KG_Generator_Prototype.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "95894b68",
   "metadata": {},
   "source": [
    "# KG Generator Tool"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "e6cd5418",
   "metadata": {},
   "source": [
    "## Let's get started by importing relevant libraries"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "35a63bc0",
   "metadata": {},
   "outputs": [],
   "source": [
    "#pip install -r requirements.txt"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "b41fb83b",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\reagon.karki\\Anaconda3\\envs\\bycovid_new\\lib\\site-packages\\pybel\\struct\\operations.py:8: TqdmExperimentalWarning: Using `tqdm.autonotebook.tqdm` in notebook mode. Use `tqdm.tqdm` instead to force console mode (e.g. in jupyter console)\n",
      "  from tqdm.autonotebook import tqdm\n"
     ]
    }
   ],
   "source": [
    "from utils import *\n",
    "#from kg_gen_4_onlyProts import *\n",
    "\n",
    "from kg_gen_3 import *\n",
    "\n",
    "from IPython.display import Markdown, display"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "7556a3fb",
   "metadata": {},
   "source": [
    "## Please execute following cells"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "65dd794b",
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 216x216 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "text/markdown": [
       "<span style='color:blue'>**Welcome to the KG Generator tool. In the following steps, we will need some inputs from your side.**</span>"
      ],
      "text/plain": [
       "<IPython.core.display.Markdown object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Please enter the disease you are interested in and we will try to find the best matches for you.\n",
      "\n",
      "Input: depression\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th>index</th>\n",
       "      <th>id</th>\n",
       "      <th>name</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <td>0</td>\n",
       "      <td>EFO_0003761</td>\n",
       "      <td>unipolar depression</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>1</td>\n",
       "      <td>MONDO_0002050</td>\n",
       "      <td>depressive disorder</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2</td>\n",
       "      <td>EFO_0007453</td>\n",
       "      <td>postpartum depression</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>3</td>\n",
       "      <td>EFO_0009854</td>\n",
       "      <td>treatment resistant depression</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>4</td>\n",
       "      <td>EFO_0009842</td>\n",
       "      <td>respiratory depression</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>5</td>\n",
       "      <td>MONDO_0004985</td>\n",
       "      <td>bipolar disorder</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>6</td>\n",
       "      <td>MONDO_0008201</td>\n",
       "      <td>Perry syndrome</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>7</td>\n",
       "      <td>EFO_1002014</td>\n",
       "      <td>melancholia</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>8</td>\n",
       "      <td>MONDO_0010026</td>\n",
       "      <td>SHORT syndrome</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>9</td>\n",
       "      <td>EFO_0004257</td>\n",
       "      <td>neurotic disorder</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>10</td>\n",
       "      <td>EFO_0007006</td>\n",
       "      <td>depressive symptom measurement</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>11</td>\n",
       "      <td>HP_0009722</td>\n",
       "      <td>Dental enamel pits</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>12</td>\n",
       "      <td>EFO_0007704</td>\n",
       "      <td>depressive episode measurement</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>13</td>\n",
       "      <td>EFO_0005411</td>\n",
       "      <td>schizoaffective disorder</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>14</td>\n",
       "      <td>EFO_0005800</td>\n",
       "      <td>substance withdrawal syndrome</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>15</td>\n",
       "      <td>HP_0000490</td>\n",
       "      <td>Deeply set eye</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>16</td>\n",
