{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Generating Cell Embeddings"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Please install the following packages before running this notebook:\n",
    "```bash\n",
    "pip install pandas transformers\n",
    "```\n",
    "\n",
    "<!-- In order to generate cell embeddings, we will use the `cell2location` package. This package is designed to map the location of single cells in situ, using a reference atlas. It uses a Bayesian model to estimate the expression of each gene in each cell type, and the proportion of each cell type in each location. The model is implemented in PyMC3, and the package provides a high-level interface to the model, as well as tools for visualisation and interpretation of the results. -->"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "# !pip install pandas transformers"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Load Toy Dataset"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "2141\n",
      "2141\n"
     ]
    }
   ],
   "source": [
    "import os\n",
    "import pandas as pd\n",
    "\n",
    "data_dir = 'data/'\n",
    "protac_df = pd.read_csv(os.path.join(data_dir, 'PROTAC-Degradation-DB.csv'))\n",
    "print(len(protac_df))\n",
    "print(len(protac_df['POI Sequence'].dropna()))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Number of cell lines: 138\n"
     ]
    }
   ],
   "source": [
    "protac_cells = protac_df['Cell Line Identifier'].dropna().unique().tolist()\n",
    "print(f'Number of cell lines: {len(protac_cells)}')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Load Cellosaurus"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The Cellosaurus is a knowledge resource on cell lines. It attempts to describe all cell lines used in biomedical research. It is the result of curation efforts by the Swiss Institute of Bioinformatics and ExPASy.\n",
    "\n",
    "The notebook expects a file named `cellosaurus.txt` in the `data` directory of the repository. This file can be downloaded from the [Cellosaurus FTP](https://ftp.expasy.org/databases/cellosaurus/cellosaurus.txt)."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "{'ID': '#132 PC3-1-SC-E8', 'AC': 'CVCL_B0T9', 'SY': 'Z48-5MG-70', 'DR': ['Wikidata; Q108819335'], 'RX': ['Patent=EP0501779A1;'], 'CC': ['Group: Patented cell line.', 'Registration: International Depositary Authority, American Type Culture Collection (ATCC); HB-10564.', 'Monoclonal antibody isotype: IgG2a.', 'Monoclonal antibody target: UniProtKB; P47712; Human PLA2G4A.'], 'OX': 'NCBI_TaxID=10090; ! Mus musculus (Mouse)', 'HI': 'CVCL_D145 ! HL-1 Friendly Myeloma-653', 'CA': 'Hybridoma', 'DT': 'Created: 23-09-21; Last updated: 30-01-24; Version: 4'}\n"
     ]
    }
   ],
   "source": [
    "def parse_cellosaurus_text(file_path):\n",
    "    \"\"\"\n",
    "    Parse a Cellosaurus text file and return a list of cell line entries.\n",
    "\n",
    "    :param file_path: Path to the Cellosaurus text file.\n",
    "    :return: A list of dictionaries, each representing a cell line entry.\n",
    "    \"\"\"\n",
    "    with open(file_path, 'r') as file:\n",
    "        lines = file.readlines()\n",
    "\n",
    "    cell_lines = []\n",
    "    cell_line_entry = {}\n",
    "    for line in lines:\n",
    "        if line.startswith(\"ID   \"):\n",
    "            if cell_line_entry:\n",
    "                cell_lines.append(cell_line_entry)\n",
    "                cell_line_entry = {}\n",
    "            cell_line_entry['ID'] = line[5:].strip()\n",
    "        elif line.startswith(\"AC   \"):\n",
    "            cell_line_entry['AC'] = line[5:].strip()\n",
    "        elif line.startswith(\"SY   \"):\n",
    "            cell_line_entry['SY'] = line[5:].strip()\n",
    "        elif line.startswith(\"DR   \"):\n",
    "            cell_line_entry.setdefault('DR', []).append(line[5:].strip())\n",
    "        elif line.startswith(\"RX   \"):\n",
    "            cell_line_entry.setdefault('RX', []).append(line[5:].strip())\n",
    "        elif line.startswith(\"CC   \"):\n",
    "            cell_line_entry.setdefault('CC', []).append(line[5:].strip())\n",
    "        elif line.startswith(\"OX   \"):\n",
    "            cell_line_entry['OX'] = line[5:].strip()\n",
    "        elif line.startswith(\"HI   \"):\n",
    "            cell_line_entry['HI'] = line[5:].strip()\n",
    "        elif line.startswith(\"CA   \"):\n",
    "            cell_line_entry['CA'] = line[5:].strip()\n",
    "        elif line.startswith(\"DT   \"):\n",
    "            cell_line_entry['DT'] = line[5:].strip()\n",
    "        # Add similar elif blocks for other line codes as needed\n",
    "\n",
    "    # Add the last entry\n",
    "    if cell_line_entry:\n",
    "        cell_lines.append(cell_line_entry)\n",
    "\n",
    "    return cell_lines\n",
    "\n",
    "\n",
    "# Example usage\n",
    "file_path = os.path.join(data_dir, \"cellosaurus.txt\")\n",
    "cell_lines = parse_cellosaurus_text(file_path)\n",
    "for cell_line in cell_lines:\n",
    "    print(cell_line)\n",
    "    break"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "cell2data = {}\n",
    "for cell_line in cell_lines:\n",
    "    cell2data[cell_line['ID']] = cell_line"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Knockout cell\n",
      "Miscellaneous\n",
      "Discontinued\n",
      "Omics\n",
      "Misspelling\n",
      "Population\n",
      "Transfected with\n",
      "Donor information\n",
      "From\n",
      "Breed/subspecies\n",
      "Problematic cell line\n",
      "Derived from site\n",
      "Biotechnology\n",
      "Registration\n",
      "Anecdotal\n",
      "Virology\n",
      "Monoclonal antibody isotype\n",
      "Monoclonal antibody target\n",
      "Sequence variation\n",
      "Characteristics\n",
      "Microsatellite instability\n",
      "Group\n",
      "Selected for resistance to\n",
      "HLA typing\n",
      "Senescence\n",
      "Part of\n",
      "Doubling time\n",
      "Karyotypic information\n",
      "Caution\n",
      "Genome ancestry\n",
      "Cell type\n",
      "Transformant\n"
     ]
    }
   ],
   "source": [
    "cc_headers = set()\n",
    "\n",
    "for i, cell_line in enumerate(cell_lines):\n",
    "    if 'CC' in cell_line:\n",
    "        # Add the CC headers to the set\n",
    "        cc_headers.update([cc_line.split(':')[0].strip()\n",
    "                          for cc_line in cell_line['CC']])\n",
    "\n",
    "for cc_header in cc_headers:\n",
    "    print(cc_header)\n",
    "\n",
    "cc_headers_to_ignore = [\n",
    "    'Miscellaneous',\n",
    "    'From',\n",
    "    'Anecdotal',\n",
    "    'Misspelling',\n",
    "    'Part of',\n",
    "    'Registration',\n",
    "    'Discontinued',\n",
    "]"
   ]
  },
  {
   "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>ID</th>\n",
       "      <th>AC</th>\n",
       "      <th>DR</th>\n",
       "      <th>RX</th>\n",
       "      <th>OX</th>\n",
       "      <th>HI</th>\n",
       "      <th>CA</th>\n",
       "      <th>Group</th>\n",
       "      <th>Monoclonal antibody isotype</th>\n",
       "      <th>Monoclonal antibody target</th>\n",
       "      <th>...</th>\n",
       "      <th>Problematic cell line</th>\n",
       "      <th>Knockout cell</th>\n",
       "      <th>Karyotypic information</th>\n",
       "      <th>Virology</th>\n",
       "      <th>Senescence</th>\n",
       "      <th>Biotechnology</th>\n",
       "      <th>Donor information</th>\n",
       "      <th>Caution</th>\n",
       "      <th>Genome ancestry</th>\n",
       "      <th>Microsatellite instability</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>#132 PC3-1-SC-E8</td>\n",
       "      <td>CVCL_B0T9</td>\n",
       "      <td>[Wikidata; Q108819335]</td>\n",
       "      <td>[Patent=EP0501779A1;]</td>\n",
       "      <td>NCBI_TaxID=10090; ! Mus musculus (Mouse)</td>\n",
       "      <td>CVCL_D145 ! HL-1 Friendly Myeloma-653</td>\n",
       "      <td>Hybridoma</td>\n",
       "      <td>Patented cell line.</td>\n",
       "      <td>IgG2a.</td>\n",
       "      <td>UniProtKB; P47712; Human PLA2G4A.</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>#132 PL12 SC-D1</td>\n",
       "      <td>CVCL_B0T8</td>\n",
       "      <td>[Wikidata; Q108819336]</td>\n",
       "      <td>[Patent=EP0501779A1;]</td>\n",
       "      <td>NCBI_TaxID=10090; ! Mus musculus (Mouse)</td>\n",
       "      <td>CVCL_D145 ! HL-1 Friendly Myeloma-653</td>\n",
       "      <td>Hybridoma</td>\n",
       "      <td>Patented cell line.</td>\n",
       "      <td>IgG1.</td>\n",
       "      <td>UniProtKB; P47712; Human PLA2G4A.</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>#15310-LN</td>\n",
       "      <td>CVCL_E548</td>\n",
       "      <td>[dbMHC; 48439, ECACC; 94050311, IHW; IHW09326,...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NCBI_TaxID=9606; ! Homo sapiens (Human)</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Transformed cell line</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>#16-15</td>\n",
       "      <td>CVCL_KA96</td>\n",
       "      <td>[RCB; RCB4635, Wikidata; Q54422067]</td>\n",
       "      <td>[PubMed=25400923;]</td>\n",
       "      <td>NCBI_TaxID=10116; ! Rattus norvegicus (Rat)</td>\n",
       "      <td>CVCL_4032 ! P3X63Ag8.653</td>\n",
       "      <td>Hybridoma</td>\n",
       "      <td>NaN</td>\n",
       "      <td>IgM.</td>\n",
       "      <td>UniProtKB; Q5T5X7; Human BEND3.</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>#40a</td>\n",
       "      <td>CVCL_IW91</td>\n",
       "      <td>[Wikidata; Q54422071]</td>\n",
       "      <td>[PubMed=28159921;]</td>\n",
       "      <td>NCBI_TaxID=10090; ! Mus musculus (Mouse)</td>\n",
       "      <td>CVCL_IW90 ! 40</td>\n",
       "      <td>Cancer cell line</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</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>153637</th>\n",
       "      <td>ZZUSAHi001-A</td>\n",
       "      <td>CVCL_ZB29</td>\n",
       "      <td>[hPSCreg; ZZUSAHi001-A, SKIP; SKIP005861, Wiki...</td>\n",
       "      <td>[PubMed=32721895;]</td>\n",
       "      <td>NCBI_TaxID=9606; ! Homo sapiens (Human)</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Induced pluripotent stem cell</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>153638</th>\n",
       "      <td>ZZUSAHi002-A</td>\n",
       "      <td>CVCL_ZB30</td>\n",
       "      <td>[hPSCreg; ZZUSAHi002-A, Wikidata; Q98136743]</td>\n",
       "      <td>[PubMed=32911326;]</td>\n",
       "      <td>NCBI_TaxID=9606; ! Homo sapiens (Human)</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Induced pluripotent stem cell</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>153639</th>\n",
       "      <td>ZZUSAHi003-A</td>\n",
       "      <td>CVCL_A3ZF</td>\n",
