MONDO:0005059   \n",
       "1   MESH:D000068877       NCBIGene:25      NCBIGene:25     MONDO:0005059   \n",
       "2   MESH:D000068877       NCBIGene:25      NCBIGene:25     MONDO:0005059   \n",
       "3   MESH:D000068877       NCBIGene:25      NCBIGene:25     MONDO:0005059   \n",
       "4   MESH:D000068877       NCBIGene:25      NCBIGene:25     MONDO:0005059   \n",
       "..              ...               ...              ...               ...   \n",
       "74  MESH:D000068877     NCBIGene:1021    NCBIGene:1021     MONDO:0005059   \n",
       "75  MESH:D000068877     NCBIGene:3725    NCBIGene:3725     MONDO:0005059   \n",
       "76  MESH:D000068877     NCBIGene:3725    NCBIGene:3725     MONDO:0005059   \n",
       "77  MESH:D000068877      NCBIGene:841     NCBIGene:841     MONDO:0005059   \n",
       "78  MESH:D000068877      NCBIGene:841     NCBIGene:841     MONDO:0005059   \n",
       "\n",
       "   n2_unified_curie                               unified_curie_set  \n",
       "0      MESH:D007938    {MESH:D007938, NCBIGene:25, MESH:D000068877}  \n",
       "1      MESH:D007938    {MESH:D007938, NCBIGene:25, MESH:D000068877}  \n",
       "2      MESH:D007938    {MESH:D007938, NCBIGene:25, MESH:D000068877}  \n",
       "3      MESH:D007938    {MESH:D007938, NCBIGene:25, MESH:D000068877}  \n",
       "4      MESH:D007938    {MESH:D007938, NCBIGene:25, MESH:D000068877}  \n",
       "..              ...                                             ...  \n",
       "74     MESH:D007938  {MESH:D007938, NCBIGene:1021, MESH:D000068877}  \n",
       "75     MESH:D007938  {NCBIGene:3725, MESH:D007938, MESH:D000068877}  \n",
       "76     MESH:D007938  {NCBIGene:3725, MESH:D007938, MESH:D000068877}  \n",
       "77     MESH:D007938   {MESH:D007938, NCBIGene:841, MESH:D000068877}  \n",
       "78     MESH:D007938   {MESH:D007938, NCBIGene:841, MESH:D000068877}  \n",
       "\n",
       "[79 rows x 12 columns]"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "trapi_results = trapi_message[\"results\"]\n",
    "\n",
    "result_row_data = []\n",
    "for trapi_result in trapi_results:\n",
    "    curie_to_qnode_ids = dict()\n",
    "    for qnode_id, entries in trapi_result[\"node_bindings\"].items():\n",
    "        for entry in entries:\n",
    "            curie = entry[\"id\"]\n",
    "            if curie not in curie_to_qnode_ids:\n",
    "                curie_to_qnode_ids[curie] = []\n",
    "            curie_to_qnode_ids[curie].append(qnode_id)\n",
    "\n",
    "    row_data_template = dict()\n",
    "    q_edge_id_to_predicates = dict()\n",
    "    trapi_result_curie_set = set()\n",
    "    for qedge_id, entries in trapi_result[\"edge_bindings\"].items():\n",
    "        for entry in entries:\n",
    "            curie = entry[\"id\"]\n",
    "            kg_entry = trapi_message[\"knowledge_graph\"][\"edges\"][curie]\n",
    "            subject_curie = kg_entry[\"subject\"]\n",
    "            object_curie = kg_entry[\"object\"]\n",
    "            predicate_curie = kg_entry[\"predicate\"]\n",
    "            [predicate_prefix, predicate_identifier] = predicate_curie.split(\":\")\n",
    "\n",
    "            if qedge_id not in q_edge_id_to_predicates:\n",
    "                q_edge_id_to_predicates[qedge_id] = set()\n",
    "            q_edge_id_to_predicates[qedge_id].add(predicate_identifier)\n",
    "\n",
    "            for curie in [subject_curie, object_curie]:\n",
    "                for qnode_id in curie_to_qnode_ids[curie]:\n",
    "                    if curie in curie_to_unified_curie:\n",
    "                        unified_curie = curie_to_unified_curie[curie]\n",
    "                    else:\n",
    "                        break\n",
    "\n",
    "                    name = curie_to_name[curie]\n",
    "                    row_data_template[qnode_id] = name\n",
    "\n",
    "                    trapi_result_curie_set.add(unified_curie)\n",
    "                    row_data_template[qnode_id + \"_original_curie\"] = curie\n",
    "                    row_data_template[qnode_id + \"_unified_curie\"] = unified_curie\n",
    "\n",
    "    if len(trapi_result_curie_set) != len(q_node_ids):\n",
    "        #print(f'skipping {list(curie_to_qnode_ids.keys())}')\n",
    "        continue\n",
    "\n",
    "    row_data_template[\"unified_curie_set\"] = trapi_result_curie_set\n",
    "    q_edge_ids_processed = set()\n",
    "    row_datas = [row_data_template]\n",
    "    for q_edge_id,predicates in q_edge_id_to_predicates.items():\n",
    "        next_row_datas = []\n",
    "        for row_data in row_datas:\n",
    "            for predicate in predicates:\n",
    "                next_row_data = deepcopy(row_data)\n",
    "                next_row_data[q_edge_id] = predicate\n",
    "                next_row_datas.append(\n",
    "                    next_row_data \n",
    "                )\n",
    "        row_datas = next_row_datas\n",
    "    result_row_data += row_datas\n",
    "\n",
    "print(\"warning: predicate direction(s) may be switched\")\n",
    "trapi_results_df = pd.DataFrame.from_records(result_row_data, columns=columns)\n",
    "trapi_results_df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'MESH:D000068877', 'MESH:D007938'}"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "user_specified_ids = set()\n",
    "for query_node in query_nodes_with_ids:\n",
    "    user_specified_ids.update(trapi_results_df[f'{query_node}_unified_curie'].tolist())\n",
    "user_specified_ids"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Unique CURIE count per query node:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "n0: 1\n",
      "n1: 20\n",
      "n2: 1\n"
     ]
    }
   ],
   "source": [
    "for q_node_id in q_node_ids:\n",
    "    print(f'{q_node_id}: {len(set(trapi_results_df[q_node_id]))}')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Total results count (excluding any that couldn't be unified):"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "20"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "len(set(trapi_results_df[\"unified_curie_set\"].map(sorted).map(tuple)))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "That's too too high for a researcher to efficiently go through manually. Let's try filtering to make that more manageable."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "uCW1x4PxnjYJ",
    "tags": []
   },
   "source": [
    "## Get PFOCR Data\n",
    "\n",
    "We could get it from the API, but for now, we'll just go ahead and download the entire JSON file we gave to BTE."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "OyJJUHbepKp2",
    "outputId": "7237b5f0-8740-47cb-9e3d-b03ea02bdcab"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "status_code: 200\n"
     ]
    }
   ],
   "source": [
    "pfocr_url = \"https://www.dropbox.com/s/1f14t5zaseocyg6/bte_chemicals_diseases_genes.ndjson?dl=1\"\n",
    "pfocr_request = requests.get(pfocr_url)\n",
    "print(f\"status_code: {pfocr_request.status_code}\")\n",
    "if pfocr_request.status_code != 200:\n",
    "    print(pfocr_request.text)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {
    "id": "8fUaenkS0gg1"
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>figure_id</th>\n",
       "      <th>chemical_curie_set</th>\n",
       "      <th>disease_curie_set</th>\n",
       "      <th>gene_curie_set</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>PMC5732092__cshperspect-CYT-028522_F2.jpg</td>\n",
       "      <td>{}</td>\n",
       "      <td>{}</td>\n",
       "      <td>{NCBIGene:2921, NCBIGene:5971, NCBIGene:5602, ...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>PMC5793760__cshperspect-TGF-022210_F4.jpg</td>\n",
       "      <td>{}</td>\n",
       "      <td>{}</td>\n",
       "      <td>{NCBIGene:353500, NCBIGene:650, NCBIGene:6498,...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>PMC5793761__cshperspect-TGF-031989_F1.jpg</td>\n",
       "      <td>{}</td>\n",
       "      <td>{}</td>\n",
       "      <td>{NCBIGene:353500, NCBIGene:650, NCBIGene:56963...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>PMC5830892__cshperspect-CEL-027961_F2.jpg</td>\n",
       "      <td>{}</td>\n",
       "      <td>{}</td>\n",
       "      <td>{NCBIGene:59, NCBIGene:58, NCBIGene:55249, NCB...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>PMC5830900__cshperspect-TGF-031997_F1.jpg</td>\n",
       "      <td>{MESH:D011374}</td>\n",
       "      <td>{}</td>\n",
       "      <td>{NCBIGene:353500, NCBIGene:650, NCBIGene:6498,...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>77714</th>\n",
       "      <td>PMC2804790__253_2009_2262_Fig1_HTML.jpg</td>\n",
       "      <td>{MESH:D018698, MESH:D014631, MESH:C018475, MES...</td>\n",
       "      <td>{}</td>\n",
       "      <td>{NCBIGene:137872, NCBIGene:130, NCBIGene:124, ...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>77715</th>\n",
       "      <td>PMC6332787__thnov09p0126g006.jpg</td>\n",
       "      <td>{}</td>\n",
       "      <td>{}</td>\n",
       "      <td>{NCBIGene:5133, NCBIGene:8295, NCBIGene:6387, ...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>77716</th>\n",
       "      <td>PMC5807036__IJO-52-03-0787-g01.jpg</td>\n",
       "      <td>{}</td>\n",
       "      <td>{}</td>\n",
       "      <td>{NCBIGene:3643, NCBIGene:285, NCBIGene:4254, N...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>77717</th>\n",
       "      <td>PMC6770832__cancers-11-01236-g005.jpg</td>\n",
       "      <td>{}</td>\n",
       "      <td>{}</td>\n",
       "      <td>{NCBIGene:5594, NCBIGene:4087, NCBIGene:6300, ...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>77718</th>\n",
       "      <td>PMC4609065__12885_2015_1721_Fig5_HTML.jpg</td>\n",
       "      <td>{MESH:C029269, MESH:D014443}</td>\n",
       "      <td>{}</td>\n",
       "      <td>{NCBIGene:2065, NCBIGene:306, NCBIGene:3845, N...</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>77719 rows × 4 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                                       figure_id  \\\n",
       "0      PMC5732092__cshperspect-CYT-028522_F2.jpg   \n",
       "1      PMC5793760__cshperspect-TGF-022210_F4.jpg   \n",
       "2      PMC5793761__cshperspect-TGF-031989_F1.jpg   \n",
       "3      PMC5830892__cshperspect-CEL-027961_F2.jpg   \n",
       "4      PMC5830900__cshperspect-TGF-031997_F1.jpg   \n",
       "...                                          ...   \n",
       "77714    PMC2804790__253_2009_2262_Fig1_HTML.jpg   \n",
       "77715           PMC6332787__thnov09p0126g006.jpg   \n",
       "77716         PMC5807036__IJO-52-03-0787-g01.jpg   \n",
       "77717      PMC6770832__cancers-11-01236-g005.jpg   \n",
       "77718  PMC4609065__12885_2015_1721_Fig5_HTML.jpg   \n",
       "\n",
       "                                      chemical_curie_set disease_curie_set  \\\n",
       "0                                                     {}                {}   \n",
       "1                                                     {}                {}   \n",
       "2                                                     {}                {}   \n",
       "3                                                     {}                {}   \n",
       "4                                         {MESH:D011374}                {}   \n",
       "...                                                  ...               ...   \n",
       "77714  {MESH:D018698, MESH:D014631, MESH:C018475, MES...                {}   \n",
       "77715                                                 {}                {}   \n",
       "77716                                                 {}                {}   \n",
       "77717                                                 {}                {}   \n",
       "77718                       {MESH:C029269, MESH:D014443}                {}   \n",
       "\n",
       "                                          gene_curie_set  \n",
       "0      {NCBIGene:2921, NCBIGene:5971, NCBIGene:5602, ...  \n",
       "1      {NCBIGene:353500, NCBIGene:650, NCBIGene:6498,...  \n",
       "2      {NCBIGene:353500, NCBIGene:650, NCBIGene:56963...  \n",
       "3      {NCBIGene:59, NCBIGene:58, NCBIGene:55249, NCB...  \n",
       "4      {NCBIGene:353500, NCBIGene:650, NCBIGene:6498,...  \n",
       "...                                                  ...  \n",
       "77714  {NCBIGene:137872, NCBIGene:130, NCBIGene:124, ...  \n",
       "77715  {NCBIGene:5133, NCBIGene:8295, NCBIGene:6387, ...  \n",
       "77716  {NCBIGene:3643, NCBIGene:285, NCBIGene:4254, N...  \n",
       "77717  {NCBIGene:5594, NCBIGene:4087, NCBIGene:6300, ...  \n",
       "77718  {NCBIGene:2065, NCBIGene:306, NCBIGene:3845, N...  \n",
       "\n",
       "[77719 rows x 4 columns]"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "figures_df_data = []\n",
    "curies_to_figure_ids = {}\n",
    "figure_id_to_curies = {}\n",
    "figure_id_to_pfocr_result = {}\n",
    "for line in pfocr_request.text.splitlines():\n",
    "    pfocr_result = json.loads(line)\n",
    "    figure_id = pfocr_result[\"_id\"]\n",
    "\n",
    "    chemical_curies = set()\n",
    "    disease_curies = set()\n",
    "    gene_curies = set()\n",
    "    for identifier in pfocr_result[\"associatedWith\"][\"mentions\"][\"chemicals\"][\"mesh\"]:\n",
    "        curie = \"MESH:\" + identifier\n",
    "        chemical_curies.add(curie)\n",
    "    for identifier in pfocr_result[\"associatedWith\"][\"mentions\"][\"diseases\"][\"mesh\"]:\n",
    "        curie = \"MESH:\" + identifier\n",
    "        disease_curies.add(curie)\n",
    "    for identifier in pfocr_result[\"associatedWith\"][\"mentions\"][\"genes\"][\"ncbigene\"]:\n",
    "        curie = \"NCBIGene:\" + identifier\n",
    "        gene_curies.add(curie)\n",
    "\n",
    "    figure_id_to_pfocr_result[figure_id] = pfocr_result\n",
    "    figure_id_to_curies[figure_id] = curies\n",
    "\n",
    "    figures_df_data.append({\n",
    "        \"figure_id\": figure_id,\n",
    "        \"chemical_curie_set\": chemical_curies,\n",
    "        \"disease_curie_set\": disease_curies,\n",
    "        \"gene_curie_set\": gene_curies,\n",
    "    })\n",
    "\n",
    "figures_df = pd.DataFrame.from_records(figures_df_data)\n",
    "figures_df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [],
   "source": [
    "biolink_category_to_pfocr_category = {\n",
    "    \"biolink:Drug\": \"chemical\",\n",
    "    \"biolink:ChemicalEntity\": \"chemical\",\n",
    "    \"biolink:SmallMolecule\": \"chemical\",\n",
    "    \"biolink:Disease\": \"disease\",\n",
    "    \"biolink:DiseaseOrPhenotypicFeature\": \"disease\",\n",
    "    \"biolink:Gene\": \"gene\",\n",
    "    \"biolink:GeneProduct\": \"gene\",\n",
    "    \"biolink:Protein\": \"gene\",\n",
    "}\n",
    "\n",
    "# limit to categories returned from query\n",
    "relevant_biolink_categories = set(biolink_category_to_pfocr_category.keys()).intersection(curie_categories)\n",
    "relevant_pfocr_categories = set([\n",
    "    biolink_category_to_pfocr_category[\n",
    "        biolink_category\n",
    "    ] for biolink_category in relevant_biolink_categories\n",
    "])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>figure_url</th>\n",
       "      <th>figure_title</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>figure_id</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>PMC5732092__cshperspect-CYT-028522_F2.jpg</th>\n",
       "      <td>https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5...</td>\n",
       "      <td>Interleukin (IL)-17RA/RC signaling pathways</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>PMC5793760__cshperspect-TGF-022210_F4.jpg</th>\n",
       "      <td>https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5...</td>\n",
       "      <td>TGFB signaling pathways</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>PMC5793761__cshperspect-TGF-031989_F1.jpg</th>\n",
       "      <td>https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5...</td>\n",
       "      <td>Bone morphogenetic protein (BMP) signaling pat...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>PMC5830892__cshperspect-CEL-027961_F2.jpg</th>\n",
       "      <td>https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5...</td>\n",
       "      <td>FERM-binding partners of Crumbs3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>PMC5830900__cshperspect-TGF-031997_F1.jpg</th>\n",
       "      <td>https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5...</td>\n",
       "      <td>Role of the TGFB family in mammary gland devel...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>PMC2804790__253_2009_2262_Fig1_HTML.jpg</th>\n",
       "      <td>https://www.ncbi.nlm.nih.gov/pmc/articles/PMC2...</td>\n",
       "      <td>Ehrlich degradation pathway from amino acid to...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>PMC6332787__thnov09p0126g006.jpg</th>\n",
       "      <td>https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6...</td>\n",
       "      <td>Illustration of the mechanisms underlying LPD ...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>PMC5807036__IJO-52-03-0787-g01.jpg</th>\n",
       "      <td>https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5...</td>\n",
       "      <td>Tumorigenic proteins significantly upregulated...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>PMC6770832__cancers-11-01236-g005.jpg</th>\n",
       "      <td>https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6...</td>\n",
       "      <td>Simplified TGFB pathway leading to p21 expression</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>PMC4609065__12885_2015_1721_Fig5_HTML.jpg</th>\n",
       "      <td>https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4...</td>\n",
       "      <td>EGFR and ANXA3 to fluoropyrimidine resistance ...</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>77719 rows × 2 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                                                                                  figure_url  \\\n",