       "      <td>HP_0001075</td>\n",
       "      <td>Atrophic scars</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>17</td>\n",
       "      <td>EFO_0008623</td>\n",
       "      <td>dysthymic disorder</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>18</td>\n",
       "      <td>MONDO_0014024</td>\n",
       "      <td>hereditary spastic paraplegia 43</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>19</td>\n",
       "      <td>MONDO_0004169</td>\n",
       "      <td>premenstrual tension</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>"
      ],
      "text/plain": [
       "<IPython.core.display.HTML object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/markdown": [
       "<span style='color:None'>**Here you go! Hopefully your disease of interest is in the list. If so, let's get started.**</span>"
      ],
      "text/plain": [
       "<IPython.core.display.Markdown object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Please enter the index value of your disease of interest. Input: 0\n",
      "\n",
      "\n",
      "Please enter the clinical trial phase of chemicals which should be identified by the workflow. Use a number between 1 (early phase) and 4 (FDA approved). For example, if you use 3, the KG will fetch chemicals that are in phase 3. Also, remember that lower the input value, higher will be the number of identified chemicals and therefore the running time of workflow also increases.\n",
      "\n",
      "\n",
      "Your desired clinical trial phase: 1\n",
      "\n",
      "\n",
      "Please provide a name for you KG. Input: depression_dec7\n",
      "\n",
      "\n",
      "We have identified 2074 proteins (Swiss-Prot) associated with the disease. Please note that the proteins identified may not be unique if you combined two or more diseases. Following is a histogram that shows distribution of proteins based on scores provided by OpenTargets. The scores are influenced by various factors such as genetic associations, expression, mutations, known pathways, targeting drugs and so on.\n",
      "\n",
      "A total of 2069 unique proteins have been identified.\n",
      "\n",
      "\n",
      "Displaying top 20 genes\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th>Protein</th>\n",
       "      <th>ENSG</th>\n",
       "      <th>UniProt</th>\n",
       "      <th>Source</th>\n",
       "      <th>Score</th>\n",
       "      <th>disease_id</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <td>DRD2</td>\n",
       "      <td>ENSG00000149295</td>\n",
       "      <td>P14416</td>\n",
       "      <td>uniprot_swissprot</td>\n",
       "      <td>0.710311</td>\n",
       "      <td>EFO_0003761</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>GRIN2A</td>\n",
       "      <td>ENSG00000183454</td>\n",
       "      <td>Q12879</td>\n",
       "      <td>uniprot_swissprot</td>\n",
       "      <td>0.653392</td>\n",
       "      <td>EFO_0003761</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>SLC6A4</td>\n",
       "      <td>ENSG00000108576</td>\n",
       "      <td>P31645</td>\n",
       "      <td>uniprot_swissprot</td>\n",
       "      <td>0.631193</td>\n",
       "      <td>EFO_0003761</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>SLC6A3</td>\n",
       "      <td>ENSG00000142319</td>\n",
       "      <td>Q01959</td>\n",
       "      <td>uniprot_swissprot</td>\n",
       "      <td>0.617103</td>\n",
       "      <td>EFO_0003761</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>SLC6A2</td>\n",
       "      <td>ENSG00000103546</td>\n",
       "      <td>P23975</td>\n",
       "      <td>uniprot_swissprot</td>\n",
       "      <td>0.614781</td>\n",
       "      <td>EFO_0003761</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>HTR2A</td>\n",
       "      <td>ENSG00000102468</td>\n",
       "      <td>P28223</td>\n",
       "      <td>uniprot_swissprot</td>\n",
       "      <td>0.613892</td>\n",
       "      <td>EFO_0003761</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>GABRA1</td>\n",
       "      <td>ENSG00000022355</td>\n",
       "      <td>P14867</td>\n",
       "      <td>uniprot_swissprot</td>\n",
       "      <td>0.609749</td>\n",
       "      <td>EFO_0003761</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>HTR2C</td>\n",
