nbs/HLA1_Classifier.ipynb

{
  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "view-in-github",
        "colab_type": "text"
      },
      "source": [
        "<a href=\"https://colab.research.google.com/github/jalew188/PeptDeep-HLA/blob/master/nbs/HLA1_Classifier.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "qQyZe9zp6ze6"
      },
      "source": [
        "# Prediction of presentation of HLA-I peptides"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "%pip install -q git+https://github.com/MannLabs/PeptDeep-HLA.git"
      ],
      "metadata": {
        "id": "i9gHuKcxyXQ5",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "b534b636-c9fb-4f86-9c24-98b5ecc405e1"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
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            "Installing collected packages: smmap, gitdb, commonmark, watchdog, validators, semver, rich, pympler, pydeck, gitpython, blinker, tokenizers, streamlit, python-decouple, mmh3, huggingface-hub, biopython, transformers, streamlit-aggrid, alphabase, pydivsufsort, peptdeep, peptdeep-hla\n",
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          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "%pip install -q gdown"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "XhwF5kDU0Hue",
        "outputId": "6859667f-d22d-46da-abda-923b7e15453c"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
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          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "jiyKoMTI6ze-"
      },
      "outputs": [],
      "source": [
        "%reload_ext autoreload\n",
        "%autoreload 2"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "import gdown\n",
        "HLA1_seq_url = \"https://drive.google.com/file/d/1MMKbqfT86SXAj37F4MPTJg6NWXDGC-Id/view?usp=share_link\"\n",
        "fasta_url = \"https://drive.google.com/file/d/1V9KxDniKwZFZnHlP58EbjkuelNJnp1Kq/view?usp=share_link\"\n",
        "seq_file = r'IEDB_HLA1_seqs.tsv'\n",
        "save_as = r'HLA1_IEDB.pt'\n",
        "fasta = r'UP000005640_human_reviewed.fasta'\n",
        "pretrained_model = None\n",
        "\n",
        "gdown.download(HLA1_seq_url, seq_file, fuzzy=True)\n",
        "gdown.download(fasta_url, fasta, fuzzy=True)\n"
      ],
      "metadata": {
        "id": "TfyNFtjCxgZq",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 182
        },
        "outputId": "251d13ce-8a59-4ef7-a9a0-aa1f7b1cb6c9"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "Downloading...\n",
            "From: https://drive.google.com/uc?id=1MMKbqfT86SXAj37F4MPTJg6NWXDGC-Id\n",
            "To: /content/IEDB_HLA1_seqs.tsv\n",
            "100%|██████████| 8.45M/8.45M [00:00<00:00, 190MB/s]\n",
            "Downloading...\n",
            "From: https://drive.google.com/uc?id=1V9KxDniKwZFZnHlP58EbjkuelNJnp1Kq\n",
            "To: /content/UP000005640_human_reviewed.fasta\n",
            "100%|██████████| 13.6M/13.6M [00:00<00:00, 78.5MB/s]\n"
          ]
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "'UP000005640_human_reviewed.fasta'"
            ],
            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "string"
            }
          },
          "metadata": {},
          "execution_count": 4
        }
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "-GIRT57W6zfA"
      },
      "outputs": [],
      "source": [
        "### transfer learning\n",
        "# from peptdeep_hla.HLA_class_I import pretrained_HLA1\n",
        "# pretrained_model = pretrained_HLA1"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "4NtlcRmo6zfA"
      },
      "source": [
        "For transfer learning of sample-specific models, please set pretrained_model as an existing model file and use sample-specific sequences instead of seq_file."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "DGaAKT1r6zfB",
        "outputId": "e57b421c-5e85-4c01-f434-91068c126448",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 424