       "figure_id                                                                                      \n",
       "PMC5732092__cshperspect-CYT-028522_F2.jpg  https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5...   \n",
       "PMC5793760__cshperspect-TGF-022210_F4.jpg  https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5...   \n",
       "PMC5793761__cshperspect-TGF-031989_F1.jpg  https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5...   \n",
       "PMC5830892__cshperspect-CEL-027961_F2.jpg  https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5...   \n",
       "PMC5830900__cshperspect-TGF-031997_F1.jpg  https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5...   \n",
       "...                                                                                      ...   \n",
       "PMC2804790__253_2009_2262_Fig1_HTML.jpg    https://www.ncbi.nlm.nih.gov/pmc/articles/PMC2...   \n",
       "PMC6332787__thnov09p0126g006.jpg           https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6...   \n",
       "PMC5807036__IJO-52-03-0787-g01.jpg         https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5...   \n",
       "PMC6770832__cancers-11-01236-g005.jpg      https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6...   \n",
       "PMC4609065__12885_2015_1721_Fig5_HTML.jpg  https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4...   \n",
       "\n",
       "                                                                                figure_title  \n",
       "figure_id                                                                                     \n",
       "PMC5732092__cshperspect-CYT-028522_F2.jpg        Interleukin (IL)-17RA/RC signaling pathways  \n",
       "PMC5793760__cshperspect-TGF-022210_F4.jpg                            TGFB signaling pathways  \n",
       "PMC5793761__cshperspect-TGF-031989_F1.jpg  Bone morphogenetic protein (BMP) signaling pat...  \n",
       "PMC5830892__cshperspect-CEL-027961_F2.jpg                   FERM-binding partners of Crumbs3  \n",
       "PMC5830900__cshperspect-TGF-031997_F1.jpg  Role of the TGFB family in mammary gland devel...  \n",
       "...                                                                                      ...  \n",
       "PMC2804790__253_2009_2262_Fig1_HTML.jpg    Ehrlich degradation pathway from amino acid to...  \n",
       "PMC6332787__thnov09p0126g006.jpg           Illustration of the mechanisms underlying LPD ...  \n",
       "PMC5807036__IJO-52-03-0787-g01.jpg         Tumorigenic proteins significantly upregulated...  \n",
       "PMC6770832__cancers-11-01236-g005.jpg      Simplified TGFB pathway leading to p21 expression  \n",
       "PMC4609065__12885_2015_1721_Fig5_HTML.jpg  EGFR and ANXA3 to fluoropyrimidine resistance ...  \n",
       "\n",
       "[77719 rows x 2 columns]"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "figure_metadata_records = []\n",
    "figure_curie_df_data = []\n",
    "for line in pfocr_request.text.splitlines():\n",
    "    pfocr_result = json.loads(line)\n",
    "    figure_id = pfocr_result[\"_id\"]\n",
    "\n",
    "    chemical_curies = set()\n",
    "    disease_curies = set()\n",
    "    gene_curies = set()\n",
    "    for identifier in pfocr_result[\"associatedWith\"][\"mentions\"][\"chemicals\"][\"mesh\"]:\n",
    "        curie = \"MESH:\" + identifier\n",
    "        chemical_curies.add(curie)\n",
    "        figure_curie_df_data.append({\n",
    "            \"figure_id\": figure_id,\n",
    "            \"curie\": curie,\n",
    "            \"category\": \"chemical\",\n",
    "        })\n",
    "    for identifier in pfocr_result[\"associatedWith\"][\"mentions\"][\"diseases\"][\"mesh\"]:\n",
    "        curie = \"MESH:\" + identifier\n",
    "        disease_curies.add(curie)\n",
    "        figure_curie_df_data.append({\n",
    "            \"figure_id\": figure_id,\n",
    "            \"curie\": curie,\n",
    "            \"category\": \"disease\",\n",
    "        })\n",
    "    for identifier in pfocr_result[\"associatedWith\"][\"mentions\"][\"genes\"][\"ncbigene\"]:\n",
    "        curie = \"NCBIGene:\" + identifier\n",
    "        gene_curies.add(curie)\n",
    "        figure_curie_df_data.append({\n",
    "            \"figure_id\": figure_id,\n",
    "            \"curie\": curie,\n",
    "            \"category\": \"gene\",\n",
    "        })\n",
    "\n",
    "    figure_id_to_pfocr_result[figure_id] = pfocr_result\n",
    "    figure_id_to_curies[figure_id] = curies\n",
    "\n",
    "    figure_metadata_records.append({\n",
    "        \"figure_id\": figure_id,\n",
    "        \"figure_url\": pfocr_result[\"associatedWith\"][\"figureUrl\"],\n",
    "        \"figure_title\": pfocr_result[\"associatedWith\"][\"title\"],\n",
    "    })\n",
    "\n",
    "figure_metadata_df = pd.DataFrame.from_records(figure_metadata_records, index='figure_id')\n",
    "figure_metadata_df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>figure_id</th>\n",
       "      <th>curie</th>\n",
       "      <th>category</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>PMC5732092__cshperspect-CYT-028522_F2.jpg</td>\n",
       "      <td>NCBIGene:5601</td>\n",
       "      <td>gene</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>PMC5732092__cshperspect-CYT-028522_F2.jpg</td>\n",
       "      <td>NCBIGene:5595</td>\n",
       "      <td>gene</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>PMC5732092__cshperspect-CYT-028522_F2.jpg</td>\n",
       "      <td>NCBIGene:1051</td>\n",
       "      <td>gene</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>PMC5732092__cshperspect-CYT-028522_F2.jpg</td>\n",
       "      <td>NCBIGene:10131</td>\n",
       "      <td>gene</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>PMC5732092__cshperspect-CYT-028522_F2.jpg</td>\n",
       "      <td>NCBIGene:23765</td>\n",
       "      <td>gene</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1665596</th>\n",
       "      <td>PMC4609065__12885_2015_1721_Fig5_HTML.jpg</td>\n",
       "      <td>NCBIGene:1956</td>\n",
       "      <td>gene</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1665597</th>\n",
       "      <td>PMC4609065__12885_2015_1721_Fig5_HTML.jpg</td>\n",
       "      <td>NCBIGene:4893</td>\n",
       "      <td>gene</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1665598</th>\n",
       "      <td>PMC4609065__12885_2015_1721_Fig5_HTML.jpg</td>\n",
       "      <td>NCBIGene:2064</td>\n",
       "      <td>gene</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1665599</th>\n",
       "      <td>PMC4609065__12885_2015_1721_Fig5_HTML.jpg</td>\n",
       "      <td>NCBIGene:3845</td>\n",
       "      <td>gene</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1665600</th>\n",
       "      <td>PMC4609065__12885_2015_1721_Fig5_HTML.jpg</td>\n",
       "      <td>NCBIGene:306</td>\n",
       "      <td>gene</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>1665601 rows × 3 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                                         figure_id           curie category\n",
       "0        PMC5732092__cshperspect-CYT-028522_F2.jpg   NCBIGene:5601     gene\n",
       "1        PMC5732092__cshperspect-CYT-028522_F2.jpg   NCBIGene:5595     gene\n",
       "2        PMC5732092__cshperspect-CYT-028522_F2.jpg   NCBIGene:1051     gene\n",
       "3        PMC5732092__cshperspect-CYT-028522_F2.jpg  NCBIGene:10131     gene\n",
       "4        PMC5732092__cshperspect-CYT-028522_F2.jpg  NCBIGene:23765     gene\n",
       "...                                            ...             ...      ...\n",
       "1665596  PMC4609065__12885_2015_1721_Fig5_HTML.jpg   NCBIGene:1956     gene\n",
       "1665597  PMC4609065__12885_2015_1721_Fig5_HTML.jpg   NCBIGene:4893     gene\n",
       "1665598  PMC4609065__12885_2015_1721_Fig5_HTML.jpg   NCBIGene:2064     gene\n",
       "1665599  PMC4609065__12885_2015_1721_Fig5_HTML.jpg   NCBIGene:3845     gene\n",
       "1665600  PMC4609065__12885_2015_1721_Fig5_HTML.jpg    NCBIGene:306     gene\n",
       "\n",
       "[1665601 rows x 3 columns]"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "figure_curie_df = pd.DataFrame.from_records(figure_curie_df_data)\n",
    "figure_curie_df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index(['figure_id', 'curie', 'category'], dtype='object')"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "figure_curie_df.columns"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Clustering"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "tags": []
   },
   "source": [
    "### Identify Cluster Figures\n",
    "\n",
    "See details in write-up at the top."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "number of figures w/ user-specified CURIEs: 62\n",
      "all_relevant_curie_count: 1037\n",
      "i: 0\n",
      "i: 1\n",
      "i: 2\n",
      "i: 3\n",
      "i: 4\n",
      "i: 5\n",
      "i: 6\n",
      "i: 7\n",
      "i: 8\n",
      "i: 9\n",
      "i: 10\n",
      "i: 11\n",
      "i: 12\n",
      "i: 13\n",
      "i: 14\n",
      "i: 15\n",
      "cluster figure count: 15\n"
     ]
    },
    {
     "data": {
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       "      <th></th>\n",
       "      <th>i</th>\n",
       "      <th>figure_id</th>\n",
       "      <th>p_value</th>\n",
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       "      <th>next_best_p_value</th>\n",
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       "      <th>relevant_figure_count</th>\n",
       "      <th>remaining_trapi_results_unified_curies</th>\n",
       "      <th>cluster_figure_id_count</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0</td>\n",
       "      <td>PMC7499322__SCI2020-8844730.003.jpg</td>\n",
       "      <td>0.001939</td>\n",
       "      <td>[PMC6678287__cancers-11-00960-g006.jpg]</td>\n",
       "      <td>0.002502</td>\n",
       "      <td>4</td>\n",
       "      <td>18</td>\n",
       "      <td>23</td>\n",
       "      <td>992</td>\n",
       "      <td>{MESH:D007938, NCBIGene:842, NCBIGene:836, NCB...</td>\n",
       "      <td>{NCBIGene:840, MESH:C051905, MESH:D011471, NCB...</td>\n",
       "      <td>{NCBIGene:842, NCBIGene:836, NCBIGene:841}</td>\n",
       "      <td>3</td>\n",
       "      <td>1037</td>\n",
       "      <td>62</td>\n",
       "      <td>22</td>\n",
       "      <td>1</td>\n",
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       "      <th>1</th>\n",
       "      <td>1</td>\n",
       "      <td>PMC4896164__40880_2016_108_Fig2_HTML.jpg</td>\n",
       "      <td>0.002356</td>\n",
       "      <td>[PMC7033644__fonc-10-00090-g0002.jpg]</td>\n",
       "      <td>0.010612</td>\n",
       "      <td>4</td>\n",
       "      <td>15</td>\n",
       "      <td>29</td>\n",
       "      <td>989</td>\n",
       "      <td>{NCBIGene:3725, MESH:D007938, NCBIGene:207, NC...</td>\n",
       "      <td>{NCBIGene:3725, NCBIGene:332, NCBIGene:2885, N...</td>\n",
       "      <td>{NCBIGene:3725, NCBIGene:207, NCBIGene:1399}</td>\n",
       "      <td>3</td>\n",
       "      <td>1034</td>\n",
       "      <td>61</td>\n",
       "      <td>19</td>\n",
       "      <td>2</td>\n",
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       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2</td>\n",
       "      <td>PMC7033644__fonc-10-00090-g0002.jpg</td>\n",
       "      <td>0.007549</td>\n",
       "      <td>[PMC6981580__ijms-21-00045-g001.jpg]</td>\n",
       "      <td>0.015429</td>\n",
       "      <td>2</td>\n",
       "      <td>14</td>\n",
       "      <td>7</td>\n",
       "      <td>1014</td>\n",
       "      <td>{MESH:D007938, NCBIGene:3576}</td>\n",
       "      <td>{NCBIGene:650, NCBIGene:3577, MESH:D015470, NC...</td>\n",
       "      <td>{NCBIGene:3576}</td>\n",
       "      <td>1</td>\n",
       "      <td>1029</td>\n",
       "      <td>60</td>\n",
       "      <td>16</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>3</td>\n",
       "      <td>PMC6981580__ijms-21-00045-g001.jpg</td>\n",
       "      <td>0.014465</td>\n",
       "      <td>[PMC2696319__nihms32253f1.jpg]</td>\n",
       "      <td>0.041475</td>\n",
       "      <td>1</td>\n",
       "      <td>14</td>\n",
       "      <td>0</td>\n",
       "      <td>1022</td>\n",
       "      <td>{MESH:D007938}</td>\n",
       "      <td>{MESH:D007938}</td>\n",
       "      <td>{}</td>\n",
       "      <td>0</td>\n",
       "      <td>1026</td>\n",
       "      <td>59</td>\n",
       "      <td>15</td>\n",
       "      <td>4</td>\n",
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       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>4</td>\n",
       "      <td>PMC6981580__ijms-21-00045-g001.jpg</td>\n",
       "      <td>0.014465</td>\n",
       "      <td>[PMC2696319__nihms32253f1.jpg]</td>\n",
       "      <td>0.041475</td>\n",
       "      <td>1</td>\n",
       "      <td>14</td>\n",
       "      <td>0</td>\n",
       "      <td>1022</td>\n",
       "      <td>{MESH:D007938}</td>\n",
       "      <td>{MESH:D007938}</td>\n",
       "      <td>{}</td>\n",
       "      <td>0</td>\n",
       "      <td>1026</td>\n",
       "      <td>59</td>\n",
       "      <td>15</td>\n",
       "      <td>5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>5</td>\n",
       "      <td>PMC6981580__ijms-21-00045-g001.jpg</td>\n",
       "      <td>0.014465</td>\n",
       "      <td>[PMC2696319__nihms32253f1.jpg]</td>\n",
       "      <td>0.041475</td>\n",
       "      <td>1</td>\n",
       "      <td>14</td>\n",
       "      <td>0</td>\n",
       "      <td>1022</td>\n",
       "      <td>{MESH:D007938}</td>\n",
       "      <td>{MESH:D007938}</td>\n",
       "      <td>{}</td>\n",
       "      <td>0</td>\n",
       "      <td>1026</td>\n",
       "      <td>59</td>\n",
       "      <td>15</td>\n",
       "      <td>6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>6</td>\n",
       "      <td>PMC6981580__ijms-21-00045-g001.jpg</td>\n",
       "      <td>0.014465</td>\n",
       "      <td>[PMC2696319__nihms32253f1.jpg]</td>\n",
       "      <td>0.041475</td>\n",
       "      <td>1</td>\n",
       "      <td>14</td>\n",
       "      <td>0</td>\n",
       "      <td>1022</td>\n",
       "      <td>{MESH:D007938}</td>\n",
       "      <td>{MESH:D007938}</td>\n",
       "      <td>{}</td>\n",
       "      <td>0</td>\n",
       "      <td>1026</td>\n",
       "      <td>59</td>\n",
       "      <td>15</td>\n",
       "      <td>7</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>7</td>\n",
       "      <td>PMC6981580__ijms-21-00045-g001.jpg</td>\n",
       "      <td>0.014465</td>\n",
       "      <td>[PMC2696319__nihms32253f1.jpg]</td>\n",
       "      <td>0.041475</td>\n",
       "      <td>1</td>\n",
       "      <td>14</td>\n",
       "      <td>0</td>\n",
       "      <td>1022</td>\n",
       "      <td>{MESH:D007938}</td>\n",
       "      <td>{MESH:D007938}</td>\n",
       "      <td>{}</td>\n",
       "      <td>0</td>\n",
       "      <td>1026</td>\n",
       "      <td>59</td>\n",
       "      <td>15</td>\n",
       "      <td>8</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>8</td>\n",
       "      <td>PMC6981580__ijms-21-00045-g001.jpg</td>\n",
       "      <td>0.014465</td>\n",
       "      <td>[PMC2696319__nihms32253f1.jpg]</td>\n",
       "      <td>0.041475</td>\n",
       "      <td>1</td>\n",
       "      <td>14</td>\n",
       "      <td>0</td>\n",
       "      <td>1022</td>\n",
       "      <td>{MESH:D007938}</td>\n",
       "      <td>{MESH:D007938}</td>\n",
       "      <td>{}</td>\n",
       "      <td>0</td>\n",
       "      <td>1026</td>\n",
       "      <td>59</td>\n",
       "      <td>15</td>\n",
       "      <td>9</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>9</td>\n",
       "      <td>PMC6981580__ijms-21-00045-g001.jpg</td>\n",
       "      <td>0.014465</td>\n",
       "      <td>[PMC2696319__nihms32253f1.jpg]</td>\n",
       "      <td>0.041475</td>\n",
       "      <td>1</td>\n",
       "      <td>14</td>\n",
       "      <td>0</td>\n",
       "      <td>1022</td>\n",
       "      <td>{MESH:D007938}</td>\n",
       "      <td>{MESH:D007938}</td>\n",
       "      <td>{}</td>\n",
       "      <td>0</td>\n",
       "      <td>1026</td>\n",
       "      <td>59</td>\n",
       "      <td>15</td>\n",
       "      <td>10</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>10</td>\n",
       "      <td>PMC6981580__ijms-21-00045-g001.jpg</td>\n",
       "      <td>0.014465</td>\n",
       "      <td>[PMC2696319__nihms32253f1.jpg]</td>\n",
       "      <td>0.041475</td>\n",
       "      <td>1</td>\n",
       "      <td>14</td>\n",
       "      <td>0</td>\n",
       "      <td>1022</td>\n",
       "      <td>{MESH:D007938}</td>\n",
       "      <td>{MESH:D007938}</td>\n",
       "      <td>{}</td>\n",
       "      <td>0</td>\n",
       "      <td>1026</td>\n",
       "      <td>59</td>\n",
       "      <td>15</td>\n",
       "      <td>11</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>11</td>\n",
       "      <td>PMC6981580__ijms-21-00045-g001.jpg</td>\n",
       "      <td>0.014465</td>\n",
       "      <td>[PMC2696319__nihms32253f1.jpg]</td>\n",
       "      <td>0.041475</td>\n",
       "      <td>1</td>\n",
       "      <td>14</td>\n",
       "      <td>0</td>\n",
       "      <td>1022</td>\n",
       "      <td>{MESH:D007938}</td>\n",
       "      <td>{MESH:D007938}</td>\n",
       "      <td>{}</td>\n",
       "      <td>0</td>\n",
       "      <td>1026</td>\n",
       "      <td>59</td>\n",
       "      <td>15</td>\n",
       "      <td>12</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>12</td>\n",
       "      <td>PMC6981580__ijms-21-00045-g001.jpg</td>\n",
       "      <td>0.014465</td>\n",
       "      <td>[PMC2696319__nihms32253f1.jpg]</td>\n",
