MeSH.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Download \n",
    "https://nlmpubs.nlm.nih.gov/projects/mesh/MESH_FILES/xmlmesh/desc2024.gz   \n",
    "or   \n",
    "https://nlmpubs.nlm.nih.gov/projects/mesh/MESH_FILES/xmlmesh/desc2024.xml"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "#!wget --timestamping --directory-prefix download/ ftp://nlmpubs.nlm.nih.gov/online/mesh/.xmlmesh/desc2015.gz"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "import os\n",
    "import gzip\n",
    "import io\n",
    "import json\n",
    "\n",
    "import xml.etree.ElementTree as ET\n",
    "\n",
    "import networkx\n",
    "import pandas"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "download_dir ='../data/MeSH'"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Read MeSH xml release\n",
    "#xml_path = '../data/desc2024.gz' #os.path.join('download', 'desc2015.gz')\n",
    "#with gzip.open(xml_path) as xml_file:\n",
    "#    tree = ET.parse(xml_file)\n",
    "#root = tree.getroot()\n",
    "\n",
    "xml_path = os.path.join(download_dir, 'desc2024.xml') \n",
    "tree = ET.parse(xml_path)\n",
    "root = tree.getroot()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
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       "        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>DescriptorUI</th>\n",
       "      <th>ConceptUI</th>\n",
       "      <th>PreferredConceptYN</th>\n",
       "      <th>TermUI</th>\n",
       "      <th>TermName</th>\n",
       "      <th>ConceptPreferredTermYN</th>\n",
       "      <th>IsPermutedTermYN</th>\n",
       "      <th>LexicalTag</th>\n",
       "      <th>RecordPreferredTermYN</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>D000001</td>\n",
       "      <td>M0000001</td>\n",
       "      <td>Y</td>\n",
       "      <td>T000002</td>\n",
       "      <td>Calcimycin</td>\n",
       "      <td>Y</td>\n",
       "      <td>N</td>\n",
       "      <td>NON</td>\n",
       "      <td>Y</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>D000001</td>\n",
       "      <td>M0000001</td>\n",
       "      <td>Y</td>\n",
       "      <td>T001124965</td>\n",
       "      <td>4-Benzoxazolecarboxylic acid, 5-(methylamino)-...</td>\n",
       "      <td>N</td>\n",
       "      <td>N</td>\n",
       "      <td>NON</td>\n",
       "      <td>N</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>D000001</td>\n",
       "      <td>M0353609</td>\n",
       "      <td>N</td>\n",
       "      <td>T000001</td>\n",
       "      <td>A-23187</td>\n",
       "      <td>Y</td>\n",
       "      <td>N</td>\n",
       "      <td>LAB</td>\n",
       "      <td>N</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>D000001</td>\n",
       "      <td>M0353609</td>\n",
       "      <td>N</td>\n",
       "      <td>T000001</td>\n",
       "      <td>A 23187</td>\n",
       "      <td>N</td>\n",
       "      <td>Y</td>\n",
       "      <td>LAB</td>\n",
       "      <td>N</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>D000001</td>\n",
       "      <td>M0353609</td>\n",
       "      <td>N</td>\n",
       "      <td>T000004</td>\n",
       "      <td>A23187</td>\n",
       "      <td>N</td>\n",
       "      <td>N</td>\n",
       "      <td>LAB</td>\n",
       "      <td>N</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",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>263931</th>\n",
       "      <td>D066331</td>\n",
       "      <td>M0594254</td>\n",
       "      <td>Y</td>\n",
       "      <td>T859704</td>\n",
       "      <td>Laser-Evoked Potentials</td>\n",
       "      <td>Y</td>\n",
       "      <td>N</td>\n",
       "      <td>NON</td>\n",
       "      <td>Y</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>263932</th>\n",
       "      <td>D066331</td>\n",
       "      <td>M0594254</td>\n",
       "      <td>Y</td>\n",
       "      <td>T859704</td>\n",
       "      <td>Laser Evoked Potentials</td>\n",
       "      <td>N</td>\n",
       "      <td>Y</td>\n",
       "      <td>NON</td>\n",
       "      <td>N</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>263933</th>\n",
       "      <td>D066331</td>\n",
       "      <td>M0594254</td>\n",
       "      <td>Y</td>\n",
       "      <td>T859704</td>\n",
       "      <td>Laser-Evoked Potential</td>\n",
       "      <td>N</td>\n",
       "      <td>Y</td>\n",
       "      <td>NON</td>\n",
       "      <td>N</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>263934</th>\n",
       "      <td>D066331</td>\n",
       "      <td>M0594254</td>\n",
       "      <td>Y</td>\n",
       "      <td>T859704</td>\n",
       "      <td>Potential, Laser-Evoked</td>\n",
       "      <td>N</td>\n",
       "      <td>Y</td>\n",
       "      <td>NON</td>\n",
       "      <td>N</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>263935</th>\n",
       "      <td>D066331</td>\n",
       "      <td>M0594254</td>\n",
       "      <td>Y</td>\n",
       "      <td>T859704</td>\n",
       "      <td>Potentials, Laser-Evoked</td>\n",
       "      <td>N</td>\n",
       "      <td>Y</td>\n",