        }
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "               sequence  nAA  HLA\n",
              "0         AAAAAAAAAAAAA   13    1\n",
              "1             AAAAPYAGW    9    1\n",
              "2             AAAARAAAL    9    1\n",
              "3             AAAATCALV    9    1\n",
              "4             AAAKAAAAV    9    1\n",
              "...                 ...  ...  ...\n",
              "705738       VVVSIDRFLR   10    1\n",
              "705739        VYNMVVKL     9    1\n",
              "705740  WGKSKEWGRNCKGCN   15    1\n",
              "705741         YASLRSLV    8    1\n",
              "705742        YGDSKPQKF    9    1\n",
              "\n",
              "[705743 rows x 3 columns]"
            ],
            "text/html": [
              "\n",
              "  <div id=\"df-440348ae-cb09-4943-a556-e3eecf55ce04\">\n",
              "    <div class=\"colab-df-container\">\n",
              "      <div>\n",
              "<style scoped>\n",
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              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>sequence</th>\n",
              "      <th>nAA</th>\n",
              "      <th>HLA</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>AAAAAAAAAAAAA</td>\n",
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              "      <td>1</td>\n",
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              "      <td>AAAAPYAGW</td>\n",
              "      <td>9</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>AAAARAAAL</td>\n",
              "      <td>9</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>AAAATCALV</td>\n",
              "      <td>9</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>AAAKAAAAV</td>\n",
              "      <td>9</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>...</th>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>705738</th>\n",
              "      <td>VVVSIDRFLR</td>\n",
              "      <td>10</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>705739</th>\n",
              "      <td>VYNMVVKL</td>\n",
              "      <td>9</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>705740</th>\n",
              "      <td>WGKSKEWGRNCKGCN</td>\n",
              "      <td>15</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>705741</th>\n",
              "      <td>YASLRSLV</td>\n",
              "      <td>8</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>705742</th>\n",
              "      <td>YGDSKPQKF</td>\n",
              "      <td>9</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>705743 rows × 3 columns</p>\n",
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              "    [theme=dark] .colab-df-convert {\n",
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              "          const element = document.querySelector('#df-440348ae-cb09-4943-a556-e3eecf55ce04');\n",
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              "                                                     [key], {});\n",
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              "  </div>\n",
              "  "
            ]
          },
          "metadata": {},
          "execution_count": 6
        }
      ],
      "source": [
        "import pandas as pd\n",
        "pep_df = pd.read_csv(seq_file)\n",
        "pep_df['nAA'] = pep_df.sequence.str.len()\n",
        "pep_df['HLA'] = 1\n",
        "pep_df"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "EaYo4xps6zfC"
      },
      "outputs": [],
      "source": [
        "def split_to_two_dfs(df, ratio=0.8):\n",
        "    train_df = df.sample(frac=ratio, replace=False)\n",
        "    test_df = df.drop(train_df.index)\n",
        "    return train_df.reset_index(drop=True), test_df.reset_index(drop=True)\n",
        "\n",
        "pos_train_df, pos_test_df = split_to_two_dfs(pep_df, 0.8)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "1ogqHQPP6zfD"