       "      <td>0.041475</td>\n",
       "      <td>1</td>\n",
       "      <td>14</td>\n",
       "      <td>0</td>\n",
       "      <td>1022</td>\n",
       "      <td>{MESH:D007938}</td>\n",
       "      <td>{MESH:D007938}</td>\n",
       "      <td>{}</td>\n",
       "      <td>0</td>\n",
       "      <td>1026</td>\n",
       "      <td>59</td>\n",
       "      <td>15</td>\n",
       "      <td>13</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>13</td>\n",
       "      <td>PMC6981580__ijms-21-00045-g001.jpg</td>\n",
       "      <td>0.014465</td>\n",
       "      <td>[PMC2696319__nihms32253f1.jpg]</td>\n",
       "      <td>0.041475</td>\n",
       "      <td>1</td>\n",
       "      <td>14</td>\n",
       "      <td>0</td>\n",
       "      <td>1022</td>\n",
       "      <td>{MESH:D007938}</td>\n",
       "      <td>{MESH:D007938}</td>\n",
       "      <td>{}</td>\n",
       "      <td>0</td>\n",
       "      <td>1026</td>\n",
       "      <td>59</td>\n",
       "      <td>15</td>\n",
       "      <td>14</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>14</td>\n",
       "      <td>PMC6981580__ijms-21-00045-g001.jpg</td>\n",
       "      <td>0.014465</td>\n",
       "      <td>[PMC2696319__nihms32253f1.jpg]</td>\n",
       "      <td>0.041475</td>\n",
       "      <td>1</td>\n",
       "      <td>14</td>\n",
       "      <td>0</td>\n",
       "      <td>1022</td>\n",
       "      <td>{MESH:D007938}</td>\n",
       "      <td>{MESH:D007938}</td>\n",
       "      <td>{}</td>\n",
       "      <td>0</td>\n",
       "      <td>1026</td>\n",
       "      <td>59</td>\n",
       "      <td>15</td>\n",
       "      <td>15</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "     i                                 figure_id   p_value  \\\n",
       "0    0       PMC7499322__SCI2020-8844730.003.jpg  0.001939   \n",
       "1    1  PMC4896164__40880_2016_108_Fig2_HTML.jpg  0.002356   \n",
       "2    2       PMC7033644__fonc-10-00090-g0002.jpg  0.007549   \n",
       "3    3        PMC6981580__ijms-21-00045-g001.jpg  0.014465   \n",
       "4    4        PMC6981580__ijms-21-00045-g001.jpg  0.014465   \n",
       "5    5        PMC6981580__ijms-21-00045-g001.jpg  0.014465   \n",
       "6    6        PMC6981580__ijms-21-00045-g001.jpg  0.014465   \n",
       "7    7        PMC6981580__ijms-21-00045-g001.jpg  0.014465   \n",
       "8    8        PMC6981580__ijms-21-00045-g001.jpg  0.014465   \n",
       "9    9        PMC6981580__ijms-21-00045-g001.jpg  0.014465   \n",
       "10  10        PMC6981580__ijms-21-00045-g001.jpg  0.014465   \n",
       "11  11        PMC6981580__ijms-21-00045-g001.jpg  0.014465   \n",
       "12  12        PMC6981580__ijms-21-00045-g001.jpg  0.014465   \n",
       "13  13        PMC6981580__ijms-21-00045-g001.jpg  0.014465   \n",
       "14  14        PMC6981580__ijms-21-00045-g001.jpg  0.014465   \n",
       "\n",
       "                       next_best_figure_ids  next_best_p_value  \\\n",
       "0   [PMC6678287__cancers-11-00960-g006.jpg]           0.002502   \n",
       "1     [PMC7033644__fonc-10-00090-g0002.jpg]           0.010612   \n",
       "2      [PMC6981580__ijms-21-00045-g001.jpg]           0.015429   \n",
       "3            [PMC2696319__nihms32253f1.jpg]           0.041475   \n",
       "4            [PMC2696319__nihms32253f1.jpg]           0.041475   \n",
       "5            [PMC2696319__nihms32253f1.jpg]           0.041475   \n",
       "6            [PMC2696319__nihms32253f1.jpg]           0.041475   \n",
       "7            [PMC2696319__nihms32253f1.jpg]           0.041475   \n",
       "8            [PMC2696319__nihms32253f1.jpg]           0.041475   \n",
       "9            [PMC2696319__nihms32253f1.jpg]           0.041475   \n",
       "10           [PMC2696319__nihms32253f1.jpg]           0.041475   \n",
       "11           [PMC2696319__nihms32253f1.jpg]           0.041475   \n",
       "12           [PMC2696319__nihms32253f1.jpg]           0.041475   \n",
       "13           [PMC2696319__nihms32253f1.jpg]           0.041475   \n",
       "14           [PMC2696319__nihms32253f1.jpg]           0.041475   \n",
       "\n",
       "    yes_trapi_results_yes_figure  yes_trapi_results_no_figure  \\\n",
       "0                              4                           18   \n",
       "1                              4                           15   \n",
       "2                              2                           14   \n",
       "3                              1                           14   \n",
       "4                              1                           14   \n",
       "5                              1                           14   \n",
       "6                              1                           14   \n",
       "7                              1                           14   \n",
       "8                              1                           14   \n",
       "9                              1                           14   \n",
       "10                             1                           14   \n",
       "11                             1                           14   \n",
       "12                             1                           14   \n",
       "13                             1                           14   \n",
       "14                             1                           14   \n",
       "\n",
       "    no_trapi_results_yes_figure  no_trapi_results_no_figure  \\\n",
       "0                            23                         992   \n",
       "1                            29                         989   \n",
       "2                             7                        1014   \n",
       "3                             0                        1022   \n",
       "4                             0                        1022   \n",
       "5                             0                        1022   \n",
       "6                             0                        1022   \n",
       "7                             0                        1022   \n",
       "8                             0                        1022   \n",
       "9                             0                        1022   \n",
       "10                            0                        1022   \n",
       "11                            0                        1022   \n",
       "12                            0                        1022   \n",
       "13                            0                        1022   \n",
       "14                            0                        1022   \n",
       "\n",
       "                       figure_curies_in_trapi_results  \\\n",
       "0   {MESH:D007938, NCBIGene:842, NCBIGene:836, NCB...   \n",
       "1   {NCBIGene:3725, MESH:D007938, NCBIGene:207, NC...   \n",
       "2                       {MESH:D007938, NCBIGene:3576}   \n",
       "3                                      {MESH:D007938}   \n",
       "4                                      {MESH:D007938}   \n",
       "5                                      {MESH:D007938}   \n",
       "6                                      {MESH:D007938}   \n",
       "7                                      {MESH:D007938}   \n",
       "8                                      {MESH:D007938}   \n",
       "9                                      {MESH:D007938}   \n",
       "10                                     {MESH:D007938}   \n",
       "11                                     {MESH:D007938}   \n",
       "12                                     {MESH:D007938}   \n",
       "13                                     {MESH:D007938}   \n",
       "14                                     {MESH:D007938}   \n",
       "\n",
       "                                            curie_set  \\\n",
       "0   {NCBIGene:840, MESH:C051905, MESH:D011471, NCB...   \n",
       "1   {NCBIGene:3725, NCBIGene:332, NCBIGene:2885, N...   \n",
       "2   {NCBIGene:650, NCBIGene:3577, MESH:D015470, NC...   \n",
       "3                                      {MESH:D007938}   \n",
       "4                                      {MESH:D007938}   \n",
       "5                                      {MESH:D007938}   \n",
       "6                                      {MESH:D007938}   \n",
       "7                                      {MESH:D007938}   \n",
       "8                                      {MESH:D007938}   \n",
       "9                                      {MESH:D007938}   \n",
       "10                                     {MESH:D007938}   \n",
       "11                                     {MESH:D007938}   \n",
       "12                                     {MESH:D007938}   \n",
       "13                                     {MESH:D007938}   \n",
       "14                                     {MESH:D007938}   \n",
       "\n",
       "                               curies_to_exclude  len_curies_to_exclude  \\\n",
       "0     {NCBIGene:842, NCBIGene:836, NCBIGene:841}                      3   \n",
       "1   {NCBIGene:3725, NCBIGene:207, NCBIGene:1399}                      3   \n",
       "2                                {NCBIGene:3576}                      1   \n",
       "3                                             {}                      0   \n",
       "4                                             {}                      0   \n",
       "5                                             {}                      0   \n",
       "6                                             {}                      0   \n",
       "7                                             {}                      0   \n",
       "8                                             {}                      0   \n",
       "9                                             {}                      0   \n",
       "10                                            {}                      0   \n",
       "11                                            {}                      0   \n",
       "12                                            {}                      0   \n",
       "13                                            {}                      0   \n",
       "14                                            {}                      0   \n",
       "\n",
       "    relevant_figures_unique_curie_count  relevant_figure_count  \\\n",
       "0                                  1037                     62   \n",
       "1                                  1034                     61   \n",
       "2                                  1029                     60   \n",
       "3                                  1026                     59   \n",
       "4                                  1026                     59   \n",
       "5                                  1026                     59   \n",
       "6                                  1026                     59   \n",
       "7                                  1026                     59   \n",
       "8                                  1026                     59   \n",
       "9                                  1026                     59   \n",
       "10                                 1026                     59   \n",
       "11                                 1026                     59   \n",
       "12                                 1026                     59   \n",
       "13                                 1026                     59   \n",
       "14                                 1026                     59   \n",
       "\n",
       "    remaining_trapi_results_unified_curies  cluster_figure_id_count  \n",
       "0                                       22                        1  \n",
       "1                                       19                        2  \n",
       "2                                       16                        3  \n",
       "3                                       15                        4  \n",
       "4                                       15                        5  \n",
       "5                                       15                        6  \n",
       "6                                       15                        7  \n",
       "7                                       15                        8  \n",
       "8                                       15                        9  \n",
       "9                                       15                       10  \n",
       "10                                      15                       11  \n",
       "11                                      15                       12  \n",
       "12                                      15                       13  \n",
       "13                                      15                       14  \n",
       "14                                      15                       15  "
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Importing reduce for \n",
    "# rolling computations\n",
    "from functools import reduce\n",
    "  \n",
    "def set_union(series):\n",
    "    return reduce(lambda x, y: x | y, series)\n",
    "\n",
    "def create_set(series):\n",
    "    return set(series.tolist())\n",
    "\n",
    "\n",
    "# TODO: remove cluster_figure_data and its associated DF.\n",
    "# It's just a temporary thing to show what's happening inside.\n",
    "cluster_figure_data = []\n",
    "def get_next_cluster_figures(\n",
    "    all_relevant_curie_count,\n",
    "    remaining_relevant_figure_curie_df,\n",
    "    remaining_trapi_results_unified_curies,\n",
    "    iteration_limit=15,\n",
    "    i=0,\n",
    "    cluster_figure_ids=[],\n",
    "):\n",
    "    print(f'i: {i}')\n",
    "    \n",
    "    relevant_figures_unique_curie_count = len(set(\n",
    "        remaining_relevant_figure_curie_df[\"curie\"].drop_duplicates()\n",
    "    ))\n",
    "    \n",
    "    relevant_figure_count = len(set(\n",
    "        remaining_relevant_figure_curie_df[\"figure_id\"].drop_duplicates()\n",
    "    ))\n",
    "\n",
    "    if (\n",
    "        i >= iteration_limit\n",
    "    ) or (\n",
    "        len(remaining_trapi_results_unified_curies) == 0\n",
    "    ) or (\n",
    "        relevant_figure_count == 0\n",
    "    ):\n",
    "        return cluster_figure_ids\n",
    "    \n",
    "    remaining_figures_df = remaining_relevant_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",
    "    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_results_unified_curies.intersection(\n",
    "            set(curie_set)\n",
    "        )\n",
    "    )\n",
    "\n",
    "    # number of CURIEs in both TRAPI results and figure\n",
    "    remaining_figures_df[\"yes_trapi_results_yes_figure\"] = remaining_figures_df[\n",
    "        \"figure_curies_in_trapi_results\"\n",
    "    ].map(len)\n",
    "\n",
    "    # number of CURIEs in TRAPI results but not in figure\n",
    "    remaining_figures_df[\"yes_trapi_results_no_figure\"] = (\n",
    "        len(remaining_trapi_results_unified_curies) -\n",
    "        remaining_figures_df[\"yes_trapi_results_yes_figure\"]\n",
    "    )\n",
    "\n",
    "    # number of CURIEs not in TRAPI results but in figure\n",
    "    remaining_figures_df[\"no_trapi_results_yes_figure\"] = (\n",
    "        remaining_figures_df[\"figure_curie_count\"] -\n",
    "        remaining_figures_df[\"yes_trapi_results_yes_figure\"]\n",
    "    )\n",
    "\n",
    "    # number of CURIEs not in TRAPI results and not in figure,\n",
    "    # ie., number of unique CURIEs only in other figures\n",
    "    remaining_figures_df[\"no_trapi_results_no_figure\"] = (\n",
    "        all_relevant_curie_count -\n",
    "        len(remaining_trapi_results_unified_curies) -\n",
    "        remaining_figures_df[\"no_trapi_results_yes_figure\"]\n",
    "    )\n",
    "\n",
    "    # see https://stackoverflow.com/a/58661068/5354298\n",
    "    _, _, twosided = pvalue_npy(\n",
    "        remaining_figures_df[\"yes_trapi_results_yes_figure\"].to_numpy('uint'),\n",
    "        remaining_figures_df[\"yes_trapi_results_no_figure\"].to_numpy('uint'),\n",
    "        remaining_figures_df[\"no_trapi_results_yes_figure\"].to_numpy('uint'),\n",
    "        remaining_figures_df[\"no_trapi_results_no_figure\"].to_numpy('uint'),\n",
    "    )\n",
    "\n",
    "    remaining_figures_df[\"p_value\"] = pd.Series(twosided, index=remaining_figures_df.index)\n",
    "    remaining_figures_df[\"p_value\"].sort_values()\n",
    "    \n",
    "    min_df = remaining_figures_df[\n",
    "        remaining_figures_df[\"p_value\"] == remaining_figures_df[\"p_value\"].min()\n",
    "    ]\n",
    "        \n",
    "    cluster_figure_ids.extend(min_df.index.tolist())\n",
    "    \n",
    "    # TODO: should we always keep the user-specified CURIE ids?\n",
    "    curies_to_exclude = set(min_df[\"figure_curies_in_trapi_results\"].agg(set_union)) - user_specified_ids\n",
    "    \n",
    "    next_min_df_score = remaining_figures_df[\n",
    "        remaining_figures_df[\"p_value\"] > remaining_figures_df[\"p_value\"].min()\n",
    "    ][\"p_value\"].min()\n",
    "    next_min_df = remaining_figures_df[remaining_figures_df[\"p_value\"] == next_min_df_score]\n",
    "    \n",
    "    for cluster_figure_id, min_row in min_df.iterrows():\n",
    "        cluster_figure_data.append({\n",
    "            \"i\": i,\n",
    "            \"figure_id\": cluster_figure_id,\n",
    "            \"p_value\": min_row[\"p_value\"],\n",
    "            \"next_best_figure_ids\": next_min_df.index.tolist(),\n",
    "            \"next_best_p_value\": next_min_df[\"p_value\"].min(),\n",
    "            \"yes_trapi_results_yes_figure\": min_row[\"yes_trapi_results_yes_figure\"],\n",
    "            \"yes_trapi_results_no_figure\": min_row[\"yes_trapi_results_no_figure\"],\n",
    "            \"no_trapi_results_yes_figure\": min_row[\"no_trapi_results_yes_figure\"],\n",
    "            \"no_trapi_results_no_figure\": min_row[\"no_trapi_results_no_figure\"],\n",
    "            \"figure_curies_in_trapi_results\": min_row[\"figure_curies_in_trapi_results\"],\n",