       "      <td>NON</td>\n",
       "      <td>N</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>263936 rows × 9 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "       DescriptorUI ConceptUI PreferredConceptYN      TermUI  \\\n",
       "0           D000001  M0000001                  Y     T000002   \n",
       "1           D000001  M0000001                  Y  T001124965   \n",
       "2           D000001  M0353609                  N     T000001   \n",
       "3           D000001  M0353609                  N     T000001   \n",
       "4           D000001  M0353609                  N     T000004   \n",
       "...             ...       ...                ...         ...   \n",
       "263931      D066331  M0594254                  Y     T859704   \n",
       "263932      D066331  M0594254                  Y     T859704   \n",
       "263933      D066331  M0594254                  Y     T859704   \n",
       "263934      D066331  M0594254                  Y     T859704   \n",
       "263935      D066331  M0594254                  Y     T859704   \n",
       "\n",
       "                                                 TermName  \\\n",
       "0                                              Calcimycin   \n",
       "1       4-Benzoxazolecarboxylic acid, 5-(methylamino)-...   \n",
       "2                                                 A-23187   \n",
       "3                                                 A 23187   \n",
       "4                                                  A23187   \n",
       "...                                                   ...   \n",
       "263931                            Laser-Evoked Potentials   \n",
       "263932                            Laser Evoked Potentials   \n",
       "263933                             Laser-Evoked Potential   \n",
       "263934                            Potential, Laser-Evoked   \n",
       "263935                           Potentials, Laser-Evoked   \n",
       "\n",
       "       ConceptPreferredTermYN IsPermutedTermYN LexicalTag  \\\n",
       "0                           Y                N        NON   \n",
       "1                           N                N        NON   \n",
       "2                           Y                N        LAB   \n",
       "3                           N                Y        LAB   \n",
       "4                           N                N        LAB   \n",
       "...                       ...              ...        ...   \n",
       "263931                      Y                N        NON   \n",
       "263932                      N                Y        NON   \n",
       "263933                      N                Y        NON   \n",
       "263934                      N                Y        NON   \n",
       "263935                      N                Y        NON   \n",
       "\n",
       "       RecordPreferredTermYN  \n",
       "0                          Y  \n",
       "1                          N  \n",
       "2                          N  \n",
       "3                          N  \n",
       "4                          N  \n",
       "...                      ...  \n",
       "263931                     Y  \n",
       "263932                     N  \n",
       "263933                     N  \n",
       "263934                     N  \n",
       "263935                     N  \n",
       "\n",
       "[263936 rows x 9 columns]"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Extract mesh terms\n",
    "term_dicts = list()\n",
    "for descriptor in root:\n",
    "    for concept in descriptor.findall('ConceptList/Concept'):\n",
    "        for term in concept.findall('TermList/Term'):\n",
    "            term_dict = {\n",
    "                'DescriptorUI': descriptor.findtext('DescriptorUI'),\n",
    "                'ConceptUI': concept.findtext('ConceptUI'),\n",
    "                'TermUI': term.findtext('TermUI'),\n",
    "                'TermName': term.findtext('String')\n",
    "            }\n",
    "            term_dict.update(concept.attrib)\n",
    "            term_dict.update(term.attrib)\n",
    "            term_dicts.append(term_dict)\n",
    "\n",
    "columns = ['DescriptorUI', 'ConceptUI', 'PreferredConceptYN', 'TermUI', 'TermName',\n",
    "           'ConceptPreferredTermYN', 'IsPermutedTermYN', 'LexicalTag', 'RecordPreferredTermYN'] #, 'PrintFlagYN'\n",
    "term_df = pandas.DataFrame(term_dicts)[columns]\n",
    "term_df.to_csv(os.path.join(download_dir, 'descriptor-terms.tsv'), index=False, sep='\\t')\n",
    "term_df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "False"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Test whether MeSH term names are unique\n",
    "len(term_df) == len(set(term_df.TermName))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "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>DescriptorUI</th>\n",
       "      <th>ConceptUI</th>\n",
       "      <th>PreferredConceptYN</th>\n",
       "      <th>TermUI</th>\n",
       "      <th>TermName</th>\n",
       "      <th>ConceptPreferredTermYN</th>\n",