       "      <td>[hPSCreg; ZZUSAHi003-A, Wikidata; Q105511894]</td>\n",
       "      <td>[PubMed=33450697;]</td>\n",
       "      <td>NCBI_TaxID=9606; ! Homo sapiens (Human)</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Induced pluripotent stem cell</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>153640</th>\n",
       "      <td>ZZUSAHi004-A</td>\n",
       "      <td>CVCL_C6U7</td>\n",
       "      <td>[BioSamples; SAMEA111442306, hPSCreg; ZZUSAHi0...</td>\n",
       "      <td>[PubMed=36395689;]</td>\n",
       "      <td>NCBI_TaxID=9606; ! Homo sapiens (Human)</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Induced pluripotent stem cell</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>153641</th>\n",
       "      <td>__Parent_cell_line_of_DLD-1/HCT 8/HCT 15/HRT-18</td>\n",
       "      <td>CVCL_3449</td>\n",
       "      <td>[Wikidata; Q54996174]</td>\n",
       "      <td>[PubMed=9809040;]</td>\n",
       "      <td>NCBI_TaxID=9606; ! Homo sapiens (Human)</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Cancer cell line</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>153642 rows × 32 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                                                     ID         AC  \\\n",
       "0                                      #132 PC3-1-SC-E8  CVCL_B0T9   \n",
       "1                                       #132 PL12 SC-D1  CVCL_B0T8   \n",
       "2                                             #15310-LN  CVCL_E548   \n",
       "3                                                #16-15  CVCL_KA96   \n",
       "4                                                  #40a  CVCL_IW91   \n",
       "...                                                 ...        ...   \n",
       "153637                                     ZZUSAHi001-A  CVCL_ZB29   \n",
       "153638                                     ZZUSAHi002-A  CVCL_ZB30   \n",
       "153639                                     ZZUSAHi003-A  CVCL_A3ZF   \n",
       "153640                                     ZZUSAHi004-A  CVCL_C6U7   \n",
       "153641  __Parent_cell_line_of_DLD-1/HCT 8/HCT 15/HRT-18  CVCL_3449   \n",
       "\n",
       "                                                       DR  \\\n",
       "0                                  [Wikidata; Q108819335]   \n",
       "1                                  [Wikidata; Q108819336]   \n",
       "2       [dbMHC; 48439, ECACC; 94050311, IHW; IHW09326,...   \n",
       "3                     [RCB; RCB4635, Wikidata; Q54422067]   \n",
       "4                                   [Wikidata; Q54422071]   \n",
       "...                                                   ...   \n",
       "153637  [hPSCreg; ZZUSAHi001-A, SKIP; SKIP005861, Wiki...   \n",
       "153638       [hPSCreg; ZZUSAHi002-A, Wikidata; Q98136743]   \n",
       "153639      [hPSCreg; ZZUSAHi003-A, Wikidata; Q105511894]   \n",
       "153640  [BioSamples; SAMEA111442306, hPSCreg; ZZUSAHi0...   \n",
       "153641                              [Wikidata; Q54996174]   \n",
       "\n",
       "                           RX                                           OX  \\\n",
       "0       [Patent=EP0501779A1;]     NCBI_TaxID=10090; ! Mus musculus (Mouse)   \n",
       "1       [Patent=EP0501779A1;]     NCBI_TaxID=10090; ! Mus musculus (Mouse)   \n",
       "2                         NaN      NCBI_TaxID=9606; ! Homo sapiens (Human)   \n",
       "3          [PubMed=25400923;]  NCBI_TaxID=10116; ! Rattus norvegicus (Rat)   \n",
       "4          [PubMed=28159921;]     NCBI_TaxID=10090; ! Mus musculus (Mouse)   \n",
       "...                       ...                                          ...   \n",
       "153637     [PubMed=32721895;]      NCBI_TaxID=9606; ! Homo sapiens (Human)   \n",
       "153638     [PubMed=32911326;]      NCBI_TaxID=9606; ! Homo sapiens (Human)   \n",
       "153639     [PubMed=33450697;]      NCBI_TaxID=9606; ! Homo sapiens (Human)   \n",
       "153640     [PubMed=36395689;]      NCBI_TaxID=9606; ! Homo sapiens (Human)   \n",
       "153641      [PubMed=9809040;]      NCBI_TaxID=9606; ! Homo sapiens (Human)   \n",
       "\n",
       "                                           HI                             CA  \\\n",
       "0       CVCL_D145 ! HL-1 Friendly Myeloma-653                      Hybridoma   \n",
       "1       CVCL_D145 ! HL-1 Friendly Myeloma-653                      Hybridoma   \n",
       "2                                         NaN          Transformed cell line   \n",
       "3                    CVCL_4032 ! P3X63Ag8.653                      Hybridoma   \n",
       "4                              CVCL_IW90 ! 40               Cancer cell line   \n",
       "...                                       ...                            ...   \n",
       "153637                                    NaN  Induced pluripotent stem cell   \n",
       "153638                                    NaN  Induced pluripotent stem cell   \n",
       "153639                                    NaN  Induced pluripotent stem cell   \n",
       "153640                                    NaN  Induced pluripotent stem cell   \n",
       "153641                                    NaN               Cancer cell line   \n",
       "\n",
       "                       Group Monoclonal antibody isotype  \\\n",
       "0       Patented cell line.                      IgG2a.    \n",
       "1       Patented cell line.                       IgG1.    \n",
       "2                        NaN                         NaN   \n",
       "3                        NaN                       IgM.    \n",
       "4                        NaN                         NaN   \n",
       "...                      ...                         ...   \n",
       "153637                   NaN                         NaN   \n",
       "153638                   NaN                         NaN   \n",
       "153639                   NaN                         NaN   \n",
       "153640                   NaN                         NaN   \n",
       "153641                   NaN                         NaN   \n",
       "\n",
       "                Monoclonal antibody target  ... Problematic cell line  \\\n",
       "0       UniProtKB; P47712; Human PLA2G4A.   ...                   NaN   \n",
       "1       UniProtKB; P47712; Human PLA2G4A.   ...                   NaN   \n",
       "2                                      NaN  ...                   NaN   \n",
       "3         UniProtKB; Q5T5X7; Human BEND3.   ...                   NaN   \n",
       "4                                      NaN  ...                   NaN   \n",
       "...                                    ...  ...                   ...   \n",
       "153637                                 NaN  ...                   NaN   \n",
       "153638                                 NaN  ...                   NaN   \n",
       "153639                                 NaN  ...                   NaN   \n",
       "153640                                 NaN  ...                   NaN   \n",
       "153641                                 NaN  ...                   NaN   \n",
       "\n",
       "       Knockout cell Karyotypic information Virology Senescence Biotechnology  \\\n",
       "0                NaN                    NaN      NaN        NaN           NaN   \n",
       "1                NaN                    NaN      NaN        NaN           NaN   \n",
       "2                NaN                    NaN      NaN        NaN           NaN   \n",
       "3                NaN                    NaN      NaN        NaN           NaN   \n",
       "4                NaN                    NaN      NaN        NaN           NaN   \n",
       "...              ...                    ...      ...        ...           ...   \n",
       "153637           NaN                    NaN      NaN        NaN           NaN   \n",
       "153638           NaN                    NaN      NaN        NaN           NaN   \n",
       "153639           NaN                    NaN      NaN        NaN           NaN   \n",
       "153640           NaN                    NaN      NaN        NaN           NaN   \n",
       "153641           NaN                    NaN      NaN        NaN           NaN   \n",
       "\n",
       "       Donor information Caution Genome ancestry Microsatellite instability  \n",
       "0                    NaN     NaN             NaN                        NaN  \n",
       "1                    NaN     NaN             NaN                        NaN  \n",
       "2                    NaN     NaN             NaN                        NaN  \n",
       "3                    NaN     NaN             NaN                        NaN  \n",
       "4                    NaN     NaN             NaN                        NaN  \n",
       "...                  ...     ...             ...                        ...  \n",
       "153637               NaN     NaN             NaN                        NaN  \n",
       "153638               NaN     NaN             NaN                        NaN  \n",
       "153639               NaN     NaN             NaN                        NaN  \n",
       "153640               NaN     NaN             NaN                        NaN  \n",
       "153641               NaN     NaN             NaN                        NaN  \n",
       "\n",
       "[153642 rows x 32 columns]"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "protac_cells_df = []\n",
    "for cell in cell2data.keys():\n",
    "    cell_data = cell2data[cell].copy()\n",
    "    for comment in cell_data.get('CC', []):\n",
    "        cc_header = comment.split(':')[0].strip()\n",
    "        if cc_header not in cc_headers_to_ignore:\n",
    "            cc_text = comment.split(':')[1].strip()\n",
    "            cell_data[cc_header] = cell_data.get(cc_header, '') + cc_text + ' '\n",
    "    cell_data.pop('CC', None)\n",
    "    cell_data.pop('DT', None)\n",
    "    cell_data.pop('SY', None)\n",
    "    protac_cells_df.append(cell_data)\n",
    "\n",
    "protac_cells_df = pd.DataFrame(protac_cells_df)\n",
    "# Drop all-Nan columns\n",
    "protac_cells_df = protac_cells_df.dropna(axis=1, how='all')\n",
    "protac_cells_df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index(['ID', 'AC', 'DR', 'RX', 'OX', 'HI', 'CA', 'Group',\n",
       "       'Monoclonal antibody isotype', 'Monoclonal antibody target',\n",
       "       'Population', 'HLA typing', 'Transformant', 'Derived from site',\n",
       "       'Cell type', 'Characteristics', 'Breed/subspecies',\n",
       "       'Sequence variation', 'Transfected with', 'Doubling time', 'Omics',\n",
       "       'Selected for resistance to', 'Problematic cell line', 'Knockout cell',\n",
       "       'Karyotypic information', 'Virology', 'Senescence', 'Biotechnology',\n",