    "            \"curie_set\": min_row[\"curie_set\"],\n",
    "            \"curies_to_exclude\": curies_to_exclude,\n",
    "            \"len_curies_to_exclude\": len(curies_to_exclude),\n",
    "            \"relevant_figures_unique_curie_count\": relevant_figures_unique_curie_count,\n",
    "            \"relevant_figure_count\": relevant_figure_count,\n",
    "            \"remaining_trapi_results_unified_curies\": len(remaining_trapi_results_unified_curies),\n",
    "            \"cluster_figure_id_count\": len(cluster_figure_ids),\n",
    "        })\n",
    "\n",
    "    if len(curies_to_exclude) == 0:\n",
    "        # On this iteration, failed to match any additional CURIE(s)\n",
    "        # This figure will be excluded, so we'll try again w/ another figure.\n",
    "        next_remaining_relevant_figure_curie_df = remaining_relevant_figure_curie_df\n",
    "    else:\n",
    "        next_remaining_relevant_figure_curie_df = remaining_relevant_figure_curie_df[\n",
    "            (~remaining_relevant_figure_curie_df[\"figure_id\"].isin(\n",
    "                cluster_figure_ids\n",
    "            ))\n",
    "        ]\n",
    "    \n",
    "    return get_next_cluster_figures(\n",
    "        all_relevant_curie_count,\n",
    "        next_remaining_relevant_figure_curie_df,\n",
    "        remaining_trapi_results_unified_curies - curies_to_exclude,\n",
    "        iteration_limit,\n",
    "        i + 1,\n",
    "        cluster_figure_ids,\n",
    "    )\n",
    "\n",
    "\n",
    "# don't worry whether cluster figure has user-specified IDs\n",
    "#relevant_figure_curie_df = figure_curie_df[figure_curie_df[\"category\"].isin(\n",
    "#    list(relevant_pfocr_categories)\n",
    "#)]\n",
    "# only use cluster figures that have the user-specified ids\n",
    "figures_with_user_specified_ids = set(\n",
    "    figure_curie_df[\n",
    "        figure_curie_df[\"curie\"].isin(user_specified_ids)\n",
    "    ][\"figure_id\"]\n",
    ")\n",
    "print(f'number of figures w/ user-specified CURIEs: {len(figures_with_user_specified_ids)}')\n",
    "relevant_figure_curie_df = figure_curie_df[figure_curie_df[\"category\"].isin(\n",
    "    list(relevant_pfocr_categories)\n",
    ") & figure_curie_df[\"figure_id\"].isin(\n",
    "    figures_with_user_specified_ids\n",
    ")]\n",
    "\n",
    "all_relevant_curie_count = len(set(\n",
    "    relevant_figure_curie_df[\"curie\"].drop_duplicates()\n",
    "))\n",
    "print(f'all_relevant_curie_count: {all_relevant_curie_count}')\n",
    "\n",
    "cluster_figure_ids_out = get_next_cluster_figures(\n",
    "    all_relevant_curie_count,\n",
    "    relevant_figure_curie_df,\n",
    "    trapi_results_unified_curies,\n",
    ")\n",
    "print(f'cluster figure count: {len(cluster_figure_ids_out)}')\n",
    "cluster_figure_df = pd.DataFrame.from_records(cluster_figure_data)\n",
    "cluster_figure_df"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Cluster BTE TRAPI Results by Selected PFOCR Figures (Cluster Figures)\n",
    "\n",
    "See details in write-up at the top."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [],
   "source": [
    "figures_df = relevant_figure_curie_df[\n",
    "    relevant_figure_curie_df[\"figure_id\"].isin(set(cluster_figure_df[\"figure_id\"]))\n",
    "][[\"figure_id\", \"curie\"]].groupby(\"figure_id\").agg(\n",
    "    create_set\n",
    ").rename(columns={\n",
    "    \"curie\": \"figure_curie_set\"\n",
    "}).join(\n",
    "    figure_metadata_df\n",
    ").reset_index()\n",
    "\n",
    "figures_df[\"figure_curie_count\"] = figures_df[\"figure_curie_set\"].map(len)\n",
    "\n",
    "figures_df['crossjoin_key'] = 0\n",
    "trapi_results_df['crossjoin_key'] = 0\n",
    "\n",
    "trapi_results_with_figures_df = trapi_results_df.merge(\n",
    "    figures_df, on='crossjoin_key', how='outer'\n",
    ").rename(columns={\n",
    "    \"unified_curie_set\": \"trapi_result_curie_set\"\n",
    "})\n",
    "\n",
    "trapi_results_with_figures_df[\"common_curie_set\"] = trapi_results_with_figures_df.apply(\n",
    "    lambda r: r[\"trapi_result_curie_set\"] & r[\"figure_curie_set\"], axis=1\n",
    ")\n",
    "trapi_results_with_figures_df\n",
    "# number of CURIEs in both TRAPI result and figure\n",
    "# trapi_curies_in_figure\n",
    "# x\n",
    "# Selected & Having the property\n",
    "trapi_results_with_figures_df[\"yes_trapi_result_yes_figure\"] = (\n",
    "    trapi_results_with_figures_df[\"common_curie_set\"].map(len)\n",
    ")\n",
    "\n",
    "# number of CURIEs in TRAPI result but not in figure\n",
    "# q_node_id_count - trapi_curies_in_figure\n",
    "# n - x\n",
    "# Selected & Not Having the property\n",
    "trapi_results_with_figures_df[\"yes_trapi_result_no_figure\"] = (\n",
    "    len(q_node_ids) - trapi_results_with_figures_df[\"yes_trapi_result_yes_figure\"]\n",
    ")\n",
    "\n",
    "# number of CURIEs not in TRAPI result but in figure\n",
    "# curies_in_figure - trapi_curies_in_figure\n",
    "# N - x\n",
    "# Not Selected & Having the property\n",
    "trapi_results_with_figures_df[\"no_trapi_result_yes_figure\"] = (\n",
    "    trapi_results_with_figures_df[\"figure_curie_set\"].map(len) - \n",
    "    trapi_results_with_figures_df[\"yes_trapi_result_yes_figure\"]\n",
    ")\n",
    "\n",
    "# number of CURIEs not in TRAPI result and not in figure,\n",
    "# ie., number of unique CURIEs only in other figures\n",
    "# 28735 - curies_in_figure - q_node_id_count + trapi_curies_in_figure\n",
    "# M - (n + N) + x\n",
    "# Not Selected & Not Having the property\n",
    "trapi_results_with_figures_df[\"no_trapi_result_no_figure\"] = (\n",
    "    all_relevant_curie_count -\n",
    "    trapi_results_with_figures_df[\"figure_curie_set\"].map(len) -\n",
    "    len(q_node_ids) +\n",
    "    trapi_results_with_figures_df[\"yes_trapi_result_yes_figure\"]\n",
    ")\n",
    "\n",
    "# see https://stackoverflow.com/a/58661068/5354298\n",
    "_, _, twosided = pvalue_npy(\n",
    "    trapi_results_with_figures_df[\"yes_trapi_result_yes_figure\"].to_numpy('uint'),\n",
    "    trapi_results_with_figures_df[\"yes_trapi_result_no_figure\"].to_numpy('uint'),\n",
    "    trapi_results_with_figures_df[\"no_trapi_result_yes_figure\"].to_numpy('uint'),\n",
    "    trapi_results_with_figures_df[\"no_trapi_result_no_figure\"].to_numpy('uint'),\n",
    ")\n",
    "\n",
    "trapi_results_with_figures_df[\"p_value\"] = pd.Series(twosided, index=trapi_results_with_figures_df.index)\n",
    "trapi_results_with_figures_df[\"p_value\"].sort_values()\n",
    "\n",
    "# Needed in order to use the TRAPI result CURIEs as a key. A Python Set is not hashable.\n",
    "trapi_results_with_figures_df[\"trapi_result_curies_key\"] = trapi_results_with_figures_df[\n",
    "    \"trapi_result_curie_set\"\n",
    "].map(\n",
    "    lambda x: tuple(sorted(x))\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(array([98.,  0., 78.,  0.,  0.,  0.,  0.,  0., 69., 71.]),\n",
       " array([0.00020015, 0.00943497, 0.01866978, 0.0279046 , 0.03713942,\n",
       "        0.04637424, 0.05560906, 0.06484387, 0.07407869, 0.08331351,\n",
       "        0.09254833]),\n",
       " <BarContainer object of 10 artists>)"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.hist(trapi_results_with_figures_df[\"p_value\"])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## View TRAPI Results by PFOCR Figure"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>n0</th>\n",
       "      <th>e0</th>\n",
       "      <th>n1</th>\n",
       "      <th>e1</th>\n",
       "      <th>n2</th>\n",
       "      <th>n0_original_curie</th>\n",
       "      <th>n0_unified_curie</th>\n",
       "      <th>n1_original_curie</th>\n",
       "      <th>n1_unified_curie</th>\n",
       "      <th>n2_original_curie</th>\n",
       "      <th>...</th>\n",
       "      <th>figure_url</th>\n",
       "      <th>figure_title</th>\n",
       "      <th>figure_curie_count</th>\n",
       "      <th>common_curie_set</th>\n",
       "      <th>yes_trapi_result_yes_figure</th>\n",
       "      <th>yes_trapi_result_no_figure</th>\n",
       "      <th>no_trapi_result_yes_figure</th>\n",
       "      <th>no_trapi_result_no_figure</th>\n",
       "      <th>p_value</th>\n",
       "      <th>trapi_result_curies_key</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>174</th>\n",
       "      <td>Imatinib mesylate</td>\n",
       "      <td>increases_secretion_of</td>\n",
       "      <td>CXCL8</td>\n",
       "      <td>gene_associated_with_condition</td>\n",
       "      <td>leukemia</td>\n",
       "      <td>PUBCHEM.COMPOUND:123596</td>\n",
       "      <td>MESH:D000068877</td>\n",
       "      <td>NCBIGene:3576</td>\n",
       "      <td>NCBIGene:3576</td>\n",
       "      <td>MONDO:0005059</td>\n",
       "      <td>...</td>\n",
       "      <td>https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7...</td>\n",
       "      <td>EV trafficking between leukemia and stromal ce...</td>\n",
       "      <td>9</td>\n",
       "      <td>{MESH:D007938, NCBIGene:3576}</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>7</td>\n",
       "      <td>1027</td>\n",
       "      <td>0.000200</td>\n",
       "      <td>(MESH:D000068877, MESH:D007938, NCBIGene:3576)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>315</th>\n",
       "      <td>Imatinib mesylate</td>\n",
       "      <td>increases_activity_of</td>\n",
       "      <td>CASP8</td>\n",
       "      <td>gene_associated_with_condition</td>\n",
       "      <td>leukemia</td>\n",
       "      <td>PUBCHEM.COMPOUND:123596</td>\n",
       "      <td>MESH:D000068877</td>\n",
       "      <td>NCBIGene:841</td>\n",
       "      <td>NCBIGene:841</td>\n",
       "      <td>MONDO:0005059</td>\n",
       "      <td>...</td>\n",
       "      <td>https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7...</td>\n",
       "      <td>Inhibitory roles of MSC-derived exosomes in tu...</td>\n",
       "      <td>27</td>\n",
       "      <td>{MESH:D007938, NCBIGene:841}</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>25</td>\n",
       "      <td>1009</td>\n",
       "      <td>0.001929</td>\n",
       "      <td>(MESH:D000068877, MESH:D007938, NCBIGene:841)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>187</th>\n",
       "      <td>Imatinib mesylate</td>\n",
       "      <td>increases_degradation_of</td>\n",
       "      <td>CASP9</td>\n",
       "      <td>correlated_with</td>\n",
       "      <td>leukemia</td>\n",
       "      <td>PUBCHEM.COMPOUND:123596</td>\n",
       "      <td>MESH:D000068877</td>\n",
       "      <td>NCBIGene:842</td>\n",
       "      <td>NCBIGene:842</td>\n",
       "      <td>MONDO:0005059</td>\n",
       "      <td>...</td>\n",
       "      <td>https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7...</td>\n",
       "      <td>Inhibitory roles of MSC-derived exosomes in tu...</td>\n",
       "      <td>27</td>\n",
       "      <td>{MESH:D007938, NCBIGene:842}</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>25</td>\n",
       "      <td>1009</td>\n",
       "      <td>0.001929</td>\n",
       "      <td>(MESH:D000068877, MESH:D007938, NCBIGene:842)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>191</th>\n",
       "      <td>Imatinib mesylate</td>\n",
       "      <td>increases_degradation_of</td>\n",
       "      <td>CASP9</td>\n",
       "      <td>gene_associated_with_condition</td>\n",
       "      <td>leukemia</td>\n",
       "      <td>PUBCHEM.COMPOUND:123596</td>\n",
       "      <td>MESH:D000068877</td>\n",
       "      <td>NCBIGene:842</td>\n",
       "      <td>NCBIGene:842</td>\n",
       "      <td>MONDO:0005059</td>\n",
       "      <td>...</td>\n",
       "      <td>https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7...</td>\n",
       "      <td>Inhibitory roles of MSC-derived exosomes in tu...</td>\n",
       "      <td>27</td>\n",
       "      <td>{MESH:D007938, NCBIGene:842}</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>25</td>\n",
       "      <td>1009</td>\n",
       "      <td>0.001929</td>\n",
       "      <td>(MESH:D000068877, MESH:D007938, NCBIGene:842)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>199</th>\n",
       "      <td>Imatinib mesylate</td>\n",
       "      <td>increases_degradation_of</td>\n",
       "      <td>CASP3</td>\n",
       "      <td>gene_associated_with_condition</td>\n",
       "      <td>leukemia</td>\n",
       "      <td>PUBCHEM.COMPOUND:123596</td>\n",
       "      <td>MESH:D000068877</td>\n",
       "      <td>NCBIGene:836</td>\n",
       "      <td>NCBIGene:836</td>\n",
       "      <td>MONDO:0005059</td>\n",
       "      <td>...</td>\n",
       "      <td>https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7...</td>\n",
       "      <td>Inhibitory roles of MSC-derived exosomes in tu...</td>\n",
       "      <td>27</td>\n",
       "      <td>{MESH:D007938, NCBIGene:836}</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>25</td>\n",
       "      <td>1009</td>\n",
       "      <td>0.001929</td>\n",
       "      <td>(MESH:D000068877, MESH:D007938, NCBIGene:836)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>180</th>\n",
       "      <td>Imatinib mesylate</td>\n",
       "      <td>increases_secretion_of</td>\n",
       "      <td>GPT</td>\n",
       "      <td>gene_associated_with_condition</td>\n",
       "      <td>leukemia</td>\n",
       "      <td>PUBCHEM.COMPOUND:123596</td>\n",
       "      <td>MESH:D000068877</td>\n",
       "      <td>NCBIGene:2875</td>\n",
       "      <td>NCBIGene:2875</td>\n",
       "      <td>MONDO:0005059</td>\n",
       "      <td>...</td>\n",
       "      <td>https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4...</td>\n",
       "      <td>Main pathways regulated by the BCR-ABL1 protein</td>\n",
       "      <td>33</td>\n",
       "      <td>{MESH:D007938}</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>32</td>\n",
       "      <td>1002</td>\n",
       "      <td>0.092548</td>\n",
       "      <td>(MESH:D000068877, MESH:D007938, NCBIGene:2875)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>176</th>\n",
       "      <td>Imatinib mesylate</td>\n",
       "      <td>increases_secretion_of</td>\n",
       "      <td>DIABLO</td>\n",
       "      <td>contributes_to</td>\n",
       "      <td>leukemia</td>\n",
       "      <td>PUBCHEM.COMPOUND:123596</td>\n",
       "      <td>MESH:D000068877</td>\n",
       "      <td>NCBIGene:56616</td>\n",
       "      <td>NCBIGene:56616</td>\n",
       "      <td>MONDO:0005059</td>\n",
       "      <td>...</td>\n",
       "      <td>https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4...</td>\n",
       "      <td>Main pathways regulated by the BCR-ABL1 protein</td>\n",
       "      <td>33</td>\n",
       "      <td>{MESH:D007938}</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>32</td>\n",
       "      <td>1002</td>\n",
       "      <td>0.092548</td>\n",
       "      <td>(MESH:D000068877, MESH:D007938, NCBIGene:56616)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>52</th>\n",
       "      <td>Imatinib mesylate</td>\n",
       "      <td>response_decreased_by</td>\n",
       "      <td>ABL1</td>\n",
       "      <td>related_to</td>\n",
       "      <td>leukemia</td>\n",
       "      <td>PUBCHEM.COMPOUND:123596</td>\n",
       "      <td>MESH:D000068877</td>\n",
       "      <td>NCBIGene:25</td>\n",
       "      <td>NCBIGene:25</td>\n",
       "      <td>MONDO:0005059</td>\n",
       "      <td>...</td>\n",
       "      <td>https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4...</td>\n",
       "      <td>Main pathways regulated by the BCR-ABL1 protein</td>\n",
       "      <td>33</td>\n",
       "      <td>{MESH:D007938}</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>32</td>\n",
       "      <td>1002</td>\n",
       "      <td>0.092548</td>\n",
       "      <td>(MESH:D000068877, MESH:D007938, NCBIGene:25)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>208</th>\n",
       "      <td>Imatinib mesylate</td>\n",
       "      <td>increases_activity_of</td>\n",
       "      <td>CASP3</td>\n",
       "      <td>gene_associated_with_condition</td>\n",
       "      <td>leukemia</td>\n",
       "      <td>PUBCHEM.COMPOUND:123596</td>\n",
       "      <td>MESH:D000068877</td>\n",
       "      <td>NCBIGene:836</td>\n",
       "      <td>NCBIGene:836</td>\n",
       "      <td>MONDO:0005059</td>\n",
       "      <td>...</td>\n",
       "      <td>https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4...</td>\n",
       "      <td>Main pathways regulated by the BCR-ABL1 protein</td>\n",
       "      <td>33</td>\n",
       "      <td>{MESH:D007938}</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>32</td>\n",
       "      <td>1002</td>\n",
       "      <td>0.092548</td>\n",
       "      <td>(MESH:D000068877, MESH:D007938, NCBIGene:836)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Imatinib mesylate</td>\n",
       "      <td>response_affected_by</td>\n",
       "      <td>ABL1</td>\n",
       "      <td>affects_risk_for</td>\n",
       "      <td>leukemia</td>\n",
       "      <td>PUBCHEM.COMPOUND:123596</td>\n",
       "      <td>MESH:D000068877</td>\n",
       "      <td>NCBIGene:25</td>\n",
       "      <td>NCBIGene:25</td>\n",
       "      <td>MONDO:0005059</td>\n",
       "      <td>...</td>\n",
       "      <td>https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4...</td>\n",
       "      <td>Main pathways regulated by the BCR-ABL1 protein</td>\n",
       "      <td>33</td>\n",
       "      <td>{MESH:D007938}</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>32</td>\n",
       "      <td>1002</td>\n",
       "      <td>0.092548</td>\n",
       "      <td>(MESH:D000068877, MESH:D007938, NCBIGene:25)</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>316 rows × 25 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                    n0                        e0      n1  \\\n",