       "      <td>ENSG00000147246</td>\n",
       "      <td>P28335</td>\n",
       "      <td>uniprot_swissprot</td>\n",
       "      <td>0.608697</td>\n",
       "      <td>EFO_0003761</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>GABRG3</td>\n",
       "      <td>ENSG00000182256</td>\n",
       "      <td>Q99928</td>\n",
       "      <td>uniprot_swissprot</td>\n",
       "      <td>0.608363</td>\n",
       "      <td>EFO_0003761</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>GABRA2</td>\n",
       "      <td>ENSG00000151834</td>\n",
       "      <td>P47869</td>\n",
       "      <td>uniprot_swissprot</td>\n",
       "      <td>0.608178</td>\n",
       "      <td>EFO_0003761</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>GABRA5</td>\n",
       "      <td>ENSG00000186297</td>\n",
       "      <td>P31644</td>\n",
       "      <td>uniprot_swissprot</td>\n",
       "      <td>0.606515</td>\n",
       "      <td>EFO_0003761</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>ADRA2A</td>\n",
       "      <td>ENSG00000150594</td>\n",
       "      <td>P08913</td>\n",
       "      <td>uniprot_swissprot</td>\n",
       "      <td>0.606146</td>\n",
       "      <td>EFO_0003761</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>GABRA3</td>\n",
       "      <td>ENSG00000011677</td>\n",
       "      <td>P34903</td>\n",
       "      <td>uniprot_swissprot</td>\n",
       "      <td>0.605591</td>\n",
       "      <td>EFO_0003761</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>GABRG2</td>\n",
       "      <td>ENSG00000113327</td>\n",
       "      <td>P18507</td>\n",
       "      <td>uniprot_swissprot</td>\n",
       "      <td>0.605037</td>\n",
       "      <td>EFO_0003761</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>HTR1A</td>\n",
       "      <td>ENSG00000178394</td>\n",
       "      <td>P08908</td>\n",
       "      <td>uniprot_swissprot</td>\n",
       "      <td>0.604962</td>\n",
       "      <td>EFO_0003761</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>GABRG1</td>\n",
       "      <td>ENSG00000163285</td>\n",
       "      <td>Q8N1C3</td>\n",
       "      <td>uniprot_swissprot</td>\n",
       "      <td>0.604667</td>\n",
       "      <td>EFO_0003761</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>ADRA2C</td>\n",
       "      <td>ENSG00000184160</td>\n",
       "      <td>P18825</td>\n",
       "      <td>uniprot_swissprot</td>\n",
       "      <td>0.604586</td>\n",
       "      <td>EFO_0003761</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>ADRA2B</td>\n",
       "      <td>ENSG00000274286</td>\n",
       "      <td>P18089</td>\n",
       "      <td>uniprot_swissprot</td>\n",
       "      <td>0.603662</td>\n",
       "      <td>EFO_0003761</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>GRIN2B</td>\n",
       "      <td>ENSG00000273079</td>\n",
       "      <td>Q13224</td>\n",
       "      <td>uniprot_swissprot</td>\n",
       "      <td>0.598315</td>\n",
       "      <td>EFO_0003761</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>SCN1A</td>\n",
       "      <td>ENSG00000144285</td>\n",
       "      <td>P35498</td>\n",
       "      <td>uniprot_swissprot</td>\n",
       "      <td>0.598179</td>\n",
       "      <td>EFO_0003761</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>"
      ],
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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "\n",
      "We recommend taking a threshold above 0.3 to exclude loosely associated proteins. \n",
      "Please enter your desired threshold: 0.0\n",
      "\n",
      "\n",
      "Alright, we are good to go now. Your KG is now being generated! Sit back and relax!!\n",
      "\n",
      " Total no. of proteins:  2074\n",
      "\n",
      "      Protein             ENSG UniProt             Source     Score  \\\n",
      "0       DRD2  ENSG00000149295  P14416  uniprot_swissprot  0.710311   \n",
      "1     GRIN2A  ENSG00000183454  Q12879  uniprot_swissprot  0.653392   \n",
      "2     SLC6A4  ENSG00000108576  P31645  uniprot_swissprot  0.631193   \n",
      "3     SLC6A3  ENSG00000142319  Q01959  uniprot_swissprot  0.617103   \n",
      "4     SLC6A2  ENSG00000103546  P23975  uniprot_swissprot  0.614781   \n",
      "...      ...              ...     ...                ...       ...   \n",