       "       'Donor information', 'Caution', 'Genome ancestry',\n",
       "       'Microsatellite instability'],\n",
       "      dtype='object')"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "protac_cells_df.columns"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Get Cell Text \"Descriptions\""
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We shall rank the descriptions of the cell lines in the Cellosaurus by their uniqueness."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [],
   "source": [
    "palette = {\n",
    "    'blue': '#83B8FE',\n",
    "    'orange': '#FFA54C',\n",
    "    'violet': '#94ED67',\n",
    "    'green': '#FF7FFF',\n",
    "}"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "['Genome ancestry', 'AC', 'Doubling time', 'Karyotypic information', 'Senescence', 'Biotechnology', 'Virology', 'Problematic cell line', 'Caution', 'Donor information', 'Sequence variation', 'Characteristics', 'Transfected with', 'Monoclonal antibody target', 'HLA typing', 'Knockout cell', 'Microsatellite instability', 'HI', 'Omics', 'Breed/subspecies', 'Derived from site', 'Population', 'Group', 'Monoclonal antibody isotype', 'Cell type', 'OX', 'Transformant', 'Selected for resistance to', 'CA']\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns\n",
    "\n",
    "# For each column, plot the number of unique values\n",
    "percentages = []\n",
    "for column in protac_cells_df.columns:\n",
    "    if column in ['ID', 'DR', 'RX']:\n",
    "        continue\n",
    "    num_unique = len(protac_cells_df[column].unique())\n",
    "    num_notna = len(protac_cells_df[column].dropna())\n",
    "    # print(f'{column}: {num_unique} ({num_unique / len(protac_cells_df):.1%})')\n",
    "    percentages.append({\n",
    "        'Column': column,\n",
    "        # len(protac_cells_df),\n",
    "        'Perc (%)': num_unique / len(protac_cells_df[column].dropna()),\n",
    "        'Unique/Not-NaN': 'Unique',\n",
    "    })\n",
    "    # percentages.append({\n",
    "    #     'Column': column,\n",
    "    #     'Perc (%)': num_notna / len(protac_cells_df),\n",
    "    #     'Unique/Not-NaN': 'Not-NaN',\n",
    "    # })\n",
    "percentages = pd.DataFrame(percentages)\n",
    "\n",
    "# Sort by non-NaN percentage\n",
    "percentages = percentages.sort_values(by='Perc (%)', ascending=False)\n",
    "# Get column order\n",
    "unique_columns_ranking = percentages['Column'].unique().tolist()\n",
    "print(unique_columns_ranking)\n",
    "\n",
    "# Bar plot of the percentages, horizontal\n",
    "ax = sns.barplot(x='Perc (%)', y='Column',\n",
    "                 hue='Unique/Not-NaN', data=percentages, palette={'Unique': palette['blue']})\n",
    "plt.xlabel('Percentage of unique, i.e., non-NaN, values')\n",
    "plt.ylabel('Database columns in Cellosaurus')\n",
    "# plt.title('Percentage of unique values per column')\n",
    "# Set x-axis to percentage\n",
    "plt.xticks(ticks=[0, 0.25, 0.5, 0.75, 1],\n",
    "           labels=['0%', '25%', '50%', '75%', '100%'])\n",
    "# Disable legend\n",
    "plt.legend([], [], frameon=False)\n",
    "plt.grid(axis='x', alpha=0.5)\n",
    "plt.savefig('plots/cell_line_unique_values.pdf', bbox_inches='tight')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['Genome ancestry',\n",
       " 'Karyotypic information',\n",
       " 'Senescence',\n",
       " 'Biotechnology',\n",
       " 'Virology',\n",
       " 'Caution',\n",
       " 'Donor information',\n",
       " 'Sequence variation',\n",
       " 'Characteristics',\n",
       " 'Transfected with',\n",
       " 'Monoclonal antibody target',\n",
       " 'HLA typing',\n",
       " 'Knockout cell',\n",
       " 'Microsatellite instability',\n",
       " 'HI',\n",
       " 'Breed/subspecies',\n",
       " 'Derived from site',\n",
       " 'Population',\n",
       " 'Group',\n",
       " 'Monoclonal antibody isotype',\n",
       " 'Cell type',\n",
       " 'Transformant',\n",
       " 'Selected for resistance to',\n",
       " 'CA']"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "features_to_ignore = [\n",
    "    'Problematic cell line',\n",
    "    'Omics',\n",
    "    'AC',\n",
    "    'OX',\n",
    "    'Doubling time',\n",
    "]\n",
    "unique_columns_ranking = [\n",
    "    c for c in unique_columns_ranking if c not in features_to_ignore]\n",
    "unique_columns_ranking"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "genome ancestry, karyotypic information, senescence, biotechnology, virology, caution, donor information, sequence variation, characteristics, transfected with, monoclonal antibody target, HLA typing, knockout cell, microsatellite instability, hierarchy (HI), breed/subspecies, derived from site, population, group, monoclonal antibody isotype, cell type, transformant, selected for resistance to, category (CA)."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "108 UniProtKB; P47712; Human PLA2G4A.\n",
      "CVCL_D145 ! HL-1 Friendly Myeloma-653\n",
      "Patented cell line.\n",
      "IgG2a.\n",
      "Hybridoma...\n",
      "--------------------------------------------------------------------------------\n",
      "107 UniProtKB; P47712; Human PLA2G4A.\n",
      "CVCL_D145 ! HL-1 Friendly Myeloma-653\n",
      "Patented cell line.\n",
      "IgG1.\n",
      "Hybridoma...\n",
      "--------------------------------------------------------------------------------\n",
      "159 A*03,25; B*37\n",
      "In situ; Peripheral blood; \n",
      "Caucasian; French Canadian.\n",
      "B-cell; CL=CL_0000236.\n",
      "NCBI_TaxID; 10376; Epstein-Barr virus (EBV).\n",
      "Transformed cell line...\n",
      "--------------------------------------------------------------------------------\n",
      "71 UniProtKB; Q5T5X7; Human BEND3.\n",
      "CVCL_4032 ! P3X63Ag8.653\n",
      "IgM.\n",
      "Hybridoma...\n",
      "--------------------------------------------------------------------------------\n",
      "173 Established from parent cell line after two passages in the peritoneal cavity of C57BL/6 mice.\n",
      "CVCL_IW90 ! 40\n",
      "C57BL/6.\n",
      "Metastatic; Peritoneum; \n",
      "ChEBI; CHEBI\n",
      "Cancer cell line...\n",
      "--------------------------------------------------------------------------------\n",
      "96 Cronartium ribicola antigens.\n",
      "CVCL_4032 ! P3X63Ag8.653\n",
      "Patented cell line.\n",
      "IgM, kappa.\n",
      "Hybridoma...\n",
      "--------------------------------------------------------------------------------\n",
      "96 Cronartium ribicola antigens.\n",
      "CVCL_4032 ! P3X63Ag8.653\n",
      "Patented cell line.\n",
      "IgM, kappa.\n",
      "Hybridoma...\n",
      "--------------------------------------------------------------------------------\n",
      "63 UniProtKB; P10683; Rat Gal.\n",
      "CVCL_6971 ! FOX-NY\n",
      "IgG2a.\n",
      "Hybridoma...\n",
      "--------------------------------------------------------------------------------\n",
      "133 A*02\n",
      "In situ; Peripheral blood; \n",
      "Caucasian.\n",
      "B-cell; CL=CL_0000236.\n",
      "NCBI_TaxID; 10376; Epstein-Barr virus (EBV).\n",
      "Transformed cell line...\n",
      "--------------------------------------------------------------------------------\n",
      "87 UniProtKB; P05067; Human APP (Note=Binds to APP42).\n",
      "Patented cell line.\n",
      "IgG1.\n",
      "Hybridoma...\n",
      "--------------------------------------------------------------------------------\n",
      "Average length of cell line description: 181.1\n"
     ]
    }
   ],
   "source": [
    "import re\n",
    "\n",
    "cell2description = {}\n",
    "for i, row in protac_cells_df.iterrows():\n",
    "    cell_description = \"\"\n",
    "    for col in unique_columns_ranking:\n",
    "        if pd.notnull(row[col]):\n",
    "            # if len(col) > 2:\n",
    "            #     cell_description += f\"{col}: {row[col].strip()}\"\n",
    "            # else:\n",
    "            #     cell_description += f\"{row[col].strip()}\"\n",
    "            cell_description += f\"{row[col].strip()}\"\n",
    "            cell_description += '\\n'\n",
    "    # Remove via regex all strings of the form \"(PubMed=12345678)\"\n",
    "    cell_description = re.sub(r'\\(PubMed=.*\\)', '', cell_description)\n",
    "    # Remove via regex all strings of the form \"UBERON=UBERON_0002048.\"\n",
    "    cell_description = re.sub(r'UBERON=.*\\.', '', cell_description)\n",
    "    cell_description = cell_description.strip()\n",
    "    cell_description = cell_description.replace(' .', '.')\n",
    "    cell_description = cell_description.replace('  ', ' ')\n",
    "\n",
    "    cell2description[row['ID']] = cell_description\n",
    "\n",
    "    if i < 10:\n",
    "        print(len(cell_description), f'{cell_description[:500]}...')\n",
    "        print('-' * 80)\n",
    "\n",
    "print(\n",
    "    f'Average length of cell line description: {sum([len(v) for v in cell2description.values()]) / len(cell2description):.1f}')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 165,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pickle\n",
    "\n",
    "cell2description_filepath = os.path.join(\n",
    "    data_dir, 'processed', 'cell2description.pkl'\n",
    ")\n",
    "with open(cell2description_filepath, 'wb') as f:\n",
    "    pickle.dump(cell2description, f)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "\\begin{figure*}[t!]\n",
    "    \\centering\n",
    "    \\begin{subfigure}{0.5\\textwidth}\n",
    "        \\centering\n",
    "        \\includegraphics[width=0.99\\columnwidth]{plots/pytorch_performance_Accuracy.pdf}\n",
    "        \\caption{}\n",
    "        \\label{fig:pytorch_accuracy}\n",
    "    \\end{subfigure}%\n",
    "    \\begin{subfigure}{0.5\\textwidth}\n",
    "        \\centering\n",
    "        \\includegraphics[width=0.99\\columnwidth]{plots/pytorch_performance_ROC AUC.pdf}\n",
    "        \\caption{}\n",
    "        \\label{fig:pytorch_roc_auc}\n",
    "    \\end{subfigure}\\\\%\n",
    "    \\begin{subfigure}{0.5\\textwidth}\n",
    "        \\centering\n",
    "        \\includegraphics[width=0.99\\columnwidth]{plots/pytorch_performance_F1 Score.pdf}\n",
    "        \\caption{}\n",
    "        \\label{fig:pytorch_f1_score}\n",
    "    \\end{subfigure}%\n",
    "    \\begin{subfigure}{0.5\\textwidth}\n",
    "        \\centering\n",
    "        \\includegraphics[width=0.99\\columnwidth]{plots/pytorch_performance_Precision.pdf}\n",
    "        \\caption{}\n",
    "        \\label{fig:pytorch_precision}\n",
    "    \\end{subfigure}\\\\%\n",
    "    \\begin{subfigure}{0.5\\textwidth}\n",
    "        \\centering\n",
    "        \\includegraphics[width=0.99\\columnwidth]{plots/pytorch_performance_Recall.pdf}\n",
    "        \\caption{}\n",