       "      <th>IsPermutedTermYN</th>\n",
       "      <th>LexicalTag</th>\n",
       "      <th>RecordPreferredTermYN</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>19706</th>\n",
       "      <td>D000081207</td>\n",
       "      <td>M000650100</td>\n",
       "      <td>N</td>\n",
       "      <td>T000999067</td>\n",
       "      <td>Inherited Immunodeficiency Syndrome</td>\n",
       "      <td>N</td>\n",
       "      <td>N</td>\n",
       "      <td>NON</td>\n",
       "      <td>N</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19715</th>\n",
       "      <td>D000081207</td>\n",
       "      <td>M000650100</td>\n",
       "      <td>N</td>\n",
       "      <td>T000958692</td>\n",
       "      <td>Inherited Immunodeficiency Syndrome</td>\n",
       "      <td>N</td>\n",
       "      <td>Y</td>\n",
       "      <td>NON</td>\n",
       "      <td>N</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50398</th>\n",
       "      <td>D002522</td>\n",
       "      <td>M0003848</td>\n",
       "      <td>Y</td>\n",
       "      <td>T007361</td>\n",
       "      <td>African Green Monkey</td>\n",
       "      <td>N</td>\n",
       "      <td>N</td>\n",
       "      <td>EPO</td>\n",
       "      <td>N</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50410</th>\n",
       "      <td>D002522</td>\n",
       "      <td>M0003850</td>\n",
       "      <td>N</td>\n",
       "      <td>T007362</td>\n",
       "      <td>African Green Monkey</td>\n",
       "      <td>N</td>\n",
       "      <td>Y</td>\n",
       "      <td>EPO</td>\n",
       "      <td>N</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>71491</th>\n",
       "      <td>D005175</td>\n",
       "      <td>M0008159</td>\n",
       "      <td>Y</td>\n",
       "      <td>T751631</td>\n",
       "      <td>Factor 12 Deficiency</td>\n",
       "      <td>N</td>\n",
       "      <td>N</td>\n",
       "      <td>NON</td>\n",
       "      <td>N</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>71498</th>\n",
       "      <td>D005175</td>\n",
       "      <td>M0008159</td>\n",
       "      <td>Y</td>\n",
       "      <td>T000913569</td>\n",
       "      <td>Factor 12 Deficiency</td>\n",
       "      <td>N</td>\n",
       "      <td>Y</td>\n",
       "      <td>NON</td>\n",
       "      <td>N</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>80191</th>\n",
       "      <td>D006319</td>\n",
       "      <td>M0009934</td>\n",
       "      <td>Y</td>\n",
       "      <td>T001102790</td>\n",
       "      <td>Sensoryneural Deafness</td>\n",
       "      <td>N</td>\n",
       "      <td>N</td>\n",
       "      <td>NON</td>\n",
       "      <td>N</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>80201</th>\n",
       "      <td>D006319</td>\n",
       "      <td>M0009934</td>\n",
       "      <td>Y</td>\n",
       "      <td>T001102791</td>\n",
       "      <td>Sensoryneural Deafness</td>\n",
       "      <td>N</td>\n",
       "      <td>Y</td>\n",
       "      <td>NON</td>\n",
       "      <td>N</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>99886</th>\n",
       "      <td>D008872</td>\n",
       "      <td>M0013839</td>\n",
       "      <td>Y</td>\n",
       "      <td>T001078150</td>\n",
       "      <td>Micro Waves</td>\n",
       "      <td>N</td>\n",
       "      <td>Y</td>\n",
       "      <td>NON</td>\n",
       "      <td>N</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>99887</th>\n",
       "      <td>D008872</td>\n",
       "      <td>M0013839</td>\n",
       "      <td>Y</td>\n",
       "      <td>T001078148</td>\n",
       "      <td>Micro Waves</td>\n",
       "      <td>N</td>\n",
       "      <td>N</td>\n",
       "      <td>NON</td>\n",
       "      <td>N</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "      DescriptorUI   ConceptUI PreferredConceptYN      TermUI  \\\n",
       "19706   D000081207  M000650100                  N  T000999067   \n",
       "19715   D000081207  M000650100                  N  T000958692   \n",
       "50398      D002522    M0003848                  Y     T007361   \n",
       "50410      D002522    M0003850                  N     T007362   \n",
       "71491      D005175    M0008159                  Y     T751631   \n",
       "71498      D005175    M0008159                  Y  T000913569   \n",
       "80191      D006319    M0009934                  Y  T001102790   \n",
       "80201      D006319    M0009934                  Y  T001102791   \n",
       "99886      D008872    M0013839                  Y  T001078150   \n",
       "99887      D008872    M0013839                  Y  T001078148   \n",
       "\n",
       "                                  TermName ConceptPreferredTermYN  \\\n",
       "19706  Inherited Immunodeficiency Syndrome                      N   \n",
       "19715  Inherited Immunodeficiency Syndrome                      N   \n",
       "50398                 African Green Monkey                      N   \n",
       "50410                 African Green Monkey                      N   \n",