      "2069   ALDOC  ENSG00000109107  P09972  uniprot_swissprot  0.001478   \n",
      "2070    WFS1  ENSG00000109501  O76024  uniprot_swissprot  0.001478   \n",
      "2071  LRATD1  ENSG00000162981  Q96KN4  uniprot_swissprot  0.001478   \n",
      "2072  NAP1L4  ENSG00000205531  Q99733  uniprot_swissprot  0.001478   \n",
      "2073    FGF1  ENSG00000113578  P05230  uniprot_swissprot  0.001478   \n",
      "\n",
      "       disease_id  \n",
      "0     EFO_0003761  \n",
      "1     EFO_0003761  \n",
      "2     EFO_0003761  \n",
      "3     EFO_0003761  \n",
      "4     EFO_0003761  \n",
      "...           ...  \n",
      "2069  EFO_0003761  \n",
      "2070  EFO_0003761  \n",
      "2071  EFO_0003761  \n",
      "2072  EFO_0003761  \n",
      "2073  EFO_0003761  \n",
      "\n",
      "[2074 rows x 6 columns]\n"
     ]
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "a065cbc7bc864b6d9529668f6a683dcd",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "Fetching Protein-related info:   0%|          | 0/2069 [00:00<?, ?it/s]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "A total of 109 drugs have been identified. Now fetching relevant data\n"
     ]
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "8858741cde5849f68ca4be7c466ebd79",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "Retrieving mechanisms from ChEMBL:   0%|          | 0/109 [00:00<?, ?it/s]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "4ff1e35b9d8447ffa1564df28e4fe774",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "Retrieving bioassays from ChEMBL:   0%|          | 0/109 [00:00<?, ?it/s]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "f2c05fba44af463ebf085237e5974bc9",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "Filtering UniProt proteins from ChEMBL:   0%|          | 0/106 [00:00<?, ?it/s]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "5a9827d0a4bb47249109c9be0c3cfd7e",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "Filtering human proteins from ChEMBL:   0%|          | 0/106 [00:00<?, ?it/s]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "07f832aed53342bf8cd2bda56a555b9c",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "Populating ChEMBL data for human proteins:   0%|          | 0/106 [00:00<?, ?it/s]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "50b2b1345816451fb863f03cfe9c3e15",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "Populating Uniprot edges:   0%|          | 0/2069 [00:00<?, ?it/s]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "e1fb89c7d6284df6bb4bb30343872266",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "Populating Chemical-MoA edges:   0%|          | 0/71 [00:00<?, ?it/s]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "b52fb8d978094c8eb14e1200fee85ad1",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "Adding bioassay edges to BEL:   0%|          | 0/60 [00:00<?, ?it/s]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "6169e842cd3e409989980b7264e6523a",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "Retrieving Adverse Effects for each drug:   0%|          | 0/109 [00:00<?, ?it/s]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "A total of 1735 SNPs have been identified from DisGeNET. Now adding relevant data\n",
      "\n",
      "\n"
     ]
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "3b1444088f1c4df39a644e4fc0a87a02",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "Adding disease associated SNPs:   0%|          | 0/1352 [00:00<?, ?it/s]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Your KG is now generated! \n",
      "\n",
      "Now let's save all the files that were created in the process. \n",
      "\n",
      "Please enter the location (e.g. 'C:\\Users\\rkarki\\Documents\\kg\\' ) where KG files should be stored. A folder will be created automatically. \n",
      "\n",
      "Input: C:\\Users\\reagon.karki\\Documents\\GitHub\\kgg\\data\\kgs\\ndd\\\n"
     ]
    },
    {
     "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": [
    "kg = createKG()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "1b19c277",
   "metadata": {},
   "outputs": [],
   "source": [
    "#kg.to_csv('data/kgs/test/cvd.csv')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "8b36d40f",