    "        \\label{fig:pytorch_recall}\n",
    "    \\end{subfigure}%\n",
    "    \\caption{Performance metrics of the proposed deep learning models. (a) ROC-AUC. (b) F1 score. (c) Precision. (d) Recall.}\n",
    "    \\label{fig:pytorch_performance}\n",
    "\\end{figure*}"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Embeddings from Cell Descriptions"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Once the descriptions are generated, we will use the `sentence-transformers` package to generate embeddings for the cell descriptions."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import torch"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "SentenceTransformer(\n",
       "  (0): Transformer({'max_seq_length': 384, 'do_lower_case': False}) with Transformer model: MPNetModel \n",
       "  (1): Pooling({'word_embedding_dimension': 768, 'pooling_mode_cls_token': False, 'pooling_mode_mean_tokens': True, 'pooling_mode_max_tokens': False, 'pooling_mode_mean_sqrt_len_tokens': False, 'pooling_mode_weightedmean_tokens': False, 'pooling_mode_lasttoken': False})\n",
       "  (2): Normalize()\n",
       ")"
      ]
     },
     "execution_count": 44,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from sentence_transformers import SentenceTransformer\n",
    "from sentence_transformers.util import cos_sim\n",
    "\n",
    "model = SentenceTransformer(\n",
    "    \"sentence-transformers/all-mpnet-base-v2\"\n",
    ")\n",
    "\n",
    "if torch.cuda.is_available():\n",
    "    device = 0  # GPU\n",
    "else:\n",
    "    device = \"cpu\"\n",
    "\n",
    "model.to(device)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0.8759459257125854\n",
      "0.28315800428390503\n",
      "0.3522447347640991\n"
     ]
    }
   ],
   "source": [
    "embeddings = model.encode([\n",
    "    \"\"\"\n",
    "UKF-NB-2rDACARB4\n",
    "CVCL_RT02\n",
    "Cancer cell line\n",
    "NCBI_TaxID=9606; ! Homo sapiens (Human)\n",
    "Part of: Resistant Cancer Cell Line (RCCL) collection.\n",
    "Selected for resistance to: ChEBI; CHEBI:4305; Dacarbazine (DTIC; (5-(3,3-dimethyl-1-triazeno)imidazole-4-carboxamide)).\n",
    "Derived from site: Metastatic; Bone marrow; UBERON=UBERON_0002371.\n",
    "NCIt; C3270; Neuroblastoma\n",
    "ORDO; Orphanet_635; Neuroblastoma\n",
    "CVCL_9902 ! UKF-NB-2\n",
    "\"\"\",\n",
    "    \"\"\"\n",
    "UKF-NB-2rDOCE10\n",
    "CVCL_RR83\n",
    "NCBI_TaxID=9606; ! Homo sapiens (Human)\n",
    "Part of: Resistant Cancer Cell Line (RCCL) collection.\n",
    "Selected for resistance to: ChEBI; CHEBI:4672; Docetaxel anhydrous (Taxotere).\n",
    "Derived from site: Metastatic; Bone marrow; UBERON=UBERON_0002371.\n",
    "NCIt; C3270; Neuroblastoma\n",
    "ORDO; Orphanet_635; Neuroblastoma\n",
    "CVCL_9902 ! UKF-NB-2\n",
    "Cancer cell line\n",
    "\"\"\",\n",
    "    \"\"\"\n",
    "FHS036i-sh18961C\n",
    "CVCL_YY67\n",
    "Induced pluripotent stem cell\n",
    "NCBI_TaxID=9606; ! Homo sapiens (Human)\n",
    "Part of: Framingham Heart Study (FHS) collection.\n",
    "Part of: Next Generation Genetic Association studies (Next Gen) program cell lines.\n",
    "Population: Caucasian.\n",
    "Sequence variation: Mutation; HGNC; 3231; CELSR2; Simple; c.*919G; dbSNP=rs12740374; Zygosity=Homozygous; Note=Major haplotype (PubMed=28388431).\n",
    "Omics: Transcriptome analysis by RNAseq.\n",
    "Derived from site: In situ; Peripheral blood; UBERON=UBERON_0000178.\n",
    "CVCL_YY66 ! FHS035i-sh18961A\n",
    "\"\"\",\n",
    "])\n",
    "print(cos_sim(embeddings[0], embeddings[1]).item())\n",
    "print(cos_sim(embeddings[0], embeddings[2]).item())\n",
    "print(cos_sim(embeddings[1], embeddings[2]).item())"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "By default, input text longer than 384 word pieces is truncated ([source](https://huggingface.co/sentence-transformers/all-mpnet-base-v2))."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 161,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "ce0fcca219c1485b83909a355f86e742",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "Get sentence embeddings:   0%|          | 0/1138 [00:00<?, ?it/s]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import numpy as np\n",
    "from tqdm.auto import tqdm\n",
    "import random\n",
    "\n",
    "tmp = {k: cell2description[k] for k in random.sample(\n",
    "    list(cell2description.keys()), 1000) + protac_cells}\n",
    "\n",
    "cell2embedding = {}\n",
    "for cell, description in tqdm(tmp.items(), desc='Get sentence embeddings'):\n",
    "    # Chunk the description in chunks of maximum 384 length\n",
    "    chunk_len = 384\n",
    "    chunks = [description[i:i+chunk_len]\n",
    "              for i in range(0, len(description), chunk_len)]\n",
    "    embeddings = np.mean(model.encode(chunks), axis=0)\n",
    "    # embeddings = model.encode(chunks[0])\n",
    "    cell2embedding[cell] = embeddings"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 170,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Embedding type: <class 'numpy.ndarray'>\n",
      "Embedding size: (768,)\n"
     ]
    }
   ],
   "source": [
    "emb = cell2embedding[list(cell2embedding.keys())[0]]\n",
    "print(f'Embedding type: {type(emb)}')\n",
    "print(f'Embedding size: {emb.shape}')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pickle\n",
    "\n",
    "cell2embedding_filepath = os.path.join(\n",
    "    data_dir, 'cell2embedding.pkl'\n",
    ")\n",
    "with open(cell2embedding_filepath, 'wb') as f:\n",
    "    pickle.dump(cell2embedding, f)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Number of cell lines: 1138\n"
     ]
    }
   ],
   "source": [
    "import pickle\n",
    "\n",
    "cell2embedding_filepath = os.path.join(\n",
    "    data_dir, 'cell2embedding.pkl'\n",
    ")\n",
    "with open(cell2embedding_filepath, 'rb') as f:\n",
    "    cell2embedding = pickle.load(f)\n",
    "print(f'Number of cell lines: {len(cell2embedding)}')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['GM15119', 'GM17453', '84 BLCL']"
      ]
     },
     "execution_count": 30,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "list(cell2embedding.keys())[:3]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "HT-29/cDDP\n",
      "HT-29\n"
     ]
    }
   ],
   "source": [
    "for k in cell2embedding.keys():\n",
    "    if 'HT-29' in k:\n",
    "        print(k)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Save to H5 File"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "metadata": {},
   "outputs": [],
   "source": [
    "import h5py\n",
    "import numpy as np\n",
    "import pandas as pd\n",
    "\n",
    "\n",
    "def save_embeddings_to_hdf5(embeddings, file_path):\n",
    "    \"\"\"\n",
    "    Save the embeddings dictionary to an HDF5 file, skipping datasets that already exist.\n",
    "\n",
    "    Parameters:\n",
    "    - embeddings: dict, where the key is the name identifier and the value is the numpy array of embeddings.\n",
    "    - file_path: str, the path to the output HDF5 file.\n",
    "    \"\"\"\n",
    "    with h5py.File(file_path, 'w') as h5f:\n",
    "        for name_id, embedding in embeddings.items():\n",
    "            if pd.isnull(embedding).any():\n",
    "                print(f\"NaN value found in embedding for '{name_id}'. Skipping...\")\n",
    "                continue\n",
    "            if pd.isnull(name_id):\n",
    "                print(f\"NaN value found in name_id. Skipping...\")\n",
    "                continue\n",
    "            if name_id in h5f:\n",
    "                print(f\"Dataset '{name_id}' already exists. Skipping...\")\n",
    "                continue  # Skip this name_id if it already exists\n",
    "            # Create dataset with compression\n",
    "            h5f.create_dataset(name_id.replace('/', '##'), data=embedding) #, compression=\"gzip\", compression_opts=9)\n",
    "\n",
    "\n",
    "def verify_embeddings(file_path, original_embeddings):\n",
    "    \"\"\"\n",
    "    Verify that embeddings stored in an HDF5 file match the original embeddings.\n",
    "\n",
    "    Parameters:\n",
    "    - file_path: str, the path to the HDF5 file.\n",
    "    - original_embeddings: dict, the original embeddings dictionary.\n",
    "    \"\"\"\n",
    "    with h5py.File(file_path, 'r') as h5f:\n",
    "        for name_id, original_embedding in original_embeddings.items():\n",
    "            name_id = name_id.replace('/', '##')\n",
    "            if name_id not in h5f:\n",
    "                print(f\"Dataset '{name_id}' not found in the HDF5 file.\")\n",
    "                continue\n",
    "            \n",
    "            # Retrieve the dataset from the file\n",
    "            stored_embedding = h5f[name_id]\n",
    "            \n",
    "            # Compare the stored embedding with the original one\n",
    "            if np.array_equal(stored_embedding, original_embedding):\n",
    "                # print(f\"Dataset '{name_id}' matches the original embedding.\")\n",
    "                pass\n",
    "            else:\n",
    "                print(f\"Dataset '{name_id}' does not match the original embedding.\")\n",
    "\n",
    "\n",
    "cell2embedding_h5_filepath = os.path.join(\n",
    "    data_dir, 'cell2embedding.h5'\n",
    "    # '..', 'cellovec', 'data', 'cell2embedding.h5'\n",
    ")\n",
    "save_embeddings_to_hdf5(cell2embedding, cell2embedding_h5_filepath)\n",
    "verify_embeddings(cell2embedding_h5_filepath, cell2embedding)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['GM15119', 'GM17453', '84 BLCL']"
      ]
     },
     "execution_count": 47,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "list(cell2embedding.keys())[:3]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Number of cell lines: 1138\n"
     ]
    }
   ],
   "source": [
    "# Save list of cell lines to a text file under ../data\n",
    "cell_lines_filepath = os.path.join(data_dir, 'current_cell_lines.txt')\n",
    "with open(cell_lines_filepath, 'w') as f:\n",
    "    for cell in cell2embedding.keys():\n",
    "        f.write(f'{cell}\\n')\n",
    "print(f'Number of cell lines: {len(cell2embedding)}')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## UMAP Cell Embeddings"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Number of cell lines: 1138\n"
     ]
    }
   ],
   "source": [
    "import pickle\n",
    "\n",
    "embeddings_path = os.path.join(data_dir, 'cell2embedding.pkl')\n",
    "with open(embeddings_path, 'rb') as f:\n",
    "    cell2embedding = pickle.load(f)\n",
    "print(f'Number of cell lines: {len(cell2embedding)}')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [],
   "source": [
    "import umap\n",