       "174  Imatinib mesylate    increases_secretion_of   CXCL8   \n",
       "315  Imatinib mesylate     increases_activity_of   CASP8   \n",
       "187  Imatinib mesylate  increases_degradation_of   CASP9   \n",
       "191  Imatinib mesylate  increases_degradation_of   CASP9   \n",
       "199  Imatinib mesylate  increases_degradation_of   CASP3   \n",
       "..                 ...                       ...     ...   \n",
       "180  Imatinib mesylate    increases_secretion_of     GPT   \n",
       "176  Imatinib mesylate    increases_secretion_of  DIABLO   \n",
       "52   Imatinib mesylate     response_decreased_by    ABL1   \n",
       "208  Imatinib mesylate     increases_activity_of   CASP3   \n",
       "0    Imatinib mesylate      response_affected_by    ABL1   \n",
       "\n",
       "                                 e1        n2        n0_original_curie  \\\n",
       "174  gene_associated_with_condition  leukemia  PUBCHEM.COMPOUND:123596   \n",
       "315  gene_associated_with_condition  leukemia  PUBCHEM.COMPOUND:123596   \n",
       "187                 correlated_with  leukemia  PUBCHEM.COMPOUND:123596   \n",
       "191  gene_associated_with_condition  leukemia  PUBCHEM.COMPOUND:123596   \n",
       "199  gene_associated_with_condition  leukemia  PUBCHEM.COMPOUND:123596   \n",
       "..                              ...       ...                      ...   \n",
       "180  gene_associated_with_condition  leukemia  PUBCHEM.COMPOUND:123596   \n",
       "176                  contributes_to  leukemia  PUBCHEM.COMPOUND:123596   \n",
       "52                       related_to  leukemia  PUBCHEM.COMPOUND:123596   \n",
       "208  gene_associated_with_condition  leukemia  PUBCHEM.COMPOUND:123596   \n",
       "0                  affects_risk_for  leukemia  PUBCHEM.COMPOUND:123596   \n",
       "\n",
       "    n0_unified_curie n1_original_curie n1_unified_curie n2_original_curie  \\\n",
       "174  MESH:D000068877     NCBIGene:3576    NCBIGene:3576     MONDO:0005059   \n",
       "315  MESH:D000068877      NCBIGene:841     NCBIGene:841     MONDO:0005059   \n",
       "187  MESH:D000068877      NCBIGene:842     NCBIGene:842     MONDO:0005059   \n",
       "191  MESH:D000068877      NCBIGene:842     NCBIGene:842     MONDO:0005059   \n",
       "199  MESH:D000068877      NCBIGene:836     NCBIGene:836     MONDO:0005059   \n",
       "..               ...               ...              ...               ...   \n",
       "180  MESH:D000068877     NCBIGene:2875    NCBIGene:2875     MONDO:0005059   \n",
       "176  MESH:D000068877    NCBIGene:56616   NCBIGene:56616     MONDO:0005059   \n",
       "52   MESH:D000068877       NCBIGene:25      NCBIGene:25     MONDO:0005059   \n",
       "208  MESH:D000068877      NCBIGene:836     NCBIGene:836     MONDO:0005059   \n",
       "0    MESH:D000068877       NCBIGene:25      NCBIGene:25     MONDO:0005059   \n",
       "\n",
       "     ...                                         figure_url  \\\n",
       "174  ...  https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7...   \n",
       "315  ...  https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7...   \n",
       "187  ...  https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7...   \n",
       "191  ...  https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7...   \n",
       "199  ...  https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7...   \n",
       "..   ...                                                ...   \n",
       "180  ...  https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4...   \n",
       "176  ...  https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4...   \n",
       "52   ...  https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4...   \n",
       "208  ...  https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4...   \n",
       "0    ...  https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4...   \n",
       "\n",
       "                                          figure_title  figure_curie_count  \\\n",
       "174  EV trafficking between leukemia and stromal ce...                   9   \n",
       "315  Inhibitory roles of MSC-derived exosomes in tu...                  27   \n",
       "187  Inhibitory roles of MSC-derived exosomes in tu...                  27   \n",
       "191  Inhibitory roles of MSC-derived exosomes in tu...                  27   \n",
       "199  Inhibitory roles of MSC-derived exosomes in tu...                  27   \n",
       "..                                                 ...                 ...   \n",
       "180    Main pathways regulated by the BCR-ABL1 protein                  33   \n",
       "176    Main pathways regulated by the BCR-ABL1 protein                  33   \n",
       "52     Main pathways regulated by the BCR-ABL1 protein                  33   \n",
       "208    Main pathways regulated by the BCR-ABL1 protein                  33   \n",
       "0      Main pathways regulated by the BCR-ABL1 protein                  33   \n",
       "\n",
       "                  common_curie_set yes_trapi_result_yes_figure  \\\n",
       "174  {MESH:D007938, NCBIGene:3576}                           2   \n",
       "315   {MESH:D007938, NCBIGene:841}                           2   \n",
       "187   {MESH:D007938, NCBIGene:842}                           2   \n",
       "191   {MESH:D007938, NCBIGene:842}                           2   \n",
       "199   {MESH:D007938, NCBIGene:836}                           2   \n",
       "..                             ...                         ...   \n",
       "180                 {MESH:D007938}                           1   \n",
       "176                 {MESH:D007938}                           1   \n",
       "52                  {MESH:D007938}                           1   \n",
       "208                 {MESH:D007938}                           1   \n",
       "0                   {MESH:D007938}                           1   \n",
       "\n",
       "    yes_trapi_result_no_figure no_trapi_result_yes_figure  \\\n",
       "174                          1                          7   \n",
       "315                          1                         25   \n",
       "187                          1                         25   \n",
       "191                          1                         25   \n",
       "199                          1                         25   \n",
       "..                         ...                        ...   \n",
       "180                          2                         32   \n",
       "176                          2                         32   \n",
       "52                           2                         32   \n",
       "208                          2                         32   \n",
       "0                            2                         32   \n",
       "\n",
       "     no_trapi_result_no_figure   p_value  \\\n",
       "174                       1027  0.000200   \n",
       "315                       1009  0.001929   \n",
       "187                       1009  0.001929   \n",
       "191                       1009  0.001929   \n",
       "199                       1009  0.001929   \n",
       "..                         ...       ...   \n",
       "180                       1002  0.092548   \n",
       "176                       1002  0.092548   \n",
       "52                        1002  0.092548   \n",
       "208                       1002  0.092548   \n",
       "0                         1002  0.092548   \n",
       "\n",
       "                             trapi_result_curies_key  \n",
       "174   (MESH:D000068877, MESH:D007938, NCBIGene:3576)  \n",
       "315    (MESH:D000068877, MESH:D007938, NCBIGene:841)  \n",
       "187    (MESH:D000068877, MESH:D007938, NCBIGene:842)  \n",
       "191    (MESH:D000068877, MESH:D007938, NCBIGene:842)  \n",
       "199    (MESH:D000068877, MESH:D007938, NCBIGene:836)  \n",
       "..                                               ...  \n",
       "180   (MESH:D000068877, MESH:D007938, NCBIGene:2875)  \n",
       "176  (MESH:D000068877, MESH:D007938, NCBIGene:56616)  \n",
       "52      (MESH:D000068877, MESH:D007938, NCBIGene:25)  \n",
       "208    (MESH:D000068877, MESH:D007938, NCBIGene:836)  \n",
       "0       (MESH:D000068877, MESH:D007938, NCBIGene:25)  \n",
       "\n",
       "[316 rows x 25 columns]"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# This isn't really a significance threshold. It's more just intended to\n",
    "# exclude results where the p-value is 1 or very close to it.\n",
    "threshold = 0.9\n",
    "significant_trapi_results_with_figures_df = trapi_results_with_figures_df[\n",
    "    trapi_results_with_figures_df[\"p_value\"] < 0.9\n",
    "]\n",
    "\n",
    "# all\n",
    "cooccurrence_df = significant_trapi_results_with_figures_df.sort_values(\n",
    "    \"p_value\"\n",
    ")\n",
    "\n",
    "### first and any other query node\n",
    "##q_node_id_combination = (q_node_ids[0],)\n",
    "\n",
    "## first and second query nodes\n",
    "##q_node_id_combination = (q_node_ids[0], q_node_ids[1])\n",
    "\n",
    "### first and last query nodes\n",
    "##q_node_id_combination = (q_node_ids[0], q_node_ids[-1])\n",
    "\n",
    "### second and last query nodes\n",
    "##q_node_id_combination = (q_node_ids[1], q_node_ids[-1])\n",
    "\n",
    "### every query node with 'ids' specified\n",
    "##q_node_id_combination = query_nodes_with_ids\n",
    "\n",
    "#print(f'{\" & \".join(q_node_id_combination)}')\n",
    "#s = pd.Series(\n",
    "#    [True] * len(significant_trapi_results_with_figures_df),\n",
    "#    index=significant_trapi_results_with_figures_dfindex\n",
    "#)\n",
    "#for q_node_id in q_node_id_combination:\n",
    "#    s = s & significant_trapi_results_with_figures_df.apply(\n",
    "#        lambda r: r[f'{q_node_id}_unified_curie'] in r[\"common_curie_set\"], axis=1\n",
    "#    )\n",
    "#cooccurrence_df = significant_trapi_results_with_figures_df[s == True].sort_values(\n",
    "#    \"p_value\"\n",
    "#)\n",
    "\n",
    "cooccurrence_df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "\n",
       "<div style=\"border: 1px solid slategray; padding: 6px;\">\n",
       "    <figure style=\"margin: 5px !important;\">\n",
       "        <a target=\"_blank\" href=\"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7033644/\">\n",
       "          <img src=\"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7033644/bin/fonc-10-00090-g0002.jpg\" style=\"max-height: 500px; max-width: 100%;\">\n",
       "        </a>\n",
       "        <br>\n",
       "        <caption>\n",
       "            EV trafficking between leukemia and stromal cells reduces sensitivity to chemotherapeutics\n",
       "        </caption>\n",
       "    </figure>\n",
       "</div>\n"
      ],
      "text/plain": [
       "<IPython.core.display.HTML object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "\n",
       "<div>\n",
       "    <span style=\"font-weight: bold; font-size: small;\">\n",
       "        TRAPI result MESH:D000068877, NCBIGene:3576, MESH:D007938\n",
       "    </span> \n",
       "    (p-value: 2.0e-04)\n",
       "    <br>\n",
       "    Imatinib mesylate - increases_secretion_of - \n",
       "<span style=\"border: 1px orange solid; padding: 1px; border-radius: 4px;\">CXCL8</span>\n",
       " - gene_associated_with_condition - \n",
       "<span style=\"border: 1px orange solid; padding: 1px; border-radius: 4px;\">leukemia</span>\n",
       "</span>\n",
       "</div>\n",
       "<br>\n",
       "<div>\n",
       "    <span style=\"font-weight: bold; font-size: small;\">\n",
       "        TRAPI result MESH:D000068877, NCBIGene:7422, MESH:D007938\n",
       "    </span> \n",
       "    (p-value: 2.6e-02)\n",
       "    <br>\n",
       "    Imatinib mesylate - decreases_secretion_of - VEGFA - gene_associated_with_condition - \n",
       "<span style=\"border: 1px orange solid; padding: 1px; border-radius: 4px;\">leukemia</span>\n",
       "</span><br>Imatinib mesylate - decreases_secretion_of - VEGFA - contributes_to - \n",
       "<span style=\"border: 1px orange solid; padding: 1px; border-radius: 4px;\">leukemia</span>\n",
       "</span><br>Imatinib mesylate - increases_secretion_of - VEGFA - contributes_to - \n",
       "<span style=\"border: 1px orange solid; padding: 1px; border-radius: 4px;\">leukemia</span>\n",
       "</span><br>Imatinib mesylate - increases_secretion_of - VEGFA - gene_associated_with_condition - \n",
       "<span style=\"border: 1px orange solid; padding: 1px; border-radius: 4px;\">leukemia</span>\n",
       "</span>\n",
       "</div>\n",
       "<br>\n",
       "<div>\n",
       "    <span style=\"font-weight: bold; font-size: small;\">\n",
       "        TRAPI result MESH:D000068877, NCBIGene:841, MESH:D007938\n",
       "    </span> \n",
       "    (p-value: 2.6e-02)\n",
       "    <br>\n",
       "    Imatinib mesylate - increases_activity_of - CASP8 - gene_associated_with_condition - \n",
       "<span style=\"border: 1px orange solid; padding: 1px; border-radius: 4px;\">leukemia</span>\n",
       "</span><br>Imatinib mesylate - increases_activity_of - CASP8 - correlated_with - \n",
       "<span style=\"border: 1px orange solid; padding: 1px; border-radius: 4px;\">leukemia</span>\n",
       "</span>\n",
       "</div>\n",
       "<br>\n",
       "<div>\n",
       "    <span style=\"font-weight: bold; font-size: small;\">\n",
       "        TRAPI result MESH:D000068877, NCBIGene:4067, MESH:D007938\n",
       "    </span> \n",
       "    (p-value: 2.6e-02)\n",
       "    <br>\n",
       "    Imatinib mesylate - response_decreased_by - LYN - gene_associated_with_condition - \n",
       "<span style=\"border: 1px orange solid; padding: 1px; border-radius: 4px;\">leukemia</span>\n",
       "</span><br>Imatinib mesylate - response_affected_by - LYN - affects - \n",
       "<span style=\"border: 1px orange solid; padding: 1px; border-radius: 4px;\">leukemia</span>\n",
       "</span><br>Imatinib mesylate - response_affected_by - LYN - gene_associated_with_condition - \n",
       "<span style=\"border: 1px orange solid; padding: 1px; border-radius: 4px;\">leukemia</span>\n",
       "</span><br>Imatinib mesylate - response_decreased_by - LYN - affects - \n",
       "<span style=\"border: 1px orange solid; padding: 1px; border-radius: 4px;\">leukemia</span>\n",
       "</span>\n",
       "</div>\n",
       "<br>\n",
       "<div>\n",
       "    <span style=\"font-weight: bold; font-size: small;\">\n",
       "        TRAPI result MESH:D000068877, NCBIGene:836, MESH:D007938\n",
       "    </span> \n",
       "    (p-value: 2.6e-02)\n",
       "    <br>\n",
       "    Imatinib mesylate - increases_degradation_of - CASP3 - gene_associated_with_condition - \n",
       "<span style=\"border: 1px orange solid; padding: 1px; border-radius: 4px;\">leukemia</span>\n",
       "</span><br>Imatinib mesylate - increases_activity_of - CASP3 - contributes_to - \n",
       "<span style=\"border: 1px orange solid; padding: 1px; border-radius: 4px;\">leukemia</span>\n",
       "</span><br>Imatinib mesylate - increases_degradation_of - CASP3 - contributes_to - \n",
       "<span style=\"border: 1px orange solid; padding: 1px; border-radius: 4px;\">leukemia</span>\n",
       "</span><br>Imatinib mesylate - increases_degradation_of - CASP3 - correlated_with - \n",
       "<span style=\"border: 1px orange solid; padding: 1px; border-radius: 4px;\">leukemia</span>\n",
       "</span><br>Imatinib mesylate - increases_activity_of - CASP3 - correlated_with - \n",
       "<span style=\"border: 1px orange solid; padding: 1px; border-radius: 4px;\">leukemia</span>\n",
       "</span><br>Imatinib mesylate - increases_activity_of - CASP3 - gene_associated_with_condition - \n",
       "<span style=\"border: 1px orange solid; padding: 1px; border-radius: 4px;\">leukemia</span>\n",
       "</span>\n",
       "</div>\n",
       "<br>\n",
       "<div>\n",
       "    <span style=\"font-weight: bold; font-size: small;\">\n",
       "        TRAPI result MESH:D000068877, NCBIGene:9429, MESH:D007938\n",
       "    </span> \n",
       "    (p-value: 2.6e-02)\n",
       "    <br>\n",
       "    Imatinib mesylate - increases_activity_of - ABCG2 - contributes_to - \n",
       "<span style=\"border: 1px orange solid; padding: 1px; border-radius: 4px;\">leukemia</span>\n",
       "</span><br>Imatinib mesylate - increases_activity_of - ABCG2 - gene_associated_with_condition - \n",
       "<span style=\"border: 1px orange solid; padding: 1px; border-radius: 4px;\">leukemia</span>\n",
       "</span>\n",
       "</div>\n",
       "<br>\n",
       "<div>\n",
       "    <span style=\"font-weight: bold; font-size: small;\">\n",
       "        TRAPI result MESH:D000068877, NCBIGene:5156, MESH:D007938\n",
       "    </span> \n",
       "    (p-value: 2.6e-02)\n",
       "    <br>\n",
       "    Imatinib mesylate - response_increased_by - PDGFRA - gene_associated_with_condition - \n",
       "<span style=\"border: 1px orange solid; padding: 1px; border-radius: 4px;\">leukemia</span>\n",
       "</span><br>Imatinib mesylate - response_affected_by - PDGFRA - gene_associated_with_condition - \n",
       "<span style=\"border: 1px orange solid; padding: 1px; border-radius: 4px;\">leukemia</span>\n",
       "</span><br>Imatinib mesylate - decreases_expression_of - PDGFRA - gene_associated_with_condition - \n",
       "<span style=\"border: 1px orange solid; padding: 1px; border-radius: 4px;\">leukemia</span>\n",
       "</span>\n",
       "</div>\n",
       "<br>\n",
       "<div>\n",
       "    <span style=\"font-weight: bold; font-size: small;\">\n",
       "        TRAPI result MESH:D000068877, NCBIGene:5243, MESH:D007938\n",
       "    </span> \n",
       "    (p-value: 2.6e-02)\n",
       "    <br>\n",
       "    Imatinib mesylate - response_decreased_by - ABCB1 - correlated_with - \n",