      },
      "outputs": [],
      "source": [
        "from peptdeep_hla.utils import get_random_sequences\n",
        "\n",
        "def concat_neg_df(pos_df, prot_df):\n",
        "    df_list = [pos_df]\n",
        "    for nAA, group_df in pos_df.groupby('nAA'):\n",
        "        rnd_seqs = get_random_sequences(\n",
        "            prot_df, \n",
        "            n=len(group_df),\n",
        "            pep_len = nAA\n",
        "        )\n",
        "        df_list.append(pd.DataFrame(\n",
        "            dict(sequence=rnd_seqs,nAA=nAA,HLA=0)\n",
        "        ))\n",
        "    return pd.concat(df_list).reset_index(drop=True)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "jnmFdWms6zfD",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "e0d48a46-75e9-410d-f832-78fbf433836f"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "2022-12-29 01:44:27> Downloading pretrained_models.zip ...\n",
            "2022-12-29 01:44:28> The pretrained models had been downloaded in /root/peptdeep/pretrained_models/pretrained_models.zip\n"
          ]
        }
      ],
      "source": [
        "from peptdeep_hla.HLA_class_I import HLA_Class_I_Classifier"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "T0N2tL9v6zfE"
      },
      "source": [
        "## Build, train and predict"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "SlcQANc96zfE",
        "outputId": "88666879-bd36-4f33-a274-41e9dd7f243a",
        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "1669697"
            ]
          },
          "metadata": {},
          "execution_count": 10
        }
      ],
      "source": [
        "model = HLA_Class_I_Classifier(\n",
        "    fasta_files=[fasta],\n",
        ")\n",
        "if pretrained_model:\n",
        "    model.load(pretrained_model)\n",
        "model.get_parameter_num()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "zfLtQ4Jk6zfF",
        "outputId": "ea1c92dd-0932-40c3-e0db-49e4d106d016",
        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "[Training] Epoch=1, lr=5e-06, loss=0.6933580278674235\n",
            "[Training] Epoch=2, lr=1e-05, loss=0.6754722594088102\n",
            "[Training] Epoch=3, lr=1.5e-05, loss=0.5297332382382427\n",
            "[Training] Epoch=4, lr=2e-05, loss=0.480515140385168\n",
            "[Training] Epoch=5, lr=2.5e-05, loss=0.45667302816937233\n",
            "[Training] Epoch=6, lr=3e-05, loss=0.4495536099084158\n",
            "[Training] Epoch=7, lr=3.5e-05, loss=0.44047606413891727\n",
            "[Training] Epoch=8, lr=4e-05, loss=0.4389060655703842\n",
            "[Training] Epoch=9, lr=4.5e-05, loss=0.43643731560508786\n",
            "[Training] Epoch=10, lr=5e-05, loss=0.4360491626654771\n",
            "[Training] Epoch=11, lr=5.500000000000001e-05, loss=0.429061990097475\n",
            "[Training] Epoch=12, lr=6e-05, loss=0.4286614368002446\n",
            "[Training] Epoch=13, lr=6.500000000000001e-05, loss=0.4262140641703723\n",
            "[Training] Epoch=14, lr=7e-05, loss=0.42403797937701465\n",
            "[Training] Epoch=15, lr=7.500000000000001e-05, loss=0.41956930462489284\n",
            "[Training] Epoch=16, lr=8e-05, loss=0.4148490408079836\n",
            "[Training] Epoch=17, lr=8.5e-05, loss=0.41393732207709316\n",
            "[Training] Epoch=18, lr=9e-05, loss=0.41151303193744954\n",
            "[Training] Epoch=19, lr=9.5e-05, loss=0.40931793867076954\n",
            "[Training] Epoch=20, lr=0.0001, loss=0.40689593316927297\n",
            "[Training] Epoch=21, lr=9.996145181203615e-05, loss=0.40372099802038847\n",
            "[Training] Epoch=22, lr=9.98458666866564e-05, loss=0.4042802637826743\n",
            "[Training] Epoch=23, lr=9.965342284774632e-05, loss=0.4008667640289422\n",
            "[Training] Epoch=24, lr=9.938441702975689e-05, loss=0.39821083418362985\n",