    "from sklearn.preprocessing import StandardScaler"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/opt/conda/lib/python3.10/site-packages/umap/umap_.py:1945: UserWarning: n_jobs value 1 overridden to 1 by setting random_state. Use no seed for parallelism.\n",
      "  warn(f\"n_jobs value {self.n_jobs} overridden to 1 by setting random_state. Use no seed for parallelism.\")\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>UMAP 1</th>\n",
       "      <th>UMAP 2</th>\n",
       "      <th>Cell ID</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>-0.368673</td>\n",
       "      <td>9.499292</td>\n",
       "      <td>Transformed cell line</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>-0.668173</td>\n",
       "      <td>8.160005</td>\n",
       "      <td>Transformed cell line</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>-0.164419</td>\n",
       "      <td>9.115404</td>\n",
       "      <td>Transformed cell line</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>6.897954</td>\n",
       "      <td>12.088582</td>\n",
       "      <td>Finite cell line</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>1.488047</td>\n",
       "      <td>14.562062</td>\n",
       "      <td>Cancer cell line</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "     UMAP 1     UMAP 2                Cell ID\n",
       "0 -0.368673   9.499292  Transformed cell line\n",
       "1 -0.668173   8.160005  Transformed cell line\n",
       "2 -0.164419   9.115404  Transformed cell line\n",
       "4  6.897954  12.088582       Finite cell line\n",
       "5  1.488047  14.562062       Cancer cell line"
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Define UMAP and Scaler\n",
    "umap_reducer = umap.UMAP(\n",
    "    n_neighbors=30,  # Good value: 50\n",
    "    min_dist=0.8,  # Good value: 0.5\n",
    "    # spread=1.0, # Good value: (not set, default)\n",
    "    metric='euclidean',\n",
    "    random_state=42,\n",
    "    unique=True,\n",
    "    # n_epochs=100,\n",
    "    init='spectral',  # Default: 'spectral'\n",
    "    verbose=False,\n",
    ")\n",
    "scaler = StandardScaler()\n",
    "\n",
    "# Get the embeddings as a numpy array\n",
    "data = scaler.fit_transform(list(cell2embedding.values()))\n",
    "data = umap_reducer.fit_transform(data)\n",
    "\n",
    "# Get the UMAP embedding coordinates\n",
    "umap_embeddings = {\n",
    "    'UMAP 1': [],\n",
    "    'UMAP 2': [],\n",
    "    'Cell ID': [],\n",
    "}\n",
    "umap_embeddings['UMAP 1'] = data[:, 0].tolist()\n",
    "umap_embeddings['UMAP 2'] = data[:, 1].tolist()\n",
    "# umap_embeddings['Cell ID'] = list(cell2embedding.keys())\n",
    "umap_embeddings['Cell ID'] = [cell2data[c]['CA']\n",
    "                              for c in cell2embedding.keys()]\n",
    "\n",
    "# Transform to dataframe and drop duplicates\n",
    "umap_embeddings = pd.DataFrame(umap_embeddings).drop_duplicates()\n",
    "umap_embeddings.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['Transformed cell line',\n",
       " 'Finite cell line',\n",
       " 'Cancer cell line',\n",
       " 'Embryonic stem cell',\n",
       " 'Hybridoma',\n",
       " 'Induced pluripotent stem cell',\n",
       " 'Spontaneously immortalized cell line',\n",
       " 'Somatic stem cell',\n",
       " 'Hybrid cell line',\n",
       " 'Conditionally immortalized cell line',\n",
       " 'Telomerase immortalized cell line',\n",
       " 'Factor-dependent cell line',\n",
       " 'Undefined cell line type']"
      ]
     },
     "execution_count": 29,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "umap_embeddings['Cell ID'].unique().tolist()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Define a rainbow color pattern for the Cell IDs (as a dictionary)\n",
    "# Thanks Chat-GPT4o for the color suggestions! (it started from a rainbow pattern and \"merged it with my palette above\")\n",
    "colors = {\n",
    "    'Transformed cell line': '#FFA54C',  # Orange\n",
    "    'Finite cell line': '#FF7F50',       # Coral (closer to orange)\n",
    "    'Cancer cell line': '#FFD700',       # Gold (yellowish)\n",
    "    'Embryonic stem cell': '#94ED67',    # Green (from palette)\n",
    "    'Hybridoma': '#83B8FE',              # Blue (from palette)\n",
    "    'Induced pluripotent stem cell': '#7B68EE',  # Medium Slate Blue (indigo)\n",
    "    'Spontaneously immortalized cell line': '#8A2BE2', # Blue Violet\n",
    "    'Somatic stem cell': '#EE82EE',      # Violet\n",
    "    'Hybrid cell line': '#DA70D6',       # Orchid (violet)\n",
    "    'Conditionally immortalized cell line': '#00CED1', # Dark Turquoise (blueish)\n",
    "    'Telomerase immortalized cell line': '#98FB98', # Pale Green\n",
    "    'Factor-dependent cell line': '#FF69B4', # Hot Pink (closer to violet)\n",
    "    'Undefined cell line type': '#D3D3D3',  # Gray\n",
    "}\n",
    "sns.scatterplot(data=umap_embeddings, x='UMAP 1', y='UMAP 2',\n",
    "                hue='Cell ID', palette=colors) #sns.color_palette('hls', 13))\n",
    "# Make the legend external\n",
    "plt.legend(bbox_to_anchor=(1.01, 0.87), borderaxespad=0)\n",
    "# plt.title('UMAP embedding of cell lines')\n",
    "plt.grid(axis='both', alpha=0.5)\n",
    "plt.savefig('plots/umap_cell_lines.pdf', bbox_inches='tight')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Embeddings from Abstracts (NOT IMPLEMENTED)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# !pip install biopython beautifulsoup4"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "from bs4 import BeautifulSoup\n",
    "\n",
    "\n",
    "def extract_abstract_from_html(html_content):\n",
    "    \"\"\"\n",
    "    Extract the abstract text from a PubMed article's HTML content.\n",
    "\n",
    "    :param html_content: The HTML content as a byte string.\n",
    "    :return: The abstract text if available, otherwise an error message.\n",
    "    \"\"\"\n",
    "    try:\n",
    "        # Parse the HTML content\n",
    "        soup = BeautifulSoup(html_content, \"html.parser\")\n",
    "\n",
    "        # Find the abstract text\n",
    "        abstract_text = soup.find(\"abstracttext\")\n",
    "        if abstract_text:\n",
    "            return abstract_text.get_text()\n",
    "        else:\n",
    "            return \"Abstract not found.\"\n",
    "    except Exception as e:\n",
    "        return f\"An error occurred: {str(e)}\"\n",
    "\n",
    "# Example usage\n",
    "# html_content = b'...'  # Replace with the actual HTML content\n",
    "# abstract = extract_abstract_from_html(html_content)\n",
    "# print(abstract)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "c:\\Users\\ste\\Anaconda2\\envs\\env-thesis\\Lib\\site-packages\\bs4\\builder\\__init__.py:545: XMLParsedAsHTMLWarning: It looks like you're parsing an XML document using an HTML parser. If this really is an HTML document (maybe it's XHTML?), you can ignore or filter this warning. If it's XML, you should know that using an XML parser will be more reliable. To parse this document as XML, make sure you have the lxml package installed, and pass the keyword argument `features=\"xml\"` into the BeautifulSoup constructor.\n",
      "  warnings.warn(\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Retrieved abstract for PubMed ID: A novel lymphoma cell line, designated TMD8 was established from cells of a patient with diffuse large B-cell lymphoma. TMD8 cells expressed HES1 mRNA, suggesting constitutive activation of Notch signaling. TMD8 cells expressed normal-sized Notch1 protein, and showed no mutations in the NOTCH1 gene. Cell growth was suppressed by gamma-secretase inhibitors (GSI). It was reported that GSI suppressed growth of T-cell acute lymphoblastic leukemia (T-ALL) cell lines, which frequently had NOTCH1 mutations. In addition to T-ALL, TMD8 is another unique cell line sensitive to GSI, and is useful to study effects of GSI in molecular targeting therapy.\n",
      "--------------------------------------------------------------------------------\n",
      "Retrieved abstract for PubMed ID: A role for B-cell-receptor (BCR) signalling in lymphomagenesis has been inferred by studying immunoglobulin genes in human lymphomas and by engineering mouse models, but genetic and functional evidence for its oncogenic role in human lymphomas is needed. Here we describe a form of 'chronic active' BCR signalling that is required for cell survival in the activated B-cell-like (ABC) subtype of diffuse large B-cell lymphoma (DLBCL). The signalling adaptor CARD11 is required for constitutive NF-kappaB pathway activity and survival in ABC DLBCL. Roughly 10% of ABC DLBCLs have mutant CARD11 isoforms that activate NF-kappaB, but the mechanism that engages wild-type CARD11 in other ABC DLBCLs was unknown. An RNA interference genetic screen revealed that a BCR signalling component, Bruton's tyrosine kinase, is essential for the survival of ABC DLBCLs with wild-type CARD11. In addition, knockdown of proximal BCR subunits (IgM, Ig-kappa, CD79A and CD79B) killed ABC DLBCLs with wild-type CARD11 but not other lymphomas. The BCRs in these ABC DLBCLs formed prominent clusters in the plasma membrane with low diffusion, similarly to BCRs in antigen-stimulated normal B cells. Somatic mutations affecting the immunoreceptor tyrosine-based activation motif (ITAM) signalling modules of CD79B and CD79A were detected frequently in ABC DLBCL biopsy samples but rarely in other DLBCLs and never in Burkitt's lymphoma or mucosa-associated lymphoid tissue lymphoma. In 18% of ABC DLBCLs, one functionally critical residue of CD79B, the first ITAM tyrosine, was mutated. These mutations increased surface BCR expression and attenuated Lyn kinase, a feedback inhibitor of BCR signalling. These findings establish chronic active BCR signalling as a new pathogenetic mechanism in ABC DLBCL, suggesting several therapeutic strategies.\n",
      "--------------------------------------------------------------------------------\n",