       "<span style=\"border: 1px orange solid; padding: 1px; border-radius: 4px;\">leukemia</span>\n",
       "</span><br>Imatinib mesylate - response_decreased_by - ABCB1 - related_to - \n",
       "<span style=\"border: 1px orange solid; padding: 1px; border-radius: 4px;\">leukemia</span>\n",
       "</span><br>Imatinib mesylate - response_decreased_by - ABCB1 - part_of - \n",
       "<span style=\"border: 1px orange solid; padding: 1px; border-radius: 4px;\">leukemia</span>\n",
       "</span><br>Imatinib mesylate - response_affected_by - ABCB1 - affects_risk_for - \n",
       "<span style=\"border: 1px orange solid; padding: 1px; border-radius: 4px;\">leukemia</span>\n",
       "</span><br>Imatinib mesylate - response_affected_by - ABCB1 - gene_associated_with_condition - \n",
       "<span style=\"border: 1px orange solid; padding: 1px; border-radius: 4px;\">leukemia</span>\n",
       "</span><br>Imatinib mesylate - response_decreased_by - ABCB1 - affects_risk_for - \n",
       "<span style=\"border: 1px orange solid; padding: 1px; border-radius: 4px;\">leukemia</span>\n",
       "</span><br>Imatinib mesylate - response_affected_by - ABCB1 - related_to - \n",
       "<span style=\"border: 1px orange solid; padding: 1px; border-radius: 4px;\">leukemia</span>\n",
       "</span><br>Imatinib mesylate - response_affected_by - ABCB1 - correlated_with - \n",
       "<span style=\"border: 1px orange solid; padding: 1px; border-radius: 4px;\">leukemia</span>\n",
       "</span><br>Imatinib mesylate - response_affected_by - ABCB1 - part_of - \n",
       "<span style=\"border: 1px orange solid; padding: 1px; border-radius: 4px;\">leukemia</span>\n",
       "</span><br>Imatinib mesylate - response_decreased_by - ABCB1 - gene_associated_with_condition - \n",
       "<span style=\"border: 1px orange solid; padding: 1px; border-radius: 4px;\">leukemia</span>\n",
       "</span>\n",
       "</div>\n",
       "<br>\n",
       "<div>\n",
       "    <span style=\"font-weight: bold; font-size: small;\">\n",
       "        TRAPI result MESH:D000068877, NCBIGene:25, MESH:D007938\n",
       "    </span> \n",
       "    (p-value: 2.6e-02)\n",
       "    <br>\n",
       "    Imatinib mesylate - response_decreased_by - ABL1 - correlated_with - \n",
       "<span style=\"border: 1px orange solid; padding: 1px; border-radius: 4px;\">leukemia</span>\n",
       "</span><br>Imatinib mesylate - response_increased_by - ABL1 - contributes_to - \n",
       "<span style=\"border: 1px orange solid; padding: 1px; border-radius: 4px;\">leukemia</span>\n",
       "</span><br>Imatinib mesylate - response_affected_by - ABL1 - contributes_to - \n",
       "<span style=\"border: 1px orange solid; padding: 1px; border-radius: 4px;\">leukemia</span>\n",
       "</span><br>Imatinib mesylate - response_increased_by - ABL1 - related_to - \n",
       "<span style=\"border: 1px orange solid; padding: 1px; border-radius: 4px;\">leukemia</span>\n",
       "</span><br>Imatinib mesylate - response_decreased_by - ABL1 - contributes_to - \n",
       "<span style=\"border: 1px orange solid; padding: 1px; border-radius: 4px;\">leukemia</span>\n",
       "</span><br>Imatinib mesylate - response_increased_by - ABL1 - correlated_with - \n",
       "<span style=\"border: 1px orange solid; padding: 1px; border-radius: 4px;\">leukemia</span>\n",
       "</span><br>Imatinib mesylate - response_decreased_by - ABL1 - affects_risk_for - \n",
       "<span style=\"border: 1px orange solid; padding: 1px; border-radius: 4px;\">leukemia</span>\n",
       "</span><br>Imatinib mesylate - response_decreased_by - ABL1 - gene_associated_with_condition - \n",
       "<span style=\"border: 1px orange solid; padding: 1px; border-radius: 4px;\">leukemia</span>\n",
       "</span><br>Imatinib mesylate - response_affected_by - ABL1 - related_to - \n",
       "<span style=\"border: 1px orange solid; padding: 1px; border-radius: 4px;\">leukemia</span>\n",
       "</span><br>Imatinib mesylate - response_decreased_by - ABL1 - related_to - \n",
       "<span style=\"border: 1px orange solid; padding: 1px; border-radius: 4px;\">leukemia</span>\n",
       "</span><br>Imatinib mesylate - decreases_molecular_modification_of - ABL1 - correlated_with - \n",
       "<span style=\"border: 1px orange solid; padding: 1px; border-radius: 4px;\">leukemia</span>\n",
       "</span><br>Imatinib mesylate - response_increased_by - ABL1 - affects_risk_for - \n",
       "<span style=\"border: 1px orange solid; padding: 1px; border-radius: 4px;\">leukemia</span>\n",
       "</span><br>Imatinib mesylate - decreases_molecular_modification_of - ABL1 - contributes_to - \n",
       "<span style=\"border: 1px orange solid; padding: 1px; border-radius: 4px;\">leukemia</span>\n",
       "</span><br>Imatinib mesylate - decreases_activity_of - ABL1 - correlated_with - \n",
       "<span style=\"border: 1px orange solid; padding: 1px; border-radius: 4px;\">leukemia</span>\n",
       "</span><br>Imatinib mesylate - decreases_molecular_modification_of - ABL1 - gene_associated_with_condition - \n",
       "<span style=\"border: 1px orange solid; padding: 1px; border-radius: 4px;\">leukemia</span>\n",
       "</span><br>Imatinib mesylate - decreases_activity_of - ABL1 - contributes_to - \n",
       "<span style=\"border: 1px orange solid; padding: 1px; border-radius: 4px;\">leukemia</span>\n",
       "</span><br>Imatinib mesylate - decreases_molecular_modification_of - ABL1 - related_to - \n",
       "<span style=\"border: 1px orange solid; padding: 1px; border-radius: 4px;\">leukemia</span>\n",
       "</span><br>Imatinib mesylate - decreases_activity_of - ABL1 - gene_associated_with_condition - \n",
       "<span style=\"border: 1px orange solid; padding: 1px; border-radius: 4px;\">leukemia</span>\n",
       "</span><br>Imatinib mesylate - response_affected_by - ABL1 - affects_risk_for - \n",
       "<span style=\"border: 1px orange solid; padding: 1px; border-radius: 4px;\">leukemia</span>\n",
       "</span><br>Imatinib mesylate - decreases_activity_of - ABL1 - affects_risk_for - \n",
       "<span style=\"border: 1px orange solid; padding: 1px; border-radius: 4px;\">leukemia</span>\n",
       "</span><br>Imatinib mesylate - response_affected_by - ABL1 - correlated_with - \n",
       "<span style=\"border: 1px orange solid; padding: 1px; border-radius: 4px;\">leukemia</span>\n",
       "</span><br>Imatinib mesylate - decreases_activity_of - ABL1 - related_to - \n",
       "<span style=\"border: 1px orange solid; padding: 1px; border-radius: 4px;\">leukemia</span>\n",
       "</span><br>Imatinib mesylate - decreases_molecular_modification_of - ABL1 - affects_risk_for - \n",
       "<span style=\"border: 1px orange solid; padding: 1px; border-radius: 4px;\">leukemia</span>\n",
       "</span><br>Imatinib mesylate - response_affected_by - ABL1 - gene_associated_with_condition - \n",
       "<span style=\"border: 1px orange solid; padding: 1px; border-radius: 4px;\">leukemia</span>\n",
       "</span><br>Imatinib mesylate - response_increased_by - ABL1 - gene_associated_with_condition - \n",
       "<span style=\"border: 1px orange solid; padding: 1px; border-radius: 4px;\">leukemia</span>\n",
       "</span>\n",
       "</div>\n",
       "<br>\n",
       "<div>\n",
       "    <span style=\"font-weight: bold; font-size: small;\">\n",
       "        TRAPI result MESH:D000068877, NCBIGene:1021, MESH:D007938\n",
       "    </span> \n",
       "    (p-value: 2.6e-02)\n",
       "    <br>\n",
       "    Imatinib mesylate - response_affected_by - CDK6 - gene_associated_with_condition - \n",
       "<span style=\"border: 1px orange solid; padding: 1px; border-radius: 4px;\">leukemia</span>\n",
       "</span><br>Imatinib mesylate - response_decreased_by - CDK6 - gene_associated_with_condition - \n",
       "<span style=\"border: 1px orange solid; padding: 1px; border-radius: 4px;\">leukemia</span>\n",
       "</span>\n",
       "</div>\n"
      ],
      "text/plain": [
       "<IPython.core.display.HTML object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "...\n",
      "\n",
      "plus 10 more TRAPI results\n",
      "\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "\n",
       "<div style=\"border: 1px solid slategray; padding: 6px;\">\n",
       "    <figure style=\"margin: 5px !important;\">\n",
       "        <a target=\"_blank\" href=\"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7499322/\">\n",
       "          <img src=\"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7499322/bin/SCI2020-8844730.003.jpg\" style=\"max-height: 500px; max-width: 100%;\">\n",
       "        </a>\n",
       "        <br>\n",
       "        <caption>\n",
       "            Inhibitory roles of MSC-derived exosomes in tumors\n",
       "        </caption>\n",
       "    </figure>\n",
       "</div>\n"
      ],
      "text/plain": [
       "<IPython.core.display.HTML object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
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    {
     "data": {
      "text/html": [
       "\n",
       "<div>\n",
       "    <span style=\"font-weight: bold; font-size: small;\">\n",
       "        TRAPI result MESH:D000068877, NCBIGene:841, MESH:D007938\n",
       "    </span> \n",
       "    (p-value: 1.9e-03)\n",
       "    <br>\n",
       "    Imatinib mesylate - increases_activity_of - \n",
       "<span style=\"border: 1px orange solid; padding: 1px; border-radius: 4px;\">CASP8</span>\n",
       " - gene_associated_with_condition - \n",
       "<span style=\"border: 1px orange solid; padding: 1px; border-radius: 4px;\">leukemia</span>\n",
       "</span><br>Imatinib mesylate - increases_activity_of - \n",
       "<span style=\"border: 1px orange solid; padding: 1px; border-radius: 4px;\">CASP8</span>\n",
       " - correlated_with - \n",
       "<span style=\"border: 1px orange solid; padding: 1px; border-radius: 4px;\">leukemia</span>\n",
       "</span>\n",
       "</div>\n",
       "<br>\n",
       "<div>\n",
       "    <span style=\"font-weight: bold; font-size: small;\">\n",
       "        TRAPI result MESH:D000068877, NCBIGene:842, MESH:D007938\n",
       "    </span> \n",
       "    (p-value: 1.9e-03)\n",
       "    <br>\n",
       "    Imatinib mesylate - increases_degradation_of - \n",
       "<span style=\"border: 1px orange solid; padding: 1px; border-radius: 4px;\">CASP9</span>\n",
       " - correlated_with - \n",
       "<span style=\"border: 1px orange solid; padding: 1px; border-radius: 4px;\">leukemia</span>\n",
       "</span><br>Imatinib mesylate - increases_degradation_of - \n",
       "<span style=\"border: 1px orange solid; padding: 1px; border-radius: 4px;\">CASP9</span>\n",
       " - gene_associated_with_condition - \n",
       "<span style=\"border: 1px orange solid; padding: 1px; border-radius: 4px;\">leukemia</span>\n",
       "</span>\n",
       "</div>\n",
       "<br>\n",
       "<div>\n",
       "    <span style=\"font-weight: bold; font-size: small;\">\n",
       "        TRAPI result MESH:D000068877, NCBIGene:836, MESH:D007938\n",
       "    </span> \n",
       "    (p-value: 1.9e-03)\n",
       "    <br>\n",
       "    Imatinib mesylate - increases_degradation_of - \n",
       "<span style=\"border: 1px orange solid; padding: 1px; border-radius: 4px;\">CASP3</span>\n",
       " - gene_associated_with_condition - \n",
       "<span style=\"border: 1px orange solid; padding: 1px; border-radius: 4px;\">leukemia</span>\n",
       "</span><br>Imatinib mesylate - increases_degradation_of - \n",
       "<span style=\"border: 1px orange solid; padding: 1px; border-radius: 4px;\">CASP3</span>\n",
       " - correlated_with - \n",
       "<span style=\"border: 1px orange solid; padding: 1px; border-radius: 4px;\">leukemia</span>\n",
       "</span><br>Imatinib mesylate - increases_activity_of - \n",
       "<span style=\"border: 1px orange solid; padding: 1px; border-radius: 4px;\">CASP3</span>\n",
       " - gene_associated_with_condition - \n",
       "<span style=\"border: 1px orange solid; padding: 1px; border-radius: 4px;\">leukemia</span>\n",
       "</span><br>Imatinib mesylate - increases_activity_of - \n",
       "<span style=\"border: 1px orange solid; padding: 1px; border-radius: 4px;\">CASP3</span>\n",
       " - correlated_with - \n",
       "<span style=\"border: 1px orange solid; padding: 1px; border-radius: 4px;\">leukemia</span>\n",
       "</span><br>Imatinib mesylate - increases_activity_of - \n",
       "<span style=\"border: 1px orange solid; padding: 1px; border-radius: 4px;\">CASP3</span>\n",
       " - contributes_to - \n",
       "<span style=\"border: 1px orange solid; padding: 1px; border-radius: 4px;\">leukemia</span>\n",
       "</span><br>Imatinib mesylate - increases_degradation_of - \n",
       "<span style=\"border: 1px orange solid; padding: 1px; border-radius: 4px;\">CASP3</span>\n",
       " - contributes_to - \n",
       "<span style=\"border: 1px orange solid; padding: 1px; border-radius: 4px;\">leukemia</span>\n",
       "</span>\n",
       "</div>\n",
       "<br>\n",
       "<div>\n",
       "    <span style=\"font-weight: bold; font-size: small;\">\n",
       "        TRAPI result MESH:D000068877, NCBIGene:25, MESH:D007938\n",
       "    </span> \n",
       "    (p-value: 7.6e-02)\n",
       "    <br>\n",
       "    Imatinib mesylate - response_affected_by - ABL1 - correlated_with - \n",
       "<span style=\"border: 1px orange solid; padding: 1px; border-radius: 4px;\">leukemia</span>\n",
       "</span><br>Imatinib mesylate - response_increased_by - ABL1 - contributes_to - \n",
       "<span style=\"border: 1px orange solid; padding: 1px; border-radius: 4px;\">leukemia</span>\n",
       "</span><br>Imatinib mesylate - response_increased_by - ABL1 - gene_associated_with_condition - \n",
       "<span style=\"border: 1px orange solid; padding: 1px; border-radius: 4px;\">leukemia</span>\n",
       "</span><br>Imatinib mesylate - response_increased_by - ABL1 - affects_risk_for - \n",
       "<span style=\"border: 1px orange solid; padding: 1px; border-radius: 4px;\">leukemia</span>\n",
       "</span><br>Imatinib mesylate - response_increased_by - ABL1 - correlated_with - \n",
       "<span style=\"border: 1px orange solid; padding: 1px; border-radius: 4px;\">leukemia</span>\n",
       "</span><br>Imatinib mesylate - response_affected_by - ABL1 - related_to - \n",
       "<span style=\"border: 1px orange solid; padding: 1px; border-radius: 4px;\">leukemia</span>\n",
       "</span><br>Imatinib mesylate - response_decreased_by - ABL1 - affects_risk_for - \n",
       "<span style=\"border: 1px orange solid; padding: 1px; border-radius: 4px;\">leukemia</span>\n",
       "</span><br>Imatinib mesylate - response_increased_by - ABL1 - related_to - \n",
       "<span style=\"border: 1px orange solid; padding: 1px; border-radius: 4px;\">leukemia</span>\n",
       "</span><br>Imatinib mesylate - decreases_molecular_modification_of - ABL1 - affects_risk_for - \n",
       "<span style=\"border: 1px orange solid; padding: 1px; border-radius: 4px;\">leukemia</span>\n",
       "</span><br>Imatinib mesylate - response_affected_by - ABL1 - gene_associated_with_condition - \n",
       "<span style=\"border: 1px orange solid; padding: 1px; border-radius: 4px;\">leukemia</span>\n",
       "</span><br>Imatinib mesylate - decreases_molecular_modification_of - ABL1 - gene_associated_with_condition - \n",
       "<span style=\"border: 1px orange solid; padding: 1px; border-radius: 4px;\">leukemia</span>\n",
       "</span><br>Imatinib mesylate - decreases_molecular_modification_of - ABL1 - contributes_to - \n",
       "<span style=\"border: 1px orange solid; padding: 1px; border-radius: 4px;\">leukemia</span>\n",
       "</span><br>Imatinib mesylate - decreases_activity_of - ABL1 - correlated_with - \n",
       "<span style=\"border: 1px orange solid; padding: 1px; border-radius: 4px;\">leukemia</span>\n",
       "</span><br>Imatinib mesylate - decreases_activity_of - ABL1 - related_to - \n",
       "<span style=\"border: 1px orange solid; padding: 1px; border-radius: 4px;\">leukemia</span>\n",
       "</span><br>Imatinib mesylate - response_affected_by - ABL1 - affects_risk_for - \n",
       "<span style=\"border: 1px orange solid; padding: 1px; border-radius: 4px;\">leukemia</span>\n",
       "</span><br>Imatinib mesylate - decreases_activity_of - ABL1 - contributes_to - \n",
       "<span style=\"border: 1px orange solid; padding: 1px; border-radius: 4px;\">leukemia</span>\n",
       "</span><br>Imatinib mesylate - decreases_activity_of - ABL1 - gene_associated_with_condition - \n",
       "<span style=\"border: 1px orange solid; padding: 1px; border-radius: 4px;\">leukemia</span>\n",
       "</span><br>Imatinib mesylate - decreases_molecular_modification_of - ABL1 - related_to - \n",
       "<span style=\"border: 1px orange solid; padding: 1px; border-radius: 4px;\">leukemia</span>\n",
       "</span><br>Imatinib mesylate - decreases_activity_of - ABL1 - affects_risk_for - \n",
       "<span style=\"border: 1px orange solid; padding: 1px; border-radius: 4px;\">leukemia</span>\n",
       "</span><br>Imatinib mesylate - decreases_molecular_modification_of - ABL1 - correlated_with - \n",
       "<span style=\"border: 1px orange solid; padding: 1px; border-radius: 4px;\">leukemia</span>\n",
       "</span><br>Imatinib mesylate - response_decreased_by - ABL1 - contributes_to - \n",
       "<span style=\"border: 1px orange solid; padding: 1px; border-radius: 4px;\">leukemia</span>\n",
       "</span><br>Imatinib mesylate - response_affected_by - ABL1 - contributes_to - \n",
       "<span style=\"border: 1px orange solid; padding: 1px; border-radius: 4px;\">leukemia</span>\n",
       "</span><br>Imatinib mesylate - response_decreased_by - ABL1 - gene_associated_with_condition - \n",
       "<span style=\"border: 1px orange solid; padding: 1px; border-radius: 4px;\">leukemia</span>\n",
       "</span><br>Imatinib mesylate - response_decreased_by - ABL1 - related_to - \n",