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "            <h2>Metadata</h2>\n",
       "            <table>\n",
       "<tbody>\n",
       "<tr><td>Name   </td><td>depression_dec7</td></tr>\n",
       "<tr><td>Version</td><td>0.0.1          </td></tr>\n",
       "</tbody>\n",
       "</table>\n",
       "            <h2>Statistics</h2>\n",
       "            <table>\n",
       "<tbody>\n",
       "<tr><td>Nodes          </td><td style=\"text-align: right;\">15047       </td></tr>\n",
       "<tr><td>Namespaces     </td><td style=\"text-align: right;\">    9       </td></tr>\n",
       "<tr><td>Edges          </td><td style=\"text-align: right;\">89531       </td></tr>\n",
       "<tr><td>Annotations    </td><td style=\"text-align: right;\">    0       </td></tr>\n",
       "<tr><td>Citations      </td><td style=\"text-align: right;\">    4       </td></tr>\n",
       "<tr><td>Authors        </td><td style=\"text-align: right;\">    0       </td></tr>\n",
       "<tr><td>Components     </td><td style=\"text-align: right;\">  273       </td></tr>\n",
       "<tr><td>Warnings       </td><td style=\"text-align: right;\">    0       </td></tr>\n",
       "<tr><td>Network Density</td><td style=\"text-align: right;\">    0.000395</td></tr>\n",
       "</tbody>\n",
       "</table>\n",
       "            <h2>Nodes</h2>\n",
       "            <table>\n",
       "<thead>\n",
       "<tr><th>Type             </th><th style=\"text-align: right;\">  Count</th><th>Example                                         </th></tr>\n",
       "</thead>\n",
       "<tbody>\n",
       "<tr><td>BiologicalProcess</td><td style=\"text-align: right;\">   9060</td><td>bp(GOBP:&quot;calcium import into the mitochondrion&quot;)</td></tr>\n",
       "<tr><td>Protein          </td><td style=\"text-align: right;\">   2429</td><td>p(HGNC:DAZAP2)                                  </td></tr>\n",
       "<tr><td>Pathology        </td><td style=\"text-align: right;\">   1425</td><td>path(SideEffect:delusion)                       </td></tr>\n",
       "<tr><td>Gene             </td><td style=\"text-align: right;\">   1136</td><td>g(dbSNP:rs2075650)                              </td></tr>\n",
       "<tr><td>Abundance        </td><td style=\"text-align: right;\">    997</td><td>a(ChEMBLAssay:CHEMBL616366)                     </td></tr>\n",
       "</tbody>\n",
       "</table>\n",
       "            <h2>Namespaces</h2>\n",
       "            <table>\n",
       "<thead>\n",
       "<tr><th>Prefix     </th><th>Name                            </th><th style=\"text-align: right;\">  Count</th><th>Example                                                            </th></tr>\n",
       "</thead>\n",
       "<tbody>\n",
       "<tr><td>GOBP       </td><td>Gene Ontology                   </td><td style=\"text-align: right;\">   6875</td><td>bp(GOBP:&quot;positive regulation of inflammatory response to wounding&quot;)</td></tr>\n",
       "<tr><td>HGNC       </td><td>HUGO Gene Nomenclature Committee</td><td style=\"text-align: right;\">   2429</td><td>p(HGNC:INSL5)                                                      </td></tr>\n",
       "<tr><td>GOMF       </td><td>Gene Ontology                   </td><td style=\"text-align: right;\">   1962</td><td>bp(GOMF:&quot;dihydropyrimidine dehydrogenase (NADP+) activity&quot;)        </td></tr>\n",
       "<tr><td>SideEffect </td><td>                                </td><td style=\"text-align: right;\">   1425</td><td>path(SideEffect:&quot;caesarean section&quot;)                               </td></tr>\n",
       "<tr><td>dbSNP      </td><td>NCBI dbSNP                      </td><td style=\"text-align: right;\">   1136</td><td>g(dbSNP:rs1011633)                                                 </td></tr>\n",
       "<tr><td>ChEMBLAssay</td><td>                                </td><td style=\"text-align: right;\">    889</td><td>a(ChEMBLAssay:CHEMBL751261)                                        </td></tr>\n",
       "<tr><td>Reactome   </td><td>Reactome                        </td><td style=\"text-align: right;\">    179</td><td>bp(Reactome:&quot;Cargo recognition for clathrin-mediated endocytosis.&quot;)</td></tr>\n",
       "<tr><td>ChEMBL     </td><td>ChEMBL                          </td><td style=\"text-align: right;\">    108</td><td>a(ChEMBL:CHEMBL1201735)                                            </td></tr>\n",