      "Retrieved abstract for PubMed ID: The activated B-cell-like (ABC) subtype of diffuse large B-cell lymphoma (DLBCL) remains the least curable form of this malignancy despite recent advances in therapy. Constitutive nuclear factor (NF)-κB and JAK kinase signalling promotes malignant cell survival in these lymphomas, but the genetic basis for this signalling is incompletely understood. Here we describe the dependence of ABC DLBCLs on MYD88, an adaptor protein that mediates toll and interleukin (IL)-1 receptor signalling, and the discovery of highly recurrent oncogenic mutations affecting MYD88 in ABC DLBCL tumours. RNA interference screening revealed that MYD88 and the associated kinases IRAK1 and IRAK4 are essential for ABC DLBCL survival. High-throughput RNA resequencing uncovered MYD88 mutations in ABC DLBCL lines. Notably, 29% of ABC DLBCL tumours harboured the same amino acid substitution, L265P, in the MYD88 Toll/IL-1 receptor (TIR) domain at an evolutionarily invariant residue in its hydrophobic core. This mutation was rare or absent in other DLBCL subtypes and Burkitt's lymphoma, but was observed in 9% of mucosa-associated lymphoid tissue lymphomas. At a lower frequency, additional mutations were observed in the MYD88 TIR domain, occurring in both the ABC and germinal centre B-cell-like (GCB) DLBCL subtypes. Survival of ABC DLBCL cells bearing the L265P mutation was sustained by the mutant but not the wild-type MYD88 isoform, demonstrating that L265P is a gain-of-function driver mutation. The L265P mutant promoted cell survival by spontaneously assembling a protein complex containing IRAK1 and IRAK4, leading to IRAK4 kinase activity, IRAK1 phosphorylation, NF-κB signalling, JAK kinase activation of STAT3, and secretion of IL-6, IL-10 and interferon-β. Hence, the MYD88 signalling pathway is integral to the pathogenesis of ABC DLBCL, supporting the development of inhibitors of IRAK4 kinase and other components of this pathway for the treatment of tumours bearing oncogenic MYD88 mutations.\n",
      "--------------------------------------------------------------------------------\n",
      "Retrieved abstract for PubMed ID: Myeloid cell leukemia-1 (MCL1) is an anti-apoptotic member of the BCL2 family that is deregulated in various solid and hematological malignancies. However, its role in the molecular pathogenesis of diffuse large B-cell lymphoma (DLBCL) is unclear. We analyzed gene expression profiling data from 350 DLBCL patient samples and detected that activated B-cell-like (ABC) DLBCLs express MCL1 at significantly higher levels compared with germinal center B-cell-like DLBCL patient samples (P=2.7 × 10(-10)). Immunohistochemistry confirmed high MCL1 protein expression predominantly in ABC DLBCL in an independent patient cohort (n=249; P=0.001). To elucidate molecular mechanisms leading to aberrant MCL1 expression, we analyzed array comparative genomic hybridization data of 203 DLBCL samples and identified recurrent chromosomal gains/amplifications of the MCL1 locus that occurred in 26% of ABC DLBCLs. In addition, aberrant STAT3 signaling contributed to high MCL1 expression in this subtype. Knockdown of MCL1 as well as treatment with the BH3-mimetic obatoclax induced apoptotic cell death in MCL1-positive DLBCL cell lines. In summary, MCL1 is deregulated in a significant fraction of ABC DLBCLs and contributes to therapy resistance. These data suggest that specific inhibition of MCL1 might be utilized therapeutically in a subset of DLBCLs.\n",
      "--------------------------------------------------------------------------------\n",
      "Retrieved abstract for PubMed ID: Diffuse large B-cell lymphoma (DLBCL) is the most common form of lymphoma in adults. The disease exhibits a striking heterogeneity in gene expression profiles and clinical outcomes, but its genetic causes remain to be fully defined. Through whole genome and exome sequencing, we characterized the genetic diversity of DLBCL. In all, we sequenced 73 DLBCL primary tumors (34 with matched normal DNA). Separately, we sequenced the exomes of 21 DLBCL cell lines. We identified 322 DLBCL cancer genes that were recurrently mutated in primary DLBCLs. We identified recurrent mutations implicating a number of known and not previously identified genes and pathways in DLBCL including those related to chromatin modification (ARID1A and MEF2B), NF-κB (CARD11 and TNFAIP3), PI3 kinase (PIK3CD, PIK3R1, and MTOR), B-cell lineage (IRF8, POU2F2, and GNA13), and WNT signaling (WIF1). We also experimentally validated a mutation in PIK3CD, a gene not previously implicated in lymphomas. The patterns of mutation demonstrated a classic long tail distribution with substantial variation of mutated genes from patient to patient and also between published studies. Thus, our study reveals the tremendous genetic heterogeneity that underlies lymphomas and highlights the need for personalized medicine approaches to treating these patients.\n",
      "--------------------------------------------------------------------------------\n",
      "Retrieved abstract for PubMed ID: A monoclonal antibody (mAb), designated 0.5 alpha, derived from a patient with adult T-cell leukemia was found previously to neutralize the human T-cell leukemia/lymphotropic type I (HTLV-I) virus in in vitro assays and bind to the major envelope glycoprotein (gp46) of HTLV-I (Matsushita, S., Guroff, M.R., Trepel, J., Crossman, J., Mitsuya, H., and Broder, S. (1986) Proc. Natl. Acad. Sci. U.S.A. 83, 2671-2676). We have designed experiments to determine the epitope for this mAb. Using simultaneous multiple peptide synthesis, we synthesized 481 overlapping octapeptides which corresponded to the sequence of gp46. We mapped the epitope for mAb 0.5 alpha to lie between residues 186 and 195 of gp46. This result was confirmed by independently synthesizing a peptide containing this epitope which bound specifically to mAb 0.5 alpha with an approximate Ka = 4 x 10(7) M-1. In addition, the peptide inhibited mAb 0.5 alpha binding to gp46 derived from T-cells infected with HTLV-I. This epitope containing peptide may facilitate understanding HTLV-1 infection of T-cells.\n",
      "--------------------------------------------------------------------------------\n"
     ]
    }
   ],
   "source": [
    "from Bio import Entrez\n",
    "\n",
    "\n",
    "def get_pubmed_abstract(pubmed_id):\n",
    "    \"\"\"\n",
    "    Retrieve the abstract of a PubMed article using its PubMed ID.\n",
    "\n",
    "    :param pubmed_id: The PubMed ID of the article.\n",
    "    :return: The abstract of the article.\n",
    "    \"\"\"\n",
    "    # Use your email here. NCBI recommends providing it.\n",
    "    Entrez.email = \"your.email@example.com\"\n",
    "\n",
    "    try:\n",
    "        handle = Entrez.efetch(db=\"pubmed\", id=pubmed_id,\n",
    "                               rettype=\"abstract\", retmode=\"html\")\n",
    "        abstract = handle.read()\n",
    "        handle.close()\n",
    "        return extract_abstract_from_html(abstract)\n",
    "    except Exception as e:\n",
    "        return f\"An error occurred: {str(e)}\"\n",
    "\n",
    "\n",
    "cells = {\n",
    "    'MV4-11': {\n",
    "        'pubmed': [\n",
    "            '1423625',\n",
    "            '2656885',\n",
    "            '3496132',\n",
    "            '8353274',\n",
    "            '8358709',\n",
    "            '9195772',\n",
    "            '12529668',\n",
    "            '14504097',\n",
    "            '14671638',\n",
    "            '15843827',\n",
    "            '16408098',\n",
    "            '19608861',\n",
    "            '20215515',\n",
    "            '20922763',\n",
    "            '21552520',\n",
    "            '22460905',\n",
    "            '25485619',\n",
    "            '25877200',\n",
    "            '25984343',\n",
    "            '26589293',\n",
    "            '27397505',\n",
    "            '30285677',\n",
    "            '30629668',\n",
    "            '30894373',\n",
    "            '31068700',\n",
    "            '35839778',\n",
    "        ],\n",
    "    },\n",
    "    'LNCaP': {\n",
    "        'pubmed': [\n",
    "            '2734981',\n",
    "            '3335022',\n",
    "            '3518877',\n",
    "            '6831420',\n",
    "            '8687134',\n",
    "            '9018337',\n",
    "            '9090379',\n",
    "            '10702678',\n",
    "            '10972993',\n",
    "            '11135431',\n",
    "            '11172901',\n",
    "            '11304728',\n",
    "            '11414198',\n",
    "            '11416159',\n",
    "            '12606952',\n",
    "            '12725112',\n",
    "            '14518029',\n",
    "            '15162376',\n",
    "            '15486987',\n",
    "            '22213130',\n",
    "            '22278370',\n",
    "            '23671654',\n",
    "            '24587179',\n",
    "            '24618588',\n",
    "            '25485619',\n",
    "            '25877200',\n",
    "            '26256267',\n",
    "            '26589293',\n",
    "            '26972028',\n",
    "            '27036029',\n",
    "            '27141528',\n",
    "            '29233929',\n",
    "            '29660373',\n",
    "            '29739788',\n",
    "            '30787054',\n",
    "            '35502546',\n",
    "        ],\n",
    "    },\n",
    "    'MM.1S': {\n",
    "        'pubmed': [\n",
    "            '12691914',\n",
    "            '14760100',\n",
    "            '16956823',\n",
    "            '17692805',\n",
    "            '18647998',\n",
    "            '21173094',\n",
    "            '22460905',\n",
    "            '25485619',\n",
    "            '25688540',\n",
    "            '25877200',\n",
    "            '25984343',\n",
    "            '26589293',\n",
    "            '27397505',\n",
    "            '28196595',\n",
    "            '30285677',\n",
    "            '30545397',\n",
    "            '30894373',\n",
    "            '30971826',\n",
    "            '31068700',\n",
    "            '32123307',\n",
    "            '35839778',\n",
    "        ],\n",
    "    },\n",
    "}\n",
    "\n",
    "# # Example usage\n",
    "# pubmed_id = \"1659122\"  # Replace with a real PubMed ID\n",
    "# print(get_pubmed_abstract(pubmed_id))\n",
    "\n",
    "pubmed_ids = [\n",
    "    \"16780947\",\n",
    "    \"20054396\",\n",
    "    \"21179087\",\n",
    "    \"23257783\",\n",
    "    \"23292937\",\n",
    "    # \"25485619\",\n",
    "    # \"26589293\",\n",
    "    # \"26787899\",\n",
    "    # \"27566572\",\n",
    "    # \"29416618\",\n",
    "    # \"29666304\",\n",
    "    # --------------------\n",
    "    \"2476442\",  # Other cell type\n",
    "]\n",
    "pubmed2abstract = {}\n",
    "for pubmed_id in pubmed_ids:\n",
    "    pubmed2abstract[pubmed_id] = get_pubmed_abstract(pubmed_id)\n",
    "    print(f\"Retrieved abstract for PubMed ID: {pubmed2abstract[pubmed_id]}\")\n",
    "    print('-' * 80)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [],
   "source": [
    "from transformers import pipeline\n",
    "import torch\n",
    "\n",
    "if torch.cuda.is_available():\n",
    "    device = 0  # GPU\n",
    "else:\n",
    "    device = \"cpu\"\n",
    "pipe = pipeline(\n",
    "    \"feature-extraction\",\n",
    "    model=\"dmis-lab/biobert-v1.1\",\n",
    "    device=device,\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "--------------------------------------------------------------------------------\n",
      "PubMed ID: 16780947\n",