       "71491                 Factor 12 Deficiency                      N   \n",
       "71498                 Factor 12 Deficiency                      N   \n",
       "80191               Sensoryneural Deafness                      N   \n",
       "80201               Sensoryneural Deafness                      N   \n",
       "99886                          Micro Waves                      N   \n",
       "99887                          Micro Waves                      N   \n",
       "\n",
       "      IsPermutedTermYN LexicalTag RecordPreferredTermYN  \n",
       "19706                N        NON                     N  \n",
       "19715                Y        NON                     N  \n",
       "50398                N        EPO                     N  \n",
       "50410                Y        EPO                     N  \n",
       "71491                N        NON                     N  \n",
       "71498                Y        NON                     N  \n",
       "80191                N        NON                     N  \n",
       "80201                Y        NON                     N  \n",
       "99886                Y        NON                     N  \n",
       "99887                N        NON                     N  "
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dup_termname = term_df[term_df.duplicated('TermName', keep=False) == True]\n",
    "dup_termname #https://www.ncbi.nlm.nih.gov/mesh/?term=D000081207 it do have duplicated entry term"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Parse MeSH xml release\n",
    "terms = list()\n",
    "\n",
    "for elem in root:\n",
    "    term = dict()\n",
    "    term['mesh_id'] = elem.findtext('DescriptorUI')\n",
    "    term['mesh_name'] = elem.findtext('DescriptorName/String')\n",
    "    term['semantic_types'] = list({x.text for x in elem.findall(\n",
    "        'ConceptList/Concept/SemanticTypeList/SemanticType/SemanticTypeUI')})\n",
    "    term['tree_numbers'] = [x.text for x in elem.findall('TreeNumberList/TreeNumber')]\n",
    "    terms.append(term)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[{'mesh_id': 'D000001',\n",
       "  'mesh_name': 'Calcimycin',\n",
       "  'semantic_types': [],\n",
       "  'tree_numbers': ['D02.355.291.933.125',\n",
       "   'D02.540.576.625.125',\n",
       "   'D03.633.100.221.173',\n",
       "   'D04.345.241.654.125',\n",
       "   'D04.345.674.625.125']},\n",
       " {'mesh_id': 'D000002',\n",
       "  'mesh_name': 'Temefos',\n",
       "  'semantic_types': [],\n",
       "  'tree_numbers': ['D02.705.400.625.800',\n",
       "   'D02.705.539.345.800',\n",
       "   'D02.886.300.692.800']},\n",
       " {'mesh_id': 'D000003',\n",
       "  'mesh_name': 'Abattoirs',\n",
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       "  'mesh_name': 'Nurses, Neonatal',\n",
       "  'semantic_types': [],\n",
       "  'tree_numbers': ['M01.526.485.650.648.940.500', 'N02.360.650.648.940.500']},\n",
       " {'mesh_id': 'D000072222',\n",
       "  'mesh_name': 'Nurses, Pediatric',\n",
       "  'semantic_types': [],\n",
       "  'tree_numbers': ['M01.526.485.650.648.940', 'N02.360.650.648.940']},\n",
       " {'mesh_id': 'D000072223',\n",
       "  'mesh_name': 'Allergists',\n",
       "  'semantic_types': [],\n",
       "  'tree_numbers': ['M01.526.485.810.020', 'N02.360.810.020']},\n",
       " {'mesh_id': 'D000072224',\n",
       "  'mesh_name': 'Bcl-2-Like Protein 11',\n",
       "  'semantic_types': [],\n",
       "  'tree_numbers': ['D12.644.360.075.323',\n",
       "   'D12.776.476.075.323',\n",
       "   'D12.776.543.116',\n",
       "   'D12.776.624.664.700.025']},\n",
       " {'mesh_id': 'D000072226',\n",
       "  'mesh_name': 'Computed Tomography Angiography',\n",
       "  'semantic_types': [],\n",
       "  'tree_numbers': ['E01.370.350.350.810.335',\n",
       "   'E01.370.350.567.250',\n",
       "   'E01.370.350.600.350.700.810.335',\n",
       "   'E01.370.350.700.700.810.335',\n",
       "   'E01.370.350.700.810.810.568',\n",
       "   'E01.370.350.825.810.810.499']},\n",
       " {'mesh_id': 'D000072227',\n",
       "  'mesh_name': 'Shoulder Prosthesis',\n",
       "  'semantic_types': [],\n",
       "  'tree_numbers': ['E07.695.400.852']},\n",
       " {'mesh_id': 'D000072228',\n",
       "  'mesh_name': 'Arthroplasty, Replacement, Shoulder',\n",
       "  'semantic_types': [],\n",
       "  'tree_numbers': ['E04.555.110.110.299',\n",
       "   'E04.617.101.110.299',\n",
       "   'E04.650.110.299']},\n",
       " {'mesh_id': 'D000072229',\n",
       "  'mesh_name': 'Posterior Cruciate Ligament Reconstruction',\n",
       "  'semantic_types': [],\n",
       "  'tree_numbers': ['E02.718.688', 'E04.555.110.557', 'E04.617.101.557']},\n",
       " {'mesh_id': 'D000072230',\n",
       "  'mesh_name': 'Sustained Virologic Response',\n",
       "  'semantic_types': [],\n",
       "  'tree_numbers': ['E01.789.800.570',\n",
       "   'N04.761.559.590.800.665',\n",
       "   'N05.715.360.575.575.800.665']},\n",
       " {'mesh_id': 'D000072231',\n",
       "  'mesh_name': 'In Vivo Dosimetry',\n",