       "<span style=\"border: 1px orange solid; padding: 1px; border-radius: 4px;\">leukemia</span>\n",
       "</span><br>Imatinib mesylate - response_decreased_by - ABL1 - correlated_with - \n",
       "<span style=\"border: 1px orange solid; padding: 1px; border-radius: 4px;\">leukemia</span>\n",
       "</span>\n",
       "</div>\n",
       "<br>\n",
       "<div>\n",
       "    <span style=\"font-weight: bold; font-size: small;\">\n",
       "        TRAPI result MESH:D000068877, NCBIGene:81608, MESH:D007938\n",
       "    </span> \n",
       "    (p-value: 7.6e-02)\n",
       "    <br>\n",
       "    Imatinib mesylate - response_increased_by - FIP1L1 - contributes_to - \n",
       "<span style=\"border: 1px orange solid; padding: 1px; border-radius: 4px;\">leukemia</span>\n",
       "</span><br>Imatinib mesylate - response_affected_by - FIP1L1 - contributes_to - \n",
       "<span style=\"border: 1px orange solid; padding: 1px; border-radius: 4px;\">leukemia</span>\n",
       "</span><br>Imatinib mesylate - response_affected_by - FIP1L1 - gene_associated_with_condition - \n",
       "<span style=\"border: 1px orange solid; padding: 1px; border-radius: 4px;\">leukemia</span>\n",
       "</span><br>Imatinib mesylate - response_increased_by - FIP1L1 - gene_associated_with_condition - \n",
       "<span style=\"border: 1px orange solid; padding: 1px; border-radius: 4px;\">leukemia</span>\n",
       "</span>\n",
       "</div>\n",
       "<br>\n",
       "<div>\n",
       "    <span style=\"font-weight: bold; font-size: small;\">\n",
       "        TRAPI result MESH:D000068877, NCBIGene:64321, MESH:D007938\n",
       "    </span> \n",
       "    (p-value: 7.6e-02)\n",
       "    <br>\n",
       "    Imatinib mesylate - decreases_localization_of - SOX17 - contributes_to - \n",
       "<span style=\"border: 1px orange solid; padding: 1px; border-radius: 4px;\">leukemia</span>\n",
       "</span>\n",
       "</div>\n",
       "<br>\n",
       "<div>\n",
       "    <span style=\"font-weight: bold; font-size: small;\">\n",
       "        TRAPI result MESH:D000068877, NCBIGene:207, MESH:D007938\n",
       "    </span> \n",
       "    (p-value: 7.6e-02)\n",
       "    <br>\n",
       "    Imatinib mesylate - decreases_molecular_modification_of - AKT1 - entity_positively_regulates_entity - \n",
       "<span style=\"border: 1px orange solid; padding: 1px; border-radius: 4px;\">leukemia</span>\n",
       "</span><br>Imatinib mesylate - decreases_molecular_modification_of - AKT1 - contributes_to - \n",
       "<span style=\"border: 1px orange solid; padding: 1px; border-radius: 4px;\">leukemia</span>\n",
       "</span><br>Imatinib mesylate - decreases_molecular_modification_of - AKT1 - related_to - \n",
       "<span style=\"border: 1px orange solid; padding: 1px; border-radius: 4px;\">leukemia</span>\n",
       "</span><br>Imatinib mesylate - decreases_molecular_modification_of - AKT1 - gene_associated_with_condition - \n",
       "<span style=\"border: 1px orange solid; padding: 1px; border-radius: 4px;\">leukemia</span>\n",
       "</span>\n",
       "</div>\n",
       "<br>\n",
       "<div>\n",
       "    <span style=\"font-weight: bold; font-size: small;\">\n",
       "        TRAPI result MESH:D000068877, NCBIGene:5156, MESH:D007938\n",
       "    </span> \n",
       "    (p-value: 7.6e-02)\n",
       "    <br>\n",
       "    Imatinib mesylate - response_affected_by - PDGFRA - gene_associated_with_condition - \n",
       "<span style=\"border: 1px orange solid; padding: 1px; border-radius: 4px;\">leukemia</span>\n",
       "</span><br>Imatinib mesylate - decreases_expression_of - PDGFRA - gene_associated_with_condition - \n",
       "<span style=\"border: 1px orange solid; padding: 1px; border-radius: 4px;\">leukemia</span>\n",
       "</span><br>Imatinib mesylate - response_increased_by - PDGFRA - gene_associated_with_condition - \n",
       "<span style=\"border: 1px orange solid; padding: 1px; border-radius: 4px;\">leukemia</span>\n",
       "</span>\n",
       "</div>\n",
       "<br>\n",
       "<div>\n",
       "    <span style=\"font-weight: bold; font-size: small;\">\n",
       "        TRAPI result MESH:D000068877, NCBIGene:9467, MESH:D007938\n",
       "    </span> \n",
       "    (p-value: 7.6e-02)\n",
       "    <br>\n",
       "    Imatinib mesylate - response_increased_by - SH3BP5 - contributes_to - \n",
       "<span style=\"border: 1px orange solid; padding: 1px; border-radius: 4px;\">leukemia</span>\n",
       "</span><br>Imatinib mesylate - response_affected_by - SH3BP5 - contributes_to - \n",
       "<span style=\"border: 1px orange solid; padding: 1px; border-radius: 4px;\">leukemia</span>\n",
       "</span>\n",
       "</div>\n",
       "<br>\n",
       "<div>\n",
       "    <span style=\"font-weight: bold; font-size: small;\">\n",
       "        TRAPI result MESH:D000068877, NCBIGene:4067, MESH:D007938\n",
       "    </span> \n",
       "    (p-value: 7.6e-02)\n",
       "    <br>\n",
       "    Imatinib mesylate - response_decreased_by - LYN - affects - \n",
       "<span style=\"border: 1px orange solid; padding: 1px; border-radius: 4px;\">leukemia</span>\n",
       "</span><br>Imatinib mesylate - response_affected_by - LYN - affects - \n",
       "<span style=\"border: 1px orange solid; padding: 1px; border-radius: 4px;\">leukemia</span>\n",
       "</span><br>Imatinib mesylate - response_affected_by - LYN - gene_associated_with_condition - \n",
       "<span style=\"border: 1px orange solid; padding: 1px; border-radius: 4px;\">leukemia</span>\n",
       "</span><br>Imatinib mesylate - response_decreased_by - LYN - gene_associated_with_condition - \n",
       "<span style=\"border: 1px orange solid; padding: 1px; border-radius: 4px;\">leukemia</span>\n",
       "</span>\n",
       "</div>\n"
      ],
      "text/plain": [
       "<IPython.core.display.HTML object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "...\n",
      "\n",
      "plus 10 more TRAPI results\n",
      "\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "\n",
       "<div style=\"border: 1px solid slategray; padding: 6px;\">\n",
       "    <figure style=\"margin: 5px !important;\">\n",
       "        <a target=\"_blank\" href=\"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4896164/\">\n",
       "          <img src=\"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4896164/bin/40880_2016_108_Fig2_HTML.jpg\" style=\"max-height: 500px; max-width: 100%;\">\n",
       "        </a>\n",
       "        <br>\n",
       "        <caption>\n",
       "            Main pathways regulated by the BCR-ABL1 protein\n",
       "        </caption>\n",
       "    </figure>\n",
       "</div>\n"
      ],
      "text/plain": [
       "<IPython.core.display.HTML object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
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    {
     "data": {
      "text/html": [
       "\n",
       "<div>\n",
       "    <span style=\"font-weight: bold; font-size: small;\">\n",
       "        TRAPI result MESH:D000068877, NCBIGene:207, MESH:D007938\n",
       "    </span> \n",
       "    (p-value: 2.9e-03)\n",
       "    <br>\n",
       "    Imatinib mesylate - decreases_molecular_modification_of - \n",
       "<span style=\"border: 1px orange solid; padding: 1px; border-radius: 4px;\">AKT1</span>\n",
       " - gene_associated_with_condition - \n",
       "<span style=\"border: 1px orange solid; padding: 1px; border-radius: 4px;\">leukemia</span>\n",
       "</span><br>Imatinib mesylate - decreases_molecular_modification_of - \n",
       "<span style=\"border: 1px orange solid; padding: 1px; border-radius: 4px;\">AKT1</span>\n",
       " - entity_positively_regulates_entity - \n",
       "<span style=\"border: 1px orange solid; padding: 1px; border-radius: 4px;\">leukemia</span>\n",
       "</span><br>Imatinib mesylate - decreases_molecular_modification_of - \n",
       "<span style=\"border: 1px orange solid; padding: 1px; border-radius: 4px;\">AKT1</span>\n",
       " - contributes_to - \n",
       "<span style=\"border: 1px orange solid; padding: 1px; border-radius: 4px;\">leukemia</span>\n",
       "</span><br>Imatinib mesylate - decreases_molecular_modification_of - \n",
       "<span style=\"border: 1px orange solid; padding: 1px; border-radius: 4px;\">AKT1</span>\n",
       " - related_to - \n",
       "<span style=\"border: 1px orange solid; padding: 1px; border-radius: 4px;\">leukemia</span>\n",
       "</span>\n",
       "</div>\n",
       "<br>\n",
       "<div>\n",
       "    <span style=\"font-weight: bold; font-size: small;\">\n",
       "        TRAPI result MESH:D000068877, NCBIGene:1399, MESH:D007938\n",
       "    </span> \n",
       "    (p-value: 2.9e-03)\n",
       "    <br>\n",
       "    Imatinib mesylate - decreases_molecular_modification_of - \n",
       "<span style=\"border: 1px orange solid; padding: 1px; border-radius: 4px;\">CRKL</span>\n",
       " - gene_associated_with_condition - \n",
       "<span style=\"border: 1px orange solid; padding: 1px; border-radius: 4px;\">leukemia</span>\n",
       "</span><br>Imatinib mesylate - decreases_molecular_modification_of - \n",
       "<span style=\"border: 1px orange solid; padding: 1px; border-radius: 4px;\">CRKL</span>\n",
       " - related_to - \n",
       "<span style=\"border: 1px orange solid; padding: 1px; border-radius: 4px;\">leukemia</span>\n",
       "</span>\n",
       "</div>\n",
       "<br>\n",
       "<div>\n",
       "    <span style=\"font-weight: bold; font-size: small;\">\n",
       "        TRAPI result MESH:D000068877, NCBIGene:3725, MESH:D007938\n",
       "    </span> \n",
       "    (p-value: 2.9e-03)\n",
       "    <br>\n",
       "    Imatinib mesylate - increases_activity_of - \n",
       "<span style=\"border: 1px orange solid; padding: 1px; border-radius: 4px;\">JUN</span>\n",
       " - contributes_to - \n",
       "<span style=\"border: 1px orange solid; padding: 1px; border-radius: 4px;\">leukemia</span>\n",
       "</span><br>Imatinib mesylate - increases_activity_of - \n",
       "<span style=\"border: 1px orange solid; padding: 1px; border-radius: 4px;\">JUN</span>\n",
       " - gene_associated_with_condition - \n",
       "<span style=\"border: 1px orange solid; padding: 1px; border-radius: 4px;\">leukemia</span>\n",
       "</span>\n",
       "</div>\n",
       "<br>\n",
       "<div>\n",
       "    <span style=\"font-weight: bold; font-size: small;\">\n",
       "        TRAPI result MESH:D000068877, NCBIGene:25, MESH:D007938\n",
       "    </span> \n",
       "    (p-value: 9.3e-02)\n",
       "    <br>\n",
       "    Imatinib mesylate - response_affected_by - ABL1 - related_to - \n",
       "<span style=\"border: 1px orange solid; padding: 1px; border-radius: 4px;\">leukemia</span>\n",
       "</span><br>Imatinib mesylate - response_affected_by - ABL1 - correlated_with - \n",
       "<span style=\"border: 1px orange solid; padding: 1px; border-radius: 4px;\">leukemia</span>\n",
       "</span><br>Imatinib mesylate - response_affected_by - ABL1 - gene_associated_with_condition - \n",
       "<span style=\"border: 1px orange solid; padding: 1px; border-radius: 4px;\">leukemia</span>\n",
       "</span><br>Imatinib mesylate - response_affected_by - ABL1 - contributes_to - \n",
       "<span style=\"border: 1px orange solid; padding: 1px; border-radius: 4px;\">leukemia</span>\n",
       "</span><br>Imatinib mesylate - response_decreased_by - ABL1 - correlated_with - \n",
       "<span style=\"border: 1px orange solid; padding: 1px; border-radius: 4px;\">leukemia</span>\n",
       "</span><br>Imatinib mesylate - decreases_molecular_modification_of - ABL1 - affects_risk_for - \n",
       "<span style=\"border: 1px orange solid; padding: 1px; border-radius: 4px;\">leukemia</span>\n",
       "</span><br>Imatinib mesylate - decreases_molecular_modification_of - ABL1 - gene_associated_with_condition - \n",
       "<span style=\"border: 1px orange solid; padding: 1px; border-radius: 4px;\">leukemia</span>\n",
       "</span><br>Imatinib mesylate - decreases_molecular_modification_of - ABL1 - contributes_to - \n",
       "<span style=\"border: 1px orange solid; padding: 1px; border-radius: 4px;\">leukemia</span>\n",
       "</span><br>Imatinib mesylate - decreases_molecular_modification_of - ABL1 - related_to - \n",
       "<span style=\"border: 1px orange solid; padding: 1px; border-radius: 4px;\">leukemia</span>\n",
       "</span><br>Imatinib mesylate - decreases_activity_of - ABL1 - correlated_with - \n",
       "<span style=\"border: 1px orange solid; padding: 1px; border-radius: 4px;\">leukemia</span>\n",
       "</span><br>Imatinib mesylate - decreases_activity_of - ABL1 - related_to - \n",
       "<span style=\"border: 1px orange solid; padding: 1px; border-radius: 4px;\">leukemia</span>\n",
       "</span><br>Imatinib mesylate - decreases_activity_of - ABL1 - contributes_to - \n",
       "<span style=\"border: 1px orange solid; padding: 1px; border-radius: 4px;\">leukemia</span>\n",
       "</span><br>Imatinib mesylate - decreases_activity_of - ABL1 - gene_associated_with_condition - \n",
       "<span style=\"border: 1px orange solid; padding: 1px; border-radius: 4px;\">leukemia</span>\n",
       "</span><br>Imatinib mesylate - decreases_molecular_modification_of - ABL1 - correlated_with - \n",
       "<span style=\"border: 1px orange solid; padding: 1px; border-radius: 4px;\">leukemia</span>\n",
       "</span><br>Imatinib mesylate - decreases_activity_of - ABL1 - affects_risk_for - \n",
       "<span style=\"border: 1px orange solid; padding: 1px; border-radius: 4px;\">leukemia</span>\n",
       "</span><br>Imatinib mesylate - response_increased_by - ABL1 - affects_risk_for - \n",
       "<span style=\"border: 1px orange solid; padding: 1px; border-radius: 4px;\">leukemia</span>\n",
       "</span><br>Imatinib mesylate - response_increased_by - ABL1 - gene_associated_with_condition - \n",
       "<span style=\"border: 1px orange solid; padding: 1px; border-radius: 4px;\">leukemia</span>\n",
       "</span><br>Imatinib mesylate - response_increased_by - ABL1 - contributes_to - \n",
       "<span style=\"border: 1px orange solid; padding: 1px; border-radius: 4px;\">leukemia</span>\n",
       "</span><br>Imatinib mesylate - response_increased_by - ABL1 - related_to - \n",
       "<span style=\"border: 1px orange solid; padding: 1px; border-radius: 4px;\">leukemia</span>\n",
       "</span><br>Imatinib mesylate - response_increased_by - ABL1 - correlated_with - \n",
       "<span style=\"border: 1px orange solid; padding: 1px; border-radius: 4px;\">leukemia</span>\n",
       "</span><br>Imatinib mesylate - response_decreased_by - ABL1 - affects_risk_for - \n",
       "<span style=\"border: 1px orange solid; padding: 1px; border-radius: 4px;\">leukemia</span>\n",
       "</span><br>Imatinib mesylate - response_decreased_by - ABL1 - gene_associated_with_condition - \n",
       "<span style=\"border: 1px orange solid; padding: 1px; border-radius: 4px;\">leukemia</span>\n",
       "</span><br>Imatinib mesylate - response_decreased_by - ABL1 - contributes_to - \n",
       "<span style=\"border: 1px orange solid; padding: 1px; border-radius: 4px;\">leukemia</span>\n",
       "</span><br>Imatinib mesylate - response_decreased_by - ABL1 - related_to - \n",
       "<span style=\"border: 1px orange solid; padding: 1px; border-radius: 4px;\">leukemia</span>\n",
       "</span><br>Imatinib mesylate - response_affected_by - ABL1 - affects_risk_for - \n",
       "<span style=\"border: 1px orange solid; padding: 1px; border-radius: 4px;\">leukemia</span>\n",
       "</span>\n",
       "</div>\n",
       "<br>\n",
       "<div>\n",
       "    <span style=\"font-weight: bold; font-size: small;\">\n",
       "        TRAPI result MESH:D000068877, NCBIGene:841, MESH:D007938\n",
       "    </span> \n",
       "    (p-value: 9.3e-02)\n",
       "    <br>\n",
       "    Imatinib mesylate - increases_activity_of - CASP8 - gene_associated_with_condition - \n",
       "<span style=\"border: 1px orange solid; padding: 1px; border-radius: 4px;\">leukemia</span>\n",
       "</span><br>Imatinib mesylate - increases_activity_of - CASP8 - correlated_with - \n",
       "<span style=\"border: 1px orange solid; padding: 1px; border-radius: 4px;\">leukemia</span>\n",
       "</span>\n",
       "</div>\n",
       "<br>\n",
       "<div>\n",
       "    <span style=\"font-weight: bold; font-size: small;\">\n",
       "        TRAPI result MESH:D000068877, NCBIGene:4067, MESH:D007938\n",
       "    </span> \n",
       "    (p-value: 9.3e-02)\n",
       "    <br>\n",
       "    Imatinib mesylate - response_affected_by - LYN - affects - \n",
       "<span style=\"border: 1px orange solid; padding: 1px; border-radius: 4px;\">leukemia</span>\n",
       "</span><br>Imatinib mesylate - response_affected_by - LYN - gene_associated_with_condition - \n",
       "<span style=\"border: 1px orange solid; padding: 1px; border-radius: 4px;\">leukemia</span>\n",
       "</span><br>Imatinib mesylate - response_decreased_by - LYN - affects - \n",
       "<span style=\"border: 1px orange solid; padding: 1px; border-radius: 4px;\">leukemia</span>\n",
       "</span><br>Imatinib mesylate - response_decreased_by - LYN - gene_associated_with_condition - \n",
       "<span style=\"border: 1px orange solid; padding: 1px; border-radius: 4px;\">leukemia</span>\n",
       "</span>\n",
       "</div>\n",
       "<br>\n",
       "<div>\n",
       "    <span style=\"font-weight: bold; font-size: small;\">\n",
       "        TRAPI result MESH:D000068877, NCBIGene:1021, MESH:D007938\n",
       "    </span> \n",
       "    (p-value: 9.3e-02)\n",
       "    <br>\n",
       "    Imatinib mesylate - response_decreased_by - CDK6 - gene_associated_with_condition - \n",
       "<span style=\"border: 1px orange solid; padding: 1px; border-radius: 4px;\">leukemia</span>\n",
       "</span><br>Imatinib mesylate - response_affected_by - CDK6 - gene_associated_with_condition - \n",