       "<tr><td>MOA        </td><td>                                </td><td style=\"text-align: right;\">     44</td><td>bp(MOA:&quot;Cyclooxygenase-2 inhibitor&quot;)                               </td></tr>\n",
       "</tbody>\n",
       "</table>\n",
       "            <h2>Edges</h2>\n",
       "            <table>\n",
       "<thead>\n",
       "<tr><th>Edge Type                              </th><th style=\"text-align: right;\">  Count</th><th>Example                                                                                   </th></tr>\n",
       "</thead>\n",
       "<tbody>\n",
       "<tr><td>Protein association BiologicalProcess  </td><td style=\"text-align: right;\">  36907</td><td>p(HGNC:CX3CR1) association bp(GOBP:&quot;negative regulation of interleukin-1 beta production&quot;)</td></tr>\n",
       "<tr><td>Pathology association Abundance        </td><td style=\"text-align: right;\">   4305</td><td>path(SideEffect:&quot;pulmonary valve stenosis&quot;) association a(ChEMBL:CHEMBL1708)              </td></tr>\n",
       "<tr><td>Protein association Gene               </td><td style=\"text-align: right;\">   1395</td><td>p(HGNC:BDNF) association g(dbSNP:rs1048220)                                               </td></tr>\n",
       "<tr><td>Protein association Abundance          </td><td style=\"text-align: right;\">    890</td><td>p(HGNC:AR) association a(ChEMBLAssay:CHEMBL647748)                                        </td></tr>\n",
       "<tr><td>BiologicalProcess association Abundance</td><td style=\"text-align: right;\">    112</td><td>bp(MOA:&quot;Beta-2 adrenergic receptor partial agonist&quot;) association a(ChEMBL:CHEMBL500)      </td></tr>\n",
       "<tr><td>Abundance decreases Protein            </td><td style=\"text-align: right;\">     87</td><td>a(ChEMBL:CHEMBL1921847) decreases p(HGNC:OPRK1)                                           </td></tr>\n",
       "<tr><td>Abundance increases Protein            </td><td style=\"text-align: right;\">     24</td><td>a(ChEMBL:CHEMBL1162) increases p(HGNC:PGR)                                                </td></tr>\n",
       "</tbody>\n",
       "</table>\n"
      ],
      "text/plain": [
       "<pybel.struct.graph.SummarizeDispatch at 0x1e0d6c4d6c0>"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "kg.summarize"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "eb82d4c6",
   "metadata": {},
   "outputs": [],
   "source": [
    "to_jupyter(kg)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "9cbd4014",
   "metadata": {},
   "outputs": [],
   "source": [
    "KG_namespace_plot(kg)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "82791e85",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "f3a51158",
   "metadata": {},
   "outputs": [],
   "source": [
    "test = pd.read_csv('data/kgs/test/t2dm.csv')\n",
    "test.head(5)\n",
    "test = list(set(list(test['UniProt'])))\n",
    "len(test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "f7b078b0",
   "metadata": {},
   "outputs": [],
   "source": [
    "def ExtractFromUniProt(uniprot_id) -> dict:\n",
    "    \"\"\"Uniprot parser to retrieve information about OMIM disease, reactome pathway, biological process,\n",
    "     and molecular functions.\n",
    "\n",
    "    :param uniprot_id:\n",
    "    :return:\n",
    "    \"\"\"\n",
    "    Uniprot_Dict = []\n",
    "\n",
    "    mapped_uprot = []\n",
    "\n",
    "    for id in tqdm(uniprot_id, desc='Fetching Protein-related info'):\n",
    "        \n",
    "        print(id)\n",
    "\n",
    "        # Retrieve data for id in text format if found in uniprot\n",
    "        ret_uprot = requests.get(\n",
    "            'https://www.uniprot.org/uniprot/' + id + '.txt'\n",
    "        ).text.split('\\n')\n",
    "        \n",
    "        #return(ret_uprot)\n",
    "\n",
    "        if ret_uprot == ['']:\n",
    "            continue\n",
    "\n",
    "        id_copy = id\n",
    "        mapped_uprot.append(id_copy)\n",
    "        i = 0\n",
    "        j = 0\n",
    "        k = 0\n",
    "        id = {}\n",
    "        id['Disease'] = {}\n",
    "        id['Reactome'] = {}\n",
    "        id['Function'] = {}\n",
    "        id['BioProcess'] = {}\n",
    "        id['Gene'] = {}\n",
    "\n",