       "  'semantic_types': [],\n",
       "  'tree_numbers': ['E05.799.638.511']},\n",
       " {'mesh_id': 'D000072232',\n",
       "  'mesh_name': 'Radiation Dosimeters',\n",
       "  'semantic_types': [],\n",
       "  'tree_numbers': ['E07.710.725']},\n",
       " {'mesh_id': 'D000072233',\n",
       "  'mesh_name': 'Homer Scaffolding Proteins',\n",
       "  'semantic_types': [],\n",
       "  'tree_numbers': ['D12.776.476.024.381']},\n",
       " {'mesh_id': 'D000072234',\n",
       "  'mesh_name': 'Donor Conception',\n",
       "  'semantic_types': [],\n",
       "  'tree_numbers': ['E02.875.800.250', 'E05.820.800.250']},\n",
       " {'mesh_id': 'D000072235',\n",
       "  'mesh_name': 'Mating Factor',\n",
       "  'semantic_types': [],\n",
       "  'tree_numbers': ['D12.644.368', 'D23.641.200']},\n",
       " {'mesh_id': 'D000072236',\n",
       "  'mesh_name': 'Methyl-Accepting Chemotaxis Proteins',\n",
       "  'semantic_types': [],\n",
       "  'tree_numbers': ['D12.644.360.420',\n",
       "   'D12.776.097.533',\n",
       "   'D12.776.476.420',\n",
       "   'D12.776.543.750.054']},\n",
       " {'mesh_id': 'D000072237',\n",
       "  'mesh_name': 'ERG1 Potassium Channel',\n",
       "  'semantic_types': [],\n",
       "  'tree_numbers': ['D12.776.157.530.400.600.900.249.500',\n",
       "   'D12.776.543.550.450.750.900.249.500',\n",
       "   'D12.776.543.585.400.750.900.249.500']},\n",
       " {'mesh_id': 'D000072257',\n",
       "  'mesh_name': 'Oxygen Radical Absorbance Capacity',\n",
       "  'semantic_types': [],\n",
       "  'tree_numbers': ['E05.657']},\n",
       " {'mesh_id': 'D000072258',\n",
       "  'mesh_name': 'Tumor Hypoxia',\n",
       "  'semantic_types': [],\n",
       "  'tree_numbers': ['G03.197.300.500', 'G04.270.300.500']},\n",
       " {'mesh_id': 'D000072259',\n",
       "  'mesh_name': 'Connexin 26',\n",
       "  'semantic_types': [],\n",
       "  'tree_numbers': ['D12.776.543.585.250.100']},\n",
       " {'mesh_id': 'D000072260',\n",
       "  'mesh_name': 'RNA Splicing Factors',\n",
       "  'semantic_types': [],\n",
       "  'tree_numbers': ['D12.776.157.725.829', 'D12.776.664.962.829']},\n",
       " {'mesh_id': 'D000072261',\n",
       "  'mesh_name': 'Dishevelled Proteins',\n",
       "  'semantic_types': [],\n",
       "  'tree_numbers': ['D12.644.360.024.288',\n",
       "   'D12.776.157.057.028',\n",
       "   'D12.776.476.024.330']},\n",
       " {'mesh_id': 'D000072263',\n",
       "  'mesh_name': 'Private Facilities',\n",
       "  'semantic_types': [],\n",
       "  'tree_numbers': ['J03.700']},\n",
       " {'mesh_id': 'D000072264',\n",
       "  'mesh_name': 'Sports and Recreational Facilities',\n",
       "  'semantic_types': [],\n",
       "  'tree_numbers': ['J03.925']},\n",
       " {'mesh_id': 'D000072277',\n",
       "  'mesh_name': 'Maximal Respiratory Pressures',\n",
       "  'semantic_types': [],\n",
       "  'tree_numbers': ['E01.370.386.700.550']},\n",
       " {'mesh_id': 'D000072278',\n",
       "  'mesh_name': 'Forkhead Box Protein M1',\n",
       "  'semantic_types': [],\n",
       "  'tree_numbers': ['D12.776.260.950.249.063', 'D12.776.930.977.249.063']},\n",
       " {'mesh_id': 'D000072280',\n",
       "  'mesh_name': 'Bacteria, Thermoduric',\n",
       "  'semantic_types': [],\n",
       "  'tree_numbers': ['B03.135']},\n",
       " {'mesh_id': 'D000072281',\n",
       "  'mesh_name': 'Lymphadenopathy',\n",
       "  'semantic_types': [],\n",
       "  'tree_numbers': ['C15.604.338']},\n",
       " {'mesh_id': 'D000072282',\n",
       "  'mesh_name': 'Extraintestinal Pathogenic Escherichia coli',\n",
       "  'semantic_types': [],\n",
       "  'tree_numbers': ['B03.440.450.425.325.300.580',\n",
       "   'B03.660.250.150.180.100.580']},\n",
       " {'mesh_id': 'D000072283',\n",
       "  'mesh_name': 'A549 Cells',\n",
       "  'semantic_types': [],\n",
       "  'tree_numbers': ['A11.251.210.190.080',\n",
       "   'A11.251.860.180.080',\n",
       "   'A11.436.054']},\n",
       " {'mesh_id': 'D000072296',\n",
       "  'mesh_name': 'Bathroom Equipment',\n",
       "  'semantic_types': [],\n",
       "  'tree_numbers': ['E07.108']},\n",
       " {'mesh_id': 'D000072316',\n",
       "  'mesh_name': 'Dioxins and Dioxin-like Compounds',\n",
       "  'semantic_types': [],\n",
       "  'tree_numbers': ['D02.309']},\n",
       " {'mesh_id': 'D000072317',\n",
       "  'mesh_name': 'Polychlorinated Dibenzodioxins',\n",
       "  'semantic_types': [],\n",
       "  'tree_numbers': ['D02.309.500.450', 'D03.633.300.786']},\n",
       " ...]"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "terms"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Determine ontology parents\n",
    "tree_number_to_id = {tn: term['mesh_id'] for term in terms for tn in term['tree_numbers']}\n",
    "\n",
    "for term in terms:\n",
    "    parents = set()\n",
    "    for tree_number in term['tree_numbers']:\n",
    "        try:\n",
    "            parent_tn, self_tn = tree_number.rsplit('.', 1)\n",
    "            parents.add(tree_number_to_id[parent_tn])\n",