       "<span style=\"border: 1px orange solid; padding: 1px; border-radius: 4px;\">leukemia</span>\n",
       "</span>\n",
       "</div>\n",
       "<br>\n",
       "<div>\n",
       "    <span style=\"font-weight: bold; font-size: small;\">\n",
       "        TRAPI result MESH:D000068877, NCBIGene:5156, MESH:D007938\n",
       "    </span> \n",
       "    (p-value: 9.3e-02)\n",
       "    <br>\n",
       "    Imatinib mesylate - decreases_expression_of - PDGFRA - gene_associated_with_condition - \n",
       "<span style=\"border: 1px orange solid; padding: 1px; border-radius: 4px;\">leukemia</span>\n",
       "</span><br>Imatinib mesylate - response_affected_by - PDGFRA - gene_associated_with_condition - \n",
       "<span style=\"border: 1px orange solid; padding: 1px; border-radius: 4px;\">leukemia</span>\n",
       "</span><br>Imatinib mesylate - response_increased_by - PDGFRA - gene_associated_with_condition - \n",
       "<span style=\"border: 1px orange solid; padding: 1px; border-radius: 4px;\">leukemia</span>\n",
       "</span>\n",
       "</div>\n",
       "<br>\n",
       "<div>\n",
       "    <span style=\"font-weight: bold; font-size: small;\">\n",
       "        TRAPI result MESH:D000068877, NCBIGene:836, MESH:D007938\n",
       "    </span> \n",
       "    (p-value: 9.3e-02)\n",
       "    <br>\n",
       "    Imatinib mesylate - increases_degradation_of - CASP3 - gene_associated_with_condition - \n",
       "<span style=\"border: 1px orange solid; padding: 1px; border-radius: 4px;\">leukemia</span>\n",
       "</span><br>Imatinib mesylate - increases_activity_of - CASP3 - contributes_to - \n",
       "<span style=\"border: 1px orange solid; padding: 1px; border-radius: 4px;\">leukemia</span>\n",
       "</span><br>Imatinib mesylate - increases_activity_of - CASP3 - correlated_with - \n",
       "<span style=\"border: 1px orange solid; padding: 1px; border-radius: 4px;\">leukemia</span>\n",
       "</span><br>Imatinib mesylate - increases_degradation_of - CASP3 - contributes_to - \n",
       "<span style=\"border: 1px orange solid; padding: 1px; border-radius: 4px;\">leukemia</span>\n",
       "</span><br>Imatinib mesylate - increases_degradation_of - CASP3 - correlated_with - \n",
       "<span style=\"border: 1px orange solid; padding: 1px; border-radius: 4px;\">leukemia</span>\n",
       "</span><br>Imatinib mesylate - increases_activity_of - CASP3 - gene_associated_with_condition - \n",
       "<span style=\"border: 1px orange solid; padding: 1px; border-radius: 4px;\">leukemia</span>\n",
       "</span>\n",
       "</div>\n",
       "<br>\n",
       "<div>\n",
       "    <span style=\"font-weight: bold; font-size: small;\">\n",
       "        TRAPI result MESH:D000068877, NCBIGene:9467, MESH:D007938\n",
       "    </span> \n",
       "    (p-value: 9.3e-02)\n",
       "    <br>\n",
       "    Imatinib mesylate - response_affected_by - SH3BP5 - contributes_to - \n",
       "<span style=\"border: 1px orange solid; padding: 1px; border-radius: 4px;\">leukemia</span>\n",
       "</span><br>Imatinib mesylate - response_increased_by - SH3BP5 - contributes_to - \n",
       "<span style=\"border: 1px orange solid; padding: 1px; border-radius: 4px;\">leukemia</span>\n",
       "</span>\n",
       "</div>\n"
      ],
      "text/plain": [
       "<IPython.core.display.HTML object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "...\n",
      "\n",
      "plus 10 more TRAPI results\n",
      "\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "\n",
       "<div style=\"border: 1px solid slategray; padding: 6px;\">\n",
       "    <figure style=\"margin: 5px !important;\">\n",
       "        <a target=\"_blank\" href=\"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6981580/\">\n",
       "          <img src=\"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6981580/bin/ijms-21-00045-g001.jpg\" style=\"max-height: 500px; max-width: 100%;\">\n",
       "        </a>\n",
       "        <br>\n",
       "        <caption>\n",
       "            First oncogenic insult restricts leukaemic stem cells to a single differentiation pathway\n",
       "        </caption>\n",
       "    </figure>\n",
       "</div>\n"
      ],
      "text/plain": [
       "<IPython.core.display.HTML object>"
      ]
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     "metadata": {},
     "output_type": "display_data"
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    {
     "data": {
      "text/html": [
       "\n",
       "<div>\n",
       "    <span style=\"font-weight: bold; font-size: small;\">\n",
       "        TRAPI result MESH:D000068877, NCBIGene:9429, MESH:D007938\n",
       "    </span> \n",
       "    (p-value: 2.9e-03)\n",
       "    <br>\n",
       "    Imatinib mesylate - increases_activity_of - ABCG2 - gene_associated_with_condition - \n",
       "<span style=\"border: 1px orange solid; padding: 1px; border-radius: 4px;\">leukemia</span>\n",
       "</span><br>Imatinib mesylate - increases_activity_of - ABCG2 - contributes_to - \n",
       "<span style=\"border: 1px orange solid; padding: 1px; border-radius: 4px;\">leukemia</span>\n",
       "</span>\n",
       "</div>\n",
       "<br>\n",
       "<div>\n",
       "    <span style=\"font-weight: bold; font-size: small;\">\n",
       "        TRAPI result MESH:D000068877, NCBIGene:4067, MESH:D007938\n",
       "    </span> \n",
       "    (p-value: 2.9e-03)\n",
       "    <br>\n",
       "    Imatinib mesylate - response_decreased_by - LYN - gene_associated_with_condition - \n",
       "<span style=\"border: 1px orange solid; padding: 1px; border-radius: 4px;\">leukemia</span>\n",
       "</span><br>Imatinib mesylate - response_decreased_by - LYN - affects - \n",
       "<span style=\"border: 1px orange solid; padding: 1px; border-radius: 4px;\">leukemia</span>\n",
       "</span><br>Imatinib mesylate - response_affected_by - LYN - gene_associated_with_condition - \n",
       "<span style=\"border: 1px orange solid; padding: 1px; border-radius: 4px;\">leukemia</span>\n",
       "</span><br>Imatinib mesylate - response_affected_by - LYN - affects - \n",
       "<span style=\"border: 1px orange solid; padding: 1px; border-radius: 4px;\">leukemia</span>\n",
       "</span>\n",
       "</div>\n",
       "<br>\n",
       "<div>\n",
       "    <span style=\"font-weight: bold; font-size: small;\">\n",
       "        TRAPI result MESH:D000068877, NCBIGene:5243, MESH:D007938\n",
       "    </span> \n",
       "    (p-value: 2.9e-03)\n",
       "    <br>\n",
       "    Imatinib mesylate - response_decreased_by - ABCB1 - correlated_with - \n",
       "<span style=\"border: 1px orange solid; padding: 1px; border-radius: 4px;\">leukemia</span>\n",
       "</span><br>Imatinib mesylate - response_decreased_by - ABCB1 - part_of - \n",
       "<span style=\"border: 1px orange solid; padding: 1px; border-radius: 4px;\">leukemia</span>\n",
       "</span><br>Imatinib mesylate - response_decreased_by - ABCB1 - gene_associated_with_condition - \n",
       "<span style=\"border: 1px orange solid; padding: 1px; border-radius: 4px;\">leukemia</span>\n",
       "</span><br>Imatinib mesylate - response_decreased_by - ABCB1 - affects_risk_for - \n",
       "<span style=\"border: 1px orange solid; padding: 1px; border-radius: 4px;\">leukemia</span>\n",
       "</span><br>Imatinib mesylate - response_affected_by - ABCB1 - correlated_with - \n",
       "<span style=\"border: 1px orange solid; padding: 1px; border-radius: 4px;\">leukemia</span>\n",
       "</span><br>Imatinib mesylate - response_affected_by - ABCB1 - related_to - \n",
       "<span style=\"border: 1px orange solid; padding: 1px; border-radius: 4px;\">leukemia</span>\n",
       "</span><br>Imatinib mesylate - response_affected_by - ABCB1 - part_of - \n",
       "<span style=\"border: 1px orange solid; padding: 1px; border-radius: 4px;\">leukemia</span>\n",
       "</span><br>Imatinib mesylate - response_affected_by - ABCB1 - gene_associated_with_condition - \n",
       "<span style=\"border: 1px orange solid; padding: 1px; border-radius: 4px;\">leukemia</span>\n",
       "</span><br>Imatinib mesylate - response_affected_by - ABCB1 - affects_risk_for - \n",
       "<span style=\"border: 1px orange solid; padding: 1px; border-radius: 4px;\">leukemia</span>\n",
       "</span><br>Imatinib mesylate - response_decreased_by - ABCB1 - related_to - \n",
       "<span style=\"border: 1px orange solid; padding: 1px; border-radius: 4px;\">leukemia</span>\n",
       "</span>\n",
       "</div>\n",
       "<br>\n",
       "<div>\n",
       "    <span style=\"font-weight: bold; font-size: small;\">\n",
       "        TRAPI result MESH:D000068877, NCBIGene:7422, MESH:D007938\n",
       "    </span> \n",
       "    (p-value: 2.9e-03)\n",
       "    <br>\n",
       "    Imatinib mesylate - increases_secretion_of - VEGFA - contributes_to - \n",
       "<span style=\"border: 1px orange solid; padding: 1px; border-radius: 4px;\">leukemia</span>\n",
       "</span><br>Imatinib mesylate - decreases_secretion_of - VEGFA - contributes_to - \n",
       "<span style=\"border: 1px orange solid; padding: 1px; border-radius: 4px;\">leukemia</span>\n",
       "</span><br>Imatinib mesylate - increases_secretion_of - VEGFA - gene_associated_with_condition - \n",
       "<span style=\"border: 1px orange solid; padding: 1px; border-radius: 4px;\">leukemia</span>\n",
       "</span><br>Imatinib mesylate - decreases_secretion_of - VEGFA - gene_associated_with_condition - \n",
       "<span style=\"border: 1px orange solid; padding: 1px; border-radius: 4px;\">leukemia</span>\n",
       "</span>\n",
       "</div>\n",
       "<br>\n",
       "<div>\n",
       "    <span style=\"font-weight: bold; font-size: small;\">\n",
       "        TRAPI result MESH:D000068877, NCBIGene:3576, MESH:D007938\n",
       "    </span> \n",
       "    (p-value: 2.9e-03)\n",
       "    <br>\n",
       "    Imatinib mesylate - increases_secretion_of - CXCL8 - gene_associated_with_condition - \n",
       "<span style=\"border: 1px orange solid; padding: 1px; border-radius: 4px;\">leukemia</span>\n",
       "</span>\n",
       "</div>\n",
       "<br>\n",
       "<div>\n",
       "    <span style=\"font-weight: bold; font-size: small;\">\n",
       "        TRAPI result MESH:D000068877, NCBIGene:841, MESH:D007938\n",
       "    </span> \n",
       "    (p-value: 2.9e-03)\n",
       "    <br>\n",
       "    Imatinib mesylate - increases_activity_of - CASP8 - gene_associated_with_condition - \n",
       "<span style=\"border: 1px orange solid; padding: 1px; border-radius: 4px;\">leukemia</span>\n",
       "</span><br>Imatinib mesylate - increases_activity_of - CASP8 - correlated_with - \n",
       "<span style=\"border: 1px orange solid; padding: 1px; border-radius: 4px;\">leukemia</span>\n",
       "</span>\n",
       "</div>\n",
       "<br>\n",
       "<div>\n",
       "    <span style=\"font-weight: bold; font-size: small;\">\n",
       "        TRAPI result MESH:D000068877, NCBIGene:56616, MESH:D007938\n",
       "    </span> \n",
       "    (p-value: 2.9e-03)\n",
       "    <br>\n",
       "    Imatinib mesylate - increases_secretion_of - DIABLO - contributes_to - \n",
       "<span style=\"border: 1px orange solid; padding: 1px; border-radius: 4px;\">leukemia</span>\n",
       "</span>\n",
       "</div>\n",
       "<br>\n",
       "<div>\n",
       "    <span style=\"font-weight: bold; font-size: small;\">\n",
       "        TRAPI result MESH:D000068877, NCBIGene:2875, MESH:D007938\n",
       "    </span> \n",
       "    (p-value: 2.9e-03)\n",
       "    <br>\n",
       "    Imatinib mesylate - increases_secretion_of - GPT - gene_associated_with_condition - \n",
       "<span style=\"border: 1px orange solid; padding: 1px; border-radius: 4px;\">leukemia</span>\n",
       "</span>\n",
       "</div>\n",
       "<br>\n",
       "<div>\n",
       "    <span style=\"font-weight: bold; font-size: small;\">\n",
       "        TRAPI result MESH:D000068877, NCBIGene:842, MESH:D007938\n",
       "    </span> \n",
       "    (p-value: 2.9e-03)\n",
       "    <br>\n",
       "    Imatinib mesylate - increases_degradation_of - CASP9 - correlated_with - \n",
       "<span style=\"border: 1px orange solid; padding: 1px; border-radius: 4px;\">leukemia</span>\n",
       "</span><br>Imatinib mesylate - increases_degradation_of - CASP9 - gene_associated_with_condition - \n",
       "<span style=\"border: 1px orange solid; padding: 1px; border-radius: 4px;\">leukemia</span>\n",
       "</span>\n",
       "</div>\n",
       "<br>\n",
       "<div>\n",
       "    <span style=\"font-weight: bold; font-size: small;\">\n",
       "        TRAPI result MESH:D000068877, NCBIGene:1399, MESH:D007938\n",
       "    </span> \n",
       "    (p-value: 2.9e-03)\n",
       "    <br>\n",
       "    Imatinib mesylate - decreases_molecular_modification_of - CRKL - related_to - \n",
       "<span style=\"border: 1px orange solid; padding: 1px; border-radius: 4px;\">leukemia</span>\n",
       "</span><br>Imatinib mesylate - decreases_molecular_modification_of - CRKL - gene_associated_with_condition - \n",
       "<span style=\"border: 1px orange solid; padding: 1px; border-radius: 4px;\">leukemia</span>\n",
       "</span>\n",
       "</div>\n"
      ],
      "text/plain": [
       "<IPython.core.display.HTML object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "...\n",
      "\n",
      "plus 10 more TRAPI results\n",
      "\n"
     ]
    }
   ],
   "source": [
    "from IPython.display import Image\n",
    "from IPython.core.display import HTML \n",
    "\n",
    "\n",
    "outer_limit = 10\n",
    "inner_limit = 10\n",
    "\n",
    "count = 0\n",
    "for figure_id, one_figure_id_df in cooccurrence_df.groupby(\"figure_id\", sort=False):\n",
    "    [pmc,filename] = figure_id.split(\"__\")\n",
    "    paper_url = f\"https://www.ncbi.nlm.nih.gov/pmc/articles/{pmc}/\"\n",
    "    figure_url = f\"https://www.ncbi.nlm.nih.gov/pmc/articles/{pmc}/bin/{filename}\"\n",
    "    figure_title = list(set(one_figure_id_df[\"figure_title\"]))[0]\n",
    "\n",
    "    display(HTML(data=f'''\n",
    "<div style=\"border: 1px solid slategray; padding: 6px;\">\n",
    "    <figure style=\"margin: 5px !important;\">\n",
    "        <a target=\"_blank\" href=\"{paper_url}\">\n",
    "          <img src=\"{figure_url}\" style=\"max-height: 500px; max-width: 100%;\">\n",
    "        </a>\n",
    "        <br>\n",
    "        <caption>\n",
    "            {figure_title}\n",
    "        </caption>\n",
    "    </figure>\n",
    "</div>\n",
    "'''))\n",
    "\n",
    "    trapi_results_row_sets = []\n",
    "    \n",
    "    for trapi_result_curie_combo, one_trapi_result_curie_combo_df in one_figure_id_df[(\n",
    "        trapi_result_columns +\n",
    "        unified_curie_columns +\n",
    "        [\"trapi_result_curies_key\", \"common_curie_set\", \"p_value\"]\n",
    "    )].groupby(\"trapi_result_curies_key\", sort=False):\n",
    "        overlap_names = []\n",
    "        for curie in one_trapi_result_curie_combo_df[\"common_curie_set\"].tolist()[0]:\n",
    "            name = curie_to_name[curie]\n",
    "            overlap_names.append(name)\n",
    "\n",
    "        trapi_results_rows = []\n",
    "        for i,row in one_trapi_result_curie_combo_df.iterrows():\n",
    "            trapi_result_chunks = []\n",
    "            for trapi_result_column in trapi_result_columns:\n",
    "                row_value = row[trapi_result_column]\n",
    "                if row_value in overlap_names:\n",
    "                    trapi_result_chunk = f'''\n",
    "<span style=\"border: 1px orange solid; padding: 1px; border-radius: 4px;\">{row_value}</span>\n",
    "'''\n",
    "                else:\n",
    "                    trapi_result_chunk = f'{row_value}'\n",
    "                trapi_result_chunks.append(trapi_result_chunk)\n",
    "                \n",
    "            trapi_results_rows.append(\n",
    "                f'{\" - \".join(trapi_result_chunks)}</span>'\n",
    "            )\n",
    "        \n",
    "        ordered_trapi_result_curie_combo = []\n",
    "        for unified_curie_column in unified_curie_columns:\n",
    "            ordered_trapi_result_curie_combo.append(list(set(\n",
    "                one_trapi_result_curie_combo_df[unified_curie_column]\n",
    "            ))[0])\n",
    "        \n",
    "        p_value = list(set(one_trapi_result_curie_combo_df[\"p_value\"]))[0]\n",
    "        trapi_results_row_sets.append(f'''\n",
    "<div>\n",
    "    <span style=\"font-weight: bold; font-size: small;\">\n",
    "        TRAPI result {\", \".join(ordered_trapi_result_curie_combo)}\n",
    "    </span> \n",
    "    (p-value: {p_value:.1e})\n",
    "    <br>\n",
    "    {\"<br>\".join(trapi_results_rows)}\n",
    "</div>\n",
    "''')\n",
    "\n",
    "    trapi_results_row_set_count = len(trapi_results_row_sets)\n",
    "    display(HTML(data=\"<br>\".join(trapi_results_row_sets[:inner_limit])))\n",
    "    if trapi_results_row_set_count > inner_limit:\n",
    "        print(f\"...\")\n",
    "        print(\"\")\n",
    "        print(f\"plus {trapi_results_row_set_count - inner_limit} more TRAPI results\")\n",
    "    print(\"\")\n",
    "\n",
    "    count += 1\n",
    "    if count >= outer_limit:\n",
    "        print(\"...\")\n",
    "        print(\"\")\n",
    "        print(f'''\n",
    "plus {len(set(cooccurrence_df[\"figure_id\"])) - outer_limit} more figures, each with TRAPI results\n",
    "''')\n",
    "        print(\"\")\n",
    "        break"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
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   "collapsed_sections": [],
   "name": "bte_clustering.ipynb",
   "provenance": []
  },
  "kernelspec": {
   "display_name": "Python 3 (ipykernel)",
   "language": "python",
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   "codemirror_mode": {
    "name": "ipython",
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