    "        # parse each line looking for info about disease, pathway, funcn, bp and so on\n",
    "        for line in ret_uprot:\n",
    "            # parse lines with disease and extract disease names and omim ids\n",
    "            if '-!- DISEASE:' in line:\n",
    "                if ('[MIM:' in line):\n",
    "                    dis = line.split(':')\n",
    "                    id['Disease'].update({dis[1][1:-5]: dis[2][:-1]})\n",
    "\n",
    "            # extract reactome ids and names\n",
    "            if 'Reactome;' in line:\n",
    "                ract = line.split(';')\n",
    "                id['Reactome'].update({ract[2][1:-2]: ract[1][1:]})\n",
    "\n",
    "            # look for functions\n",
    "            if ' F:' in line:\n",
    "                if j < 5:\n",
    "                    fn = line.split(';')\n",
    "                    id['Function'].update({fn[2][3:]: fn[1][1:]})\n",
    "                    j += 1\n",
    "\n",
    "            # look for biological processes\n",
    "            if ' P:' in line and 'GO;' in line:\n",
    "                if i < 5:\n",
    "                    bp = line.split(';')\n",
    "                    # bp returns list with GO ids and names\n",
    "                    id['BioProcess'].update({bp[2][3:]: bp[1][1:]})\n",
    "                    i += 1\n",
    "\n",
    "            if 'GN   Name' in line:\n",
    "                if k == 0:\n",
    "                    gene = line.split('=')\n",
    "                    gene = gene[1].split(' ')\n",
    "                    if ';' in gene[0]:\n",
    "                        gene = gene[0].split(';')\n",
    "                        gene = {'Gene': gene[0]}\n",
    "                    else:\n",
    "                        gene = {'Gene': gene[0]}\n",
    "                    id.update(gene)\n",
    "                    k += 1\n",
    "\n",
    "        Uniprot_Dict.append(id)\n",
    "\n",
    "    Uniprot_Dict = dict(zip(mapped_uprot, Uniprot_Dict))\n",
    "\n",
    "    return Uniprot_Dict"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "68bce560",
   "metadata": {},
   "outputs": [],
   "source": [
    "xx = ExtractFromUniProt(test[4790:4800])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "703cc6c0",
   "metadata": {},
   "outputs": [],
   "source": [
    "xx"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "6c2cb501",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "52ef209d",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "7e25f815",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "a0c8c008",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "2651ef06",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "id": "8b7ff7e7",
   "metadata": {},
   "source": [
    "# Additional stuff"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "04d8905e",
   "metadata": {},
   "outputs": [],
   "source": [
    "infile= open('data/kgs/ndd/ad/ad.pkl','rb')\n",
    "graph= pickle.load(infile)\n",
    "infile.close()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "e38ef4f0",
   "metadata": {},
   "outputs": [],
   "source": [
    "graph.summarize"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "15f3a2ec",
   "metadata": {},
   "outputs": [],
   "source": [
    "prot = pd.read_csv('data/kgs/ndd/ad/diseaseAssociatedProteins.csv')\n",
    "prot = list(prot['Protein'])\n",
    "prot = prot[1:6]\n",
    "\n",
    "chem = pd.read_csv('data/kgs/ndd/ad/diseaseAssociatedDrugs.csv')\n",
    "chem = list(chem['drugId'])\n",
    "chem = chem[1:6]\n",
    "\n",
    "prot_chem = prot + chem"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "3fd3155a",
   "metadata": {},
   "outputs": [],
   "source": [
    "prot_chem"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "1ffa6482",
   "metadata": {},
   "outputs": [],
   "source": [
    "query_graph = filter_graph(graph,prot_chem)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "32c30f69",
   "metadata": {
    "scrolled": false
   },
   "outputs": [],
   "source": [
    "to_jupyter(query_graph)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "df642fa9",
   "metadata": {},
   "outputs": [],
   "source": [
    "prtn_attr = nx.get_node_attributes(graph,'UniProt')\n",
    "bionote_attr = nx.get_node_attributes(graph,'3Dbio')\n",
    "\n",
    "for node in graph.nodes():\n",
    "    unique_node = []\n",