    "        except ValueError:\n",
    "            pass\n",
    "    term['parents'] = list(parents)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'mesh_id': 'D066331',\n",
       " 'mesh_name': 'Laser-Evoked Potentials',\n",
       " 'semantic_types': [],\n",
       " 'tree_numbers': ['G07.265.216.500.400.500', 'G11.561.200.500.400.500'],\n",
       " 'parents': ['D005073']}"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "term"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [],
   "source": [
    "path = os.path.join(download_dir, 'mesh.json')\n",
    "with open(path, 'w') as write_file:\n",
    "    json.dump(terms, write_file, indent=2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Create a newtorkx directed graph represented mesh\n",
    "network = networkx.DiGraph()\n",
    "\n",
    "# add nodes\n",
    "for term in terms:\n",
    "    network.add_node(term['mesh_id'], name=term['mesh_name'])\n",
    "\n",
    "# add edges\n",
    "for term in terms:\n",
    "    for parent in term['parents']:\n",
    "        network.add_edge(parent, term['mesh_id'])\n",
    "\n",
    "#assert networkx.is_directed_acyclic_graph(network)\n",
    "\n",
    "networkx.write_gexf(network, os.path.join(download_dir, 'ontology.gexf.gz'))"
   ]
  },
  {
   "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>mesh_id</th>\n",
       "      <th>mesh_name</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>D000001</td>\n",
       "      <td>Calcimycin</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>D000002</td>\n",
       "      <td>Temefos</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>D000003</td>\n",
       "      <td>Abattoirs</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>D000004</td>\n",
       "      <td>Abbreviations as Topic</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>D000005</td>\n",
       "      <td>Abdomen</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>30759</th>\n",
       "      <td>D066310</td>\n",
       "      <td>Digital Divide</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>30760</th>\n",
       "      <td>D066328</td>\n",
       "      <td>Ventral Striatum</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>30761</th>\n",
       "      <td>D066329</td>\n",
       "      <td>Protein Aggregates</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>30762</th>\n",
       "      <td>D066330</td>\n",
       "      <td>Printing, Three-Dimensional</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>30763</th>\n",
       "      <td>D066331</td>\n",
       "      <td>Laser-Evoked Potentials</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>30764 rows × 2 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "       mesh_id                    mesh_name\n",
       "0      D000001                   Calcimycin\n",
       "1      D000002                      Temefos\n",
       "2      D000003                    Abattoirs\n",
       "3      D000004       Abbreviations as Topic\n",
       "4      D000005                      Abdomen\n",
       "...        ...                          ...\n",
       "30759  D066310               Digital Divide\n",
       "30760  D066328             Ventral Striatum\n",
       "30761  D066329           Protein Aggregates\n",
       "30762  D066330  Printing, Three-Dimensional\n",
       "30763  D066331      Laser-Evoked Potentials\n",
       "\n",
       "[30764 rows x 2 columns]"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Read mesh\n",
    "path = os.path.join(download_dir, 'mesh.json')\n",
    "with open(path) as read_file:\n",
    "    mesh = json.load(read_file)\n",
    "\n",
    "mesh_df = pandas.DataFrame.from_dict(mesh)[['mesh_id', 'mesh_name']]\n",
    "mesh_df.to_csv(os.path.join(download_dir, 'terms.tsv'), sep='\\t', index=False)\n",
    "mesh_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>mesh_id</th>\n",
       "      <th>mesh_name</th>\n",
       "      <th>mesh_tree_number</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>D000001</td>\n",
       "      <td>Calcimycin</td>\n",
       "      <td>D02.355.291.933.125</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>D000001</td>\n",
       "      <td>Calcimycin</td>\n",
       "      <td>D02.540.576.625.125</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>D000001</td>\n",
       "      <td>Calcimycin</td>\n",
       "      <td>D03.633.100.221.173</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>D000001</td>\n",
       "      <td>Calcimycin</td>\n",
       "      <td>D04.345.241.654.125</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>D000001</td>\n",
       "      <td>Calcimycin</td>\n",
       "      <td>D04.345.674.625.125</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>64452</th>\n",
       "      <td>D066330</td>\n",
       "      <td>Printing, Three-Dimensional</td>\n",
       "      <td>J01.897.564</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>64453</th>\n",
       "      <td>D066330</td>\n",
       "      <td>Printing, Three-Dimensional</td>\n",
       "      <td>L01.224.108.150.500</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>64454</th>\n",