      "Abstract: A novel lymphoma cell line, designated TMD8 was established from cells of a patient with diffuse large B-cell lymphoma. TMD8 cells expressed HES1 mRNA, suggesting constitutive activation of Notch signaling. TMD8 cells expressed normal-sized Notch1 protein, and showed no mutations in the NOTCH1 gene. Cell growth was suppressed by gamma-secretase inhibitors (GSI). It was reported that GSI suppressed growth of T-cell acute lymphoblastic leukemia (T-ALL) cell lines, which frequently had NOTCH1 mutations. In addition to T-ALL, TMD8 is another unique cell line sensitive to GSI, and is useful to study effects of GSI in molecular targeting therapy.\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "c:\\Users\\ste\\Anaconda2\\envs\\env-thesis\\Lib\\site-packages\\transformers\\pipelines\\base.py:997: UserWarning: You seem to be using the pipelines sequentially on GPU. In order to maximize efficiency please use a dataset\n",
      "  warnings.warn(\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Number of features: 1\n",
      "Shape of the features: (1, 134, 768)\n",
      "--------------------------------------------------------------------------------\n",
      "PubMed ID: 20054396\n",
      "Abstract: A role for B-cell-receptor (BCR) signalling in lymphomagenesis has been inferred by studying immunoglobulin genes in human lymphomas and by engineering mouse models, but genetic and functional evidence for its oncogenic role in human lymphomas is needed. Here we describe a form of 'chronic active' BCR signalling that is required for cell survival in the activated B-cell-like (ABC) subtype of diffuse large B-cell lymphoma (DLBCL). The signalling adaptor CARD11 is required for constitutive NF-kappaB pathway activity and survival in ABC DLBCL. Roughly 10% of ABC DLBCLs have mutant CARD11 isoforms that activate NF-kappaB, but the mechanism that engages wild-type CARD11 in other ABC DLBCLs was unknown. An RNA interference genetic screen revealed that a BCR signalling component, Bruton's tyrosine kinase, is essential for the survival of ABC DLBCLs with wild-type CARD11. In addition, knockdown of proximal BCR subunits (IgM, Ig-kappa, CD79A and CD79B) killed ABC DLBCLs with wild-type CARD11 but not other lymphomas. The BCRs in these ABC DLBCLs formed prominent clusters in the plasma membrane with low diffusion, similarly to BCRs in antigen-stimulated normal B cells. Somatic mutations affecting the immunoreceptor tyrosine-based activation motif (ITAM) signalling modules of CD79B and CD79A were detected frequently in ABC DLBCL biopsy samples but rarely in other DLBCLs and never in Burkitt's lymphoma or mucosa-associated lymphoid tissue lymphoma. In 18% of ABC DLBCLs, one functionally critical residue of CD79B, the first ITAM tyrosine, was mutated. These mutations increased surface BCR expression and attenuated Lyn kinase, a feedback inhibitor of BCR signalling. These findings establish chronic active BCR signalling as a new pathogenetic mechanism in ABC DLBCL, suggesting several therapeutic strategies.\n",
      "Number of features: 1\n",
      "Shape of the features: (1, 137, 768)\n",
      "--------------------------------------------------------------------------------\n",
      "PubMed ID: 21179087\n",
      "Abstract: The activated B-cell-like (ABC) subtype of diffuse large B-cell lymphoma (DLBCL) remains the least curable form of this malignancy despite recent advances in therapy. Constitutive nuclear factor (NF)-κB and JAK kinase signalling promotes malignant cell survival in these lymphomas, but the genetic basis for this signalling is incompletely understood. Here we describe the dependence of ABC DLBCLs on MYD88, an adaptor protein that mediates toll and interleukin (IL)-1 receptor signalling, and the discovery of highly recurrent oncogenic mutations affecting MYD88 in ABC DLBCL tumours. RNA interference screening revealed that MYD88 and the associated kinases IRAK1 and IRAK4 are essential for ABC DLBCL survival. High-throughput RNA resequencing uncovered MYD88 mutations in ABC DLBCL lines. Notably, 29% of ABC DLBCL tumours harboured the same amino acid substitution, L265P, in the MYD88 Toll/IL-1 receptor (TIR) domain at an evolutionarily invariant residue in its hydrophobic core. This mutation was rare or absent in other DLBCL subtypes and Burkitt's lymphoma, but was observed in 9% of mucosa-associated lymphoid tissue lymphomas. At a lower frequency, additional mutations were observed in the MYD88 TIR domain, occurring in both the ABC and germinal centre B-cell-like (GCB) DLBCL subtypes. Survival of ABC DLBCL cells bearing the L265P mutation was sustained by the mutant but not the wild-type MYD88 isoform, demonstrating that L265P is a gain-of-function driver mutation. The L265P mutant promoted cell survival by spontaneously assembling a protein complex containing IRAK1 and IRAK4, leading to IRAK4 kinase activity, IRAK1 phosphorylation, NF-κB signalling, JAK kinase activation of STAT3, and secretion of IL-6, IL-10 and interferon-β. Hence, the MYD88 signalling pathway is integral to the pathogenesis of ABC DLBCL, supporting the development of inhibitors of IRAK4 kinase and other components of this pathway for the treatment of tumours bearing oncogenic MYD88 mutations.\n",
      "Number of features: 1\n",
      "Shape of the features: (1, 136, 768)\n",
      "--------------------------------------------------------------------------------\n",
      "PubMed ID: 23257783\n",
      "Abstract: Myeloid cell leukemia-1 (MCL1) is an anti-apoptotic member of the BCL2 family that is deregulated in various solid and hematological malignancies. However, its role in the molecular pathogenesis of diffuse large B-cell lymphoma (DLBCL) is unclear. We analyzed gene expression profiling data from 350 DLBCL patient samples and detected that activated B-cell-like (ABC) DLBCLs express MCL1 at significantly higher levels compared with germinal center B-cell-like DLBCL patient samples (P=2.7 × 10(-10)). Immunohistochemistry confirmed high MCL1 protein expression predominantly in ABC DLBCL in an independent patient cohort (n=249; P=0.001). To elucidate molecular mechanisms leading to aberrant MCL1 expression, we analyzed array comparative genomic hybridization data of 203 DLBCL samples and identified recurrent chromosomal gains/amplifications of the MCL1 locus that occurred in 26% of ABC DLBCLs. In addition, aberrant STAT3 signaling contributed to high MCL1 expression in this subtype. Knockdown of MCL1 as well as treatment with the BH3-mimetic obatoclax induced apoptotic cell death in MCL1-positive DLBCL cell lines. In summary, MCL1 is deregulated in a significant fraction of ABC DLBCLs and contributes to therapy resistance. These data suggest that specific inhibition of MCL1 might be utilized therapeutically in a subset of DLBCLs.\n",
      "Number of features: 1\n",
      "Shape of the features: (1, 152, 768)\n",
      "--------------------------------------------------------------------------------\n",
      "PubMed ID: 23292937\n",
      "Abstract: Diffuse large B-cell lymphoma (DLBCL) is the most common form of lymphoma in adults. The disease exhibits a striking heterogeneity in gene expression profiles and clinical outcomes, but its genetic causes remain to be fully defined. Through whole genome and exome sequencing, we characterized the genetic diversity of DLBCL. In all, we sequenced 73 DLBCL primary tumors (34 with matched normal DNA). Separately, we sequenced the exomes of 21 DLBCL cell lines. We identified 322 DLBCL cancer genes that were recurrently mutated in primary DLBCLs. We identified recurrent mutations implicating a number of known and not previously identified genes and pathways in DLBCL including those related to chromatin modification (ARID1A and MEF2B), NF-κB (CARD11 and TNFAIP3), PI3 kinase (PIK3CD, PIK3R1, and MTOR), B-cell lineage (IRF8, POU2F2, and GNA13), and WNT signaling (WIF1). We also experimentally validated a mutation in PIK3CD, a gene not previously implicated in lymphomas. The patterns of mutation demonstrated a classic long tail distribution with substantial variation of mutated genes from patient to patient and also between published studies. Thus, our study reveals the tremendous genetic heterogeneity that underlies lymphomas and highlights the need for personalized medicine approaches to treating these patients.\n",
      "Number of features: 1\n",
      "Shape of the features: (1, 127, 768)\n",
      "--------------------------------------------------------------------------------\n",
      "PubMed ID: 2476442\n",
      "Abstract: A monoclonal antibody (mAb), designated 0.5 alpha, derived from a patient with adult T-cell leukemia was found previously to neutralize the human T-cell leukemia/lymphotropic type I (HTLV-I) virus in in vitro assays and bind to the major envelope glycoprotein (gp46) of HTLV-I (Matsushita, S., Guroff, M.R., Trepel, J., Crossman, J., Mitsuya, H., and Broder, S. (1986) Proc. Natl. Acad. Sci. U.S.A. 83, 2671-2676). We have designed experiments to determine the epitope for this mAb. Using simultaneous multiple peptide synthesis, we synthesized 481 overlapping octapeptides which corresponded to the sequence of gp46. We mapped the epitope for mAb 0.5 alpha to lie between residues 186 and 195 of gp46. This result was confirmed by independently synthesizing a peptide containing this epitope which bound specifically to mAb 0.5 alpha with an approximate Ka = 4 x 10(7) M-1. In addition, the peptide inhibited mAb 0.5 alpha binding to gp46 derived from T-cells infected with HTLV-I. This epitope containing peptide may facilitate understanding HTLV-1 infection of T-cells.\n",
      "Number of features: 1\n",
      "Shape of the features: (1, 189, 768)\n"
     ]
    }
   ],
   "source": [
    "import numpy as np\n",
    "\n",
    "pubmed2features = {}\n",
    "\n",
    "for pubmed_id, abstract in pubmed2abstract.items():\n",
    "    print('-' * 80)\n",
    "    print(f\"PubMed ID: {pubmed_id}\")\n",
    "    print(f\"Abstract: {abstract}\")\n",
    "\n",
    "    # Extract features\n",
    "    features = pipe(abstract[:512])\n",
    "\n",
    "    print(f\"Number of features: {len(features)}\")\n",
    "    print(f\"Shape of the features: {np.array(features).shape}\")\n",
    "\n",
    "    pubmed2features[pubmed_id] = np.array(features)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "--------------------------------------------------------------------------------\n",
      "PubMed ID 1: 16780947\n",
      "PubMed ID 2: 20054396\n",
      "Cosine similarity: 0.5031364979399338\n",
      "--------------------------------------------------------------------------------\n",
      "PubMed ID 1: 16780947\n",
      "PubMed ID 2: 21179087\n",
      "Cosine similarity: 0.4757581799666462\n",
      "--------------------------------------------------------------------------------\n",
      "PubMed ID 1: 16780947\n",
      "PubMed ID 2: 23257783\n",
      "Cosine similarity: 0.47590786790387574\n",
      "--------------------------------------------------------------------------------\n",
      "PubMed ID 1: 16780947\n",