    "    \n",
    "    if node.name in prot:\n",
    "        if node not in unique_node:\n",
    "            \n",
    "            unique_node.append(node)\n",
    "            print(node.name)\n",
    "       \n",
    "            if isinstance(node,pybel.dsl.Abundance):\n",
    "                print(chem_attr[node])\n",
    "                \n",
    "            if isinstance(node,pybel.dsl.Protein):\n",
    "                print(prtn_attr[node])\n",
    "                \n",
    "            if isinstance(node,pybel.dsl.Protein):\n",
    "                print(bionote_attr[node])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "117ac294",
   "metadata": {},
   "outputs": [],
   "source": [
    "17*40000000*20000000"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "18245ceb",
   "metadata": {},
   "outputs": [],
   "source": [
    "13600000000000000/105004"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "8cb0467e",
   "metadata": {},
   "outputs": [],
   "source": [
    "129518875471/20509"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "16cc64af",
   "metadata": {},
   "outputs": [],
   "source": [
    "def GetDiseaseAssociatedDrugs(disease_id,CT_phase):\n",
    "\n",
    "    efo_id = disease_id\n",
    "    size = getDrugCount(efo_id)\n",
    "\n",
    "    query_string = \"\"\"\n",
    "        query associatedTargets($my_efo_id: String!, $my_size: Int){\n",
    "          disease(efoId: $my_efo_id){\n",
    "            id\n",
    "            name\n",
    "            knownDrugs(size:$my_size){\n",
    "                uniqueTargets\n",
    "                uniqueDrugs\n",
    "                count\n",
    "                rows{\n",
    "                    approvedSymbol\n",
    "                    approvedName\n",
    "                    prefName\n",
    "                    drugType\n",
    "                    drugId\n",
    "                    phase\n",
    "                    ctIds\n",
    "                }\n",
    "\n",
    "            }\n",
    "          }\n",
    "        }\n",
    "\n",
    "    \"\"\"\n",
    "\n",
    "    #replace $efo_id with value from efo_id\n",
    "    #query_string = query_string.replace(\"$efo_id\",f'\"{efo_id}\"')\n",
    "    #query_string = query_string.replace(\"$efo_id\",f'\"{efo_id}\"')\n",
    "\n",
    "    # Set variables object of arguments to be passed to endpoint\n",
    "    variables = {\"my_efo_id\": efo_id, \"my_size\": size}\n",
    "\n",
    "    # Set base URL of GraphQL API endpoint\n",
    "    base_url = \"https://api.platform.opentargets.org/api/v4/graphql\"\n",
    "\n",
    "    # Perform POST request and check status code of response\n",
    "    r = requests.post(base_url, json={\"query\": query_string, \"variables\": variables})\n",
    "    #r = requests.post(base_url, json={\"query\": query_string})\n",
    "    #print(r.status_code)\n",
    "\n",
    "    # Transform API response from JSON into Python dictionary and print in console\n",
    "    api_response = json.loads(r.text)\n",
    "    return(api_response)\n",
    "    \n",
    "    if api_response['data']['disease']['knownDrugs']['uniqueTargets'] == 0:\n",
    "        \n",
    "        print('No drugs found in clinical trials for ' + str(api_response['data']['disease']['name']))\n",
    "        \n",
    "    else:\n",
    "    \n",
    "        df = pd.DataFrame(api_response['data']['disease']['knownDrugs']['rows'])\n",
    "        df = df.loc[df['phase'] >= int(CT_phase),:]\n",
    "\n",
    "        if not df.empty:\n",
    "            df['id'] = efo_id\n",
    "            df['disease'] = api_response['data']['disease']['name']\n",
    "            #print('Your dataframe is ready')\n",
    "            return(df)\n",
    "    \n",
    "#     else:\n",
    "#         print('No drugs found in clinical trials')\n",
    "    "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "3d983db4",
   "metadata": {},
   "outputs": [],
   "source": [
    "tt = GetDiseaseAssociatedDrugs('MONDO_0008903',1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "29ae63e9",
   "metadata": {},
   "outputs": [],
   "source": [
    "tt"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "853c2b4c",
   "metadata": {},
   "outputs": [],
   "source": []
  }
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