       "      <td>D066330</td>\n",
       "      <td>Printing, Three-Dimensional</td>\n",
       "      <td>L01.296.110.150.500</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>64455</th>\n",
       "      <td>D066331</td>\n",
       "      <td>Laser-Evoked Potentials</td>\n",
       "      <td>G07.265.216.500.400.500</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>64456</th>\n",
       "      <td>D066331</td>\n",
       "      <td>Laser-Evoked Potentials</td>\n",
       "      <td>G11.561.200.500.400.500</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>64457 rows × 3 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "       mesh_id                    mesh_name         mesh_tree_number\n",
       "0      D000001                   Calcimycin      D02.355.291.933.125\n",
       "1      D000001                   Calcimycin      D02.540.576.625.125\n",
       "2      D000001                   Calcimycin      D03.633.100.221.173\n",
       "3      D000001                   Calcimycin      D04.345.241.654.125\n",
       "4      D000001                   Calcimycin      D04.345.674.625.125\n",
       "...        ...                          ...                      ...\n",
       "64452  D066330  Printing, Three-Dimensional              J01.897.564\n",
       "64453  D066330  Printing, Three-Dimensional      L01.224.108.150.500\n",
       "64454  D066330  Printing, Three-Dimensional      L01.296.110.150.500\n",
       "64455  D066331      Laser-Evoked Potentials  G07.265.216.500.400.500\n",
       "64456  D066331      Laser-Evoked Potentials  G11.561.200.500.400.500\n",
       "\n",
       "[64457 rows x 3 columns]"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Extract (mesh_id, mesh_tree_number) pairs\n",
    "rows = []\n",
    "for term in mesh:\n",
    "    mesh_id = term['mesh_id']\n",
    "    mesh_name = term['mesh_name']\n",
    "    for tree_number in term['tree_numbers']:\n",
    "        rows.append([mesh_id, mesh_name, tree_number])\n",
    "\n",
    "tn_df = pandas.DataFrame(rows, columns=['mesh_id', 'mesh_name', 'mesh_tree_number'])\n",
    "tn_df.to_csv(os.path.join(download_dir, 'tree-numbers.tsv'), sep='\\t', index=False)\n",
    "tn_df"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Diseases"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [],
   "source": [
    "def is_human_disease(tn):\n",
    "    \"\"\"Given a tree number, return whether the heirarchical path suggests a human disease.\"\"\"\n",
    "    # F03 (mental disorders)\n",
    "    if tn.startswith('F03'):\n",
    "        return True\n",
    "    # C01 though C21 and C24 -- C26\n",
    "    for i in list(range(1, 22)) + ['C24', 'C25', 'C26']:\n",
    "        if tn.startswith('C' + str(i).zfill(2)):\n",
    "            return True\n",
    "    # C23 exlcuding C23.888 (Symptoms and Signs)\n",
    "    if tn.startswith('C23') and not tn.startswith('C23.888'):\n",
    "        return True\n",
    "    return False"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "4851"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "diseases = {term['mesh_id'] for term in terms if any(map(is_human_disease, term['tree_numbers']))}\n",
    "len(diseases)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Symptoms"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Read HSDN symptoms\n",
    "url = 'https://raw.githubusercontent.com/LABrueggs/HSDN/master/Symptom-Occurence-Output.tsv' #https://github.com/LeoBman/HSDN/tree/master?tab=readme-ov-file\n",
    "hsdn_symptom_df = pandas.read_table(url, index_col=0)\n",
    "hsdn_symptoms = hsdn_symptom_df['MeSH Symptom ID']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
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       "        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>mesh_id</th>\n",
       "      <th>mesh_name</th>\n",
       "      <th>in_hsdn</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>D000006</td>\n",
       "      <td>Abdomen, Acute</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>131</th>\n",
       "      <td>D000067329</td>\n",
       "      <td>Obesity, Metabolically Benign</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>141</th>\n",
       "      <td>D000067404</td>\n",
       "      <td>Social Communication Disorder</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>147</th>\n",
       "      <td>D000067454</td>\n",
       "      <td>Childhood-Onset Fluency Disorder</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>168</th>\n",
       "      <td>D000067559</td>\n",
       "      <td>Specific Learning Disorder</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>30548</th>\n",
       "      <td>D065634</td>\n",
       "      <td>Cerebrospinal Fluid Leak</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>30549</th>\n",
       "      <td>D065635</td>\n",
       "      <td>Benign Paroxysmal Positional Vertigo</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>30645</th>\n",
       "      <td>D065906</td>\n",