      "PubMed ID 2: 23292937\n",
      "Cosine similarity: 0.4879428252319288\n",
      "--------------------------------------------------------------------------------\n",
      "PubMed ID 1: 16780947\n",
      "PubMed ID 2: 2476442\n",
      "Cosine similarity: 0.4214291931633308\n",
      "--------------------------------------------------------------------------------\n",
      "PubMed ID 1: 20054396\n",
      "PubMed ID 2: 21179087\n",
      "Cosine similarity: 0.5045157039333007\n",
      "--------------------------------------------------------------------------------\n",
      "PubMed ID 1: 20054396\n",
      "PubMed ID 2: 23257783\n",
      "Cosine similarity: 0.49657810363333577\n",
      "--------------------------------------------------------------------------------\n",
      "PubMed ID 1: 20054396\n",
      "PubMed ID 2: 23292937\n",
      "Cosine similarity: 0.5086263451426487\n",
      "--------------------------------------------------------------------------------\n",
      "PubMed ID 1: 20054396\n",
      "PubMed ID 2: 2476442\n",
      "Cosine similarity: 0.4304950016743829\n",
      "--------------------------------------------------------------------------------\n",
      "PubMed ID 1: 21179087\n",
      "PubMed ID 2: 23257783\n",
      "Cosine similarity: 0.47206888505666444\n",
      "--------------------------------------------------------------------------------\n",
      "PubMed ID 1: 21179087\n",
      "PubMed ID 2: 23292937\n",
      "Cosine similarity: 0.4819276538197922\n",
      "--------------------------------------------------------------------------------\n",
      "PubMed ID 1: 21179087\n",
      "PubMed ID 2: 2476442\n",
      "Cosine similarity: 0.40880872034849103\n",
      "--------------------------------------------------------------------------------\n",
      "PubMed ID 1: 23257783\n",
      "PubMed ID 2: 23292937\n",
      "Cosine similarity: 0.4828115019411304\n",
      "--------------------------------------------------------------------------------\n",
      "PubMed ID 1: 23257783\n",
      "PubMed ID 2: 2476442\n",
      "Cosine similarity: 0.4105191548824988\n",
      "--------------------------------------------------------------------------------\n",
      "PubMed ID 1: 23292937\n",
      "PubMed ID 2: 2476442\n",
      "Cosine similarity: 0.4156930615669943\n"
     ]
    }
   ],
   "source": [
    "# Calculate the cosine similarity between the features of all pairs of articles\n",
    "from sklearn.metrics.pairwise import cosine_similarity\n",
    "\n",
    "pubmed_ids = list(pubmed2features.keys())\n",
    "pubmed_ids.sort()\n",
    "for i, pubmed_id1 in enumerate(pubmed_ids):\n",
    "    for j, pubmed_id2 in enumerate(pubmed_ids):\n",
    "        if i < j:\n",
    "            print('-' * 80)\n",
    "            print(f\"PubMed ID 1: {pubmed_id1}\")\n",
    "            print(f\"PubMed ID 2: {pubmed_id2}\")\n",
    "            print(\n",
    "                f\"Cosine similarity: {np.mean(cosine_similarity(pubmed2features[pubmed_id1][0], pubmed2features[pubmed_id2][0]))}\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(768,)\n",
      "(768,)\n",
      "Cosine similarity: 0.931316114549088\n"
     ]
    }
   ],
   "source": [
    "cell_emb1 = np.mean(\n",
    "    np.vstack([pubmed2features[p][0]\n",
    "              for p in [\"16780947\", \"20054396\", \"21179087\", \"23257783\", \"23292937\"]]),\n",
    "    axis=0,\n",
    ")\n",
    "cell_emb2 = np.mean(pubmed2features[\"2476442\"][0], axis=0)\n",
    "\n",
    "print(cell_emb1.shape)\n",
    "print(cell_emb2.shape)\n",
    "\n",
    "print(\n",
    "    f\"Cosine similarity: {np.mean(cosine_similarity(cell_emb1[None], cell_emb2[None]))}\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'[CLS] celsr2 [SEP]'"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Load tokenizer\n",
    "from transformers import AutoTokenizer\n",
    "\n",
    "tokenizer = AutoTokenizer.from_pretrained(\n",
    "    \"jinaai/jina-embeddings-v2-base-en\"\n",
    "    # \"sentence-transformers/all-mpnet-base-v2\"\n",
    ")\n",
    "encoded = tokenizer('CELSR2')\n",
    "tokenizer.decode(encoded['input_ids'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'<s> celsr2 </s>'"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "tokenizer = AutoTokenizer.from_pretrained(\n",
    "    # \"jinaai/jina-embeddings-v2-base-en\"\n",
    "    \"sentence-transformers/all-mpnet-base-v2\"\n",
    ")\n",
    "encoded = tokenizer('CELSR2')\n",
    "tokenizer.decode(encoded['input_ids'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "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>ID</th>\n",
       "      <th>AC</th>\n",
       "      <th>SY</th>\n",
       "      <th>DR</th>\n",
       "      <th>RX</th>\n",
       "      <th>CC</th>\n",
       "      <th>OX</th>\n",
       "      <th>HI</th>\n",
       "      <th>CA</th>\n",
       "      <th>DT</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>#132 PC3-1-SC-E8</td>\n",
       "      <td>CVCL_B0T9</td>\n",
       "      <td>Z48-5MG-70</td>\n",
       "      <td>[Wikidata; Q108819335]</td>\n",
       "      <td>[Patent=EP0501779A1;]</td>\n",
       "      <td>[Group: Patented cell line., Registration: Int...</td>\n",
       "      <td>NCBI_TaxID=10090; ! Mus musculus (Mouse)</td>\n",
       "      <td>CVCL_D145 ! HL-1 Friendly Myeloma-653</td>\n",
       "      <td>Hybridoma</td>\n",
       "      <td>Created: 23-09-21; Last updated: 30-01-24; Ver...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>#132 PL12 SC-D1</td>\n",
       "      <td>CVCL_B0T8</td>\n",
       "      <td>Z48-5MG-63</td>\n",
       "      <td>[Wikidata; Q108819336]</td>\n",
       "      <td>[Patent=EP0501779A1;]</td>\n",
       "      <td>[Group: Patented cell line., Registration: Int...</td>\n",
       "      <td>NCBI_TaxID=10090; ! Mus musculus (Mouse)</td>\n",
       "      <td>CVCL_D145 ! HL-1 Friendly Myeloma-653</td>\n",
       "      <td>Hybridoma</td>\n",
       "      <td>Created: 23-09-21; Last updated: 30-01-24; Ver...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>#15310-LN</td>\n",
       "      <td>CVCL_E548</td>\n",
       "      <td>15310-LN; TER461; TER-461; Ter 461; TER479; TE...</td>\n",
       "      <td>[dbMHC; 48439, ECACC; 94050311, IHW; IHW09326,...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>[Part of: 12th International Histocompatibilit...</td>\n",
       "      <td>NCBI_TaxID=9606; ! Homo sapiens (Human)</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Transformed cell line</td>\n",
       "      <td>Created: 22-10-12; Last updated: 30-01-24; Ver...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>#16-15</td>\n",
       "      <td>CVCL_KA96</td>\n",
       "      <td>NaN</td>\n",
       "      <td>[RCB; RCB4635, Wikidata; Q54422067]</td>\n",
       "      <td>[PubMed=25400923;]</td>\n",
       "      <td>[Monoclonal antibody isotype: IgM., Monoclonal...</td>\n",
       "      <td>NCBI_TaxID=10116; ! Rattus norvegicus (Rat)</td>\n",
       "      <td>CVCL_4032 ! P3X63Ag8.653</td>\n",
       "      <td>Hybridoma</td>\n",
       "      <td>Created: 22-08-17; Last updated: 21-03-23; Ver...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>#40a</td>\n",
       "      <td>CVCL_IW91</td>\n",
       "      <td>NaN</td>\n",
       "      <td>[Wikidata; Q54422071]</td>\n",
       "      <td>[PubMed=28159921;]</td>\n",
       "      <td>[Characteristics: Established from parent cell...</td>\n",
       "      <td>NCBI_TaxID=10090; ! Mus musculus (Mouse)</td>\n",
       "      <td>CVCL_IW90 ! 40</td>\n",
       "      <td>Cancer cell line</td>\n",
       "      <td>Created: 15-05-17; Last updated: 29-06-23; Ver...</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                 ID         AC  \\\n",
       "0  #132 PC3-1-SC-E8  CVCL_B0T9   \n",
       "1   #132 PL12 SC-D1  CVCL_B0T8   \n",
       "2         #15310-LN  CVCL_E548   \n",
       "3            #16-15  CVCL_KA96   \n",
       "4              #40a  CVCL_IW91   \n",
       "\n",
       "                                                  SY  \\\n",
       "0                                         Z48-5MG-70   \n",
       "1                                         Z48-5MG-63   \n",
       "2  15310-LN; TER461; TER-461; Ter 461; TER479; TE...   \n",
       "3                                                NaN   \n",
       "4                                                NaN   \n",
       "\n",
       "                                                  DR                     RX  \\\n",
       "0                             [Wikidata; Q108819335]  [Patent=EP0501779A1;]   \n",
       "1                             [Wikidata; Q108819336]  [Patent=EP0501779A1;]   \n",
       "2  [dbMHC; 48439, ECACC; 94050311, IHW; IHW09326,...                    NaN   \n",
       "3                [RCB; RCB4635, Wikidata; Q54422067]     [PubMed=25400923;]   \n",
       "4                              [Wikidata; Q54422071]     [PubMed=28159921;]   \n",
       "\n",
       "                                                  CC  \\\n",
       "0  [Group: Patented cell line., Registration: Int...   \n",
       "1  [Group: Patented cell line., Registration: Int...   \n",
       "2  [Part of: 12th International Histocompatibilit...   \n",
       "3  [Monoclonal antibody isotype: IgM., Monoclonal...   \n",
       "4  [Characteristics: Established from parent cell...   \n",
       "\n",
       "                                            OX  \\\n",
       "0     NCBI_TaxID=10090; ! Mus musculus (Mouse)   \n",
       "1     NCBI_TaxID=10090; ! Mus musculus (Mouse)   \n",
       "2      NCBI_TaxID=9606; ! Homo sapiens (Human)   \n",
       "3  NCBI_TaxID=10116; ! Rattus norvegicus (Rat)   \n",
       "4     NCBI_TaxID=10090; ! Mus musculus (Mouse)   \n",
       "\n",
       "                                      HI                     CA  \\\n",
       "0  CVCL_D145 ! HL-1 Friendly Myeloma-653              Hybridoma   \n",
       "1  CVCL_D145 ! HL-1 Friendly Myeloma-653              Hybridoma   \n",
       "2                                    NaN  Transformed cell line   \n",
       "3               CVCL_4032 ! P3X63Ag8.653              Hybridoma   \n",
       "4                         CVCL_IW90 ! 40       Cancer cell line   \n",
       "\n",
       "                                                  DT  \n",
       "0  Created: 23-09-21; Last updated: 30-01-24; Ver...  \n",
       "1  Created: 23-09-21; Last updated: 30-01-24; Ver...  \n",
       "2  Created: 22-10-12; Last updated: 30-01-24; Ver...  \n",
       "3  Created: 22-08-17; Last updated: 21-03-23; Ver...  \n",
       "4  Created: 15-05-17; Last updated: 29-06-23; Ver...  "
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import pandas as pd\n",
    "\n",
    "pd.DataFrame(cell_lines).head()"
   ]
  }
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