       "      <td>Hyperlactatemia</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>30685</th>\n",
       "      <td>D066190</td>\n",
       "      <td>Allesthesia</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>30694</th>\n",
       "      <td>D066229</td>\n",
       "      <td>Speech Sound Disorder</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>505 rows × 3 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "          mesh_id                             mesh_name  in_hsdn\n",
       "5         D000006                        Abdomen, Acute        1\n",
       "131    D000067329         Obesity, Metabolically Benign        0\n",
       "141    D000067404         Social Communication Disorder        0\n",
       "147    D000067454      Childhood-Onset Fluency Disorder        0\n",
       "168    D000067559            Specific Learning Disorder        0\n",
       "...           ...                                   ...      ...\n",
       "30548     D065634              Cerebrospinal Fluid Leak        0\n",
       "30549     D065635  Benign Paroxysmal Positional Vertigo        0\n",
       "30645     D065906                       Hyperlactatemia        0\n",
       "30685     D066190                           Allesthesia        0\n",
       "30694     D066229                 Speech Sound Disorder        0\n",
       "\n",
       "[505 rows x 3 columns]"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# find MeSH symptoms\n",
    "symptoms = networkx.descendants(network, 'D012816') # signs and symptoms https://www.ncbi.nlm.nih.gov/mesh/68012816 \n",
    "symptom_df = mesh_df[mesh_df.mesh_id.isin(symptoms)]\n",
    "pandas.options.mode.chained_assignment = None\n",
    "symptom_df['in_hsdn'] = symptom_df.mesh_id.isin(hsdn_symptoms).astype(int)\n",
    "symptom_df.to_csv(os.path.join(download_dir, 'symptoms.tsv'), index=False, sep='\\t')\n",
    "symptom_df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "317"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "sum(symptom_df.in_hsdn)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "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>mesh_name</th>\n",
       "      <th>in_hsdn</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mesh_id</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>D000006</th>\n",
       "      <td>Abdomen, Acute</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>D000270</th>\n",
       "      <td>Adie Syndrome</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>D000326</th>\n",
       "      <td>Adrenoleukodystrophy</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>D000334</th>\n",
       "      <td>Aerophagy</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>D000370</th>\n",
       "      <td>Ageusia</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>D064250</th>\n",
       "      <td>Hypertriglyceridemic Waist</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>D065634</th>\n",
       "      <td>Cerebrospinal Fluid Leak</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>D065635</th>\n",
       "      <td>Benign Paroxysmal Positional Vertigo</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>D065906</th>\n",
       "      <td>Hyperlactatemia</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>D066190</th>\n",
       "      <td>Allesthesia</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>438 rows × 2 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                                    mesh_name  in_hsdn\n",
       "mesh_id                                               \n",
       "D000006                        Abdomen, Acute        1\n",
       "D000270                         Adie Syndrome        0\n",
       "D000326                  Adrenoleukodystrophy        0\n",
       "D000334                             Aerophagy        1\n",
       "D000370                               Ageusia        1\n",
       "...                                       ...      ...\n",
       "D064250            Hypertriglyceridemic Waist        0\n",
       "D065634              Cerebrospinal Fluid Leak        0\n",
       "D065635  Benign Paroxysmal Positional Vertigo        0\n",
       "D065906                       Hyperlactatemia        0\n",
       "D066190                           Allesthesia        0\n",
       "\n",
       "[438 rows x 2 columns]"
      ]
     },
     "execution_count": 27,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#compare with hetionet version  438 mesh terms \n",
    "symptom_hetionet = pandas.read_table('https://raw.githubusercontent.com/dhimmel/mesh/gh-pages/data/symptoms.tsv') #, index_col=0\n",
    "symptom_hetionet"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Side Effects"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "side_effects = networkx.descendants(network, 'D064420') # Drug-Related Side Effects and Adverse Reactions\n",
    "side_effect_df = mesh_df[mesh_df.mesh_id.isin(side_effects)]\n",
    "len(side_effect_df)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 93,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": []
  }
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