            "[Training] Epoch=25, lr=9.903926402016153e-05, loss=0.3991623419444367\n",
            "[Training] Epoch=26, lr=9.861849601988383e-05, loss=0.3966082459023409\n",
            "[Training] Epoch=27, lr=9.812276182268236e-05, loss=0.39673875173684103\n",
            "[Training] Epoch=28, lr=9.755282581475769e-05, loss=0.39468211420091653\n",
            "[Training] Epoch=29, lr=9.690956679612421e-05, loss=0.3929502010232785\n",
            "[Training] Epoch=30, lr=9.619397662556435e-05, loss=0.3937774211562649\n",
            "[Training] Epoch=31, lr=9.540715869125407e-05, loss=0.3916592339937538\n",
            "[Training] Epoch=32, lr=9.45503262094184e-05, loss=0.3898399244634776\n",
            "[Training] Epoch=33, lr=9.362480035363986e-05, loss=0.3889402135896773\n",
            "[Training] Epoch=34, lr=9.263200821770461e-05, loss=0.38660778524970285\n",
            "[Training] Epoch=35, lr=9.157348061512727e-05, loss=0.38667018996069247\n",
            "[Training] Epoch=36, lr=9.045084971874738e-05, loss=0.3848527992718161\n",
            "[Training] Epoch=37, lr=8.926584654403724e-05, loss=0.38417401295753867\n",
            "[Training] Epoch=38, lr=8.802029828000156e-05, loss=0.3839233760459662\n",
            "[Training] Epoch=39, lr=8.671612547178428e-05, loss=0.3834471276667258\n",
            "[Training] Epoch=40, lr=8.535533905932738e-05, loss=0.3814419550233167\n",
            "[Training] Epoch=41, lr=8.39400372766471e-05, loss=0.3815421482340375\n",
            "[Training] Epoch=42, lr=8.247240241650918e-05, loss=0.3794020321396213\n",
            "[Training] Epoch=43, lr=8.095469746549172e-05, loss=0.3799792000273901\n",
            "[Training] Epoch=44, lr=7.938926261462366e-05, loss=0.3790042185715782\n",
            "[Training] Epoch=45, lr=7.777851165098012e-05, loss=0.3772911426024987\n",
            "[Training] Epoch=46, lr=7.612492823579745e-05, loss=0.37687010789864006\n",
            "[Training] Epoch=47, lr=7.443106207484776e-05, loss=0.3761838626658759\n",
            "[Training] Epoch=48, lr=7.269952498697734e-05, loss=0.3745051567743766\n",
            "[Training] Epoch=49, lr=7.09329868768714e-05, loss=0.3745999556520711\n",
            "[Training] Epoch=50, lr=6.91341716182545e-05, loss=0.3745334654877452\n",
            "[Training] Epoch=51, lr=6.730585285387465e-05, loss=0.37324101127163\n",
            "[Training] Epoch=52, lr=6.545084971874738e-05, loss=0.3720428817438944\n",
            "[Training] Epoch=53, lr=6.357202249325371e-05, loss=0.37032282048227205\n",
            "[Training] Epoch=54, lr=6.167226819279528e-05, loss=0.3707283969074206\n",
            "[Training] Epoch=55, lr=5.9754516100806423e-05, loss=0.36959943856994687\n",
            "[Training] Epoch=56, lr=5.782172325201155e-05, loss=0.3689202238120744\n",
            "[Training] Epoch=57, lr=5.587686987289189e-05, loss=0.37028842184178545\n",
            "[Training] Epoch=58, lr=5.392295478639225e-05, loss=0.3688842769155881\n",
            "[Training] Epoch=59, lr=5.196299078795344e-05, loss=0.3666714936325816\n",
            "[Training] Epoch=60, lr=5e-05, loss=0.3670897659034044\n",
            "[Training] Epoch=61, lr=4.8037009212046586e-05, loss=0.3659021577785506\n",
            "[Training] Epoch=62, lr=4.607704521360776e-05, loss=0.36551022890159446\n",
            "[Training] Epoch=63, lr=4.412313012710813e-05, loss=0.3659091277474041\n",
            "[Training] Epoch=64, lr=4.2178276747988446e-05, loss=0.3648230017696301\n",
            "[Training] Epoch=65, lr=4.0245483899193595e-05, loss=0.36423861253689727\n",
            "[Training] Epoch=66, lr=3.832773180720475e-05, loss=0.36332537985929697\n",
            "[Training] Epoch=67, lr=3.642797750674629e-05, loss=0.36294885264227206\n",
            "[Training] Epoch=68, lr=3.4549150281252636e-05, loss=0.3617711653105478\n",
            "[Training] Epoch=69, lr=3.2694147146125345e-05, loss=0.3614386485181819\n",
            "[Training] Epoch=70, lr=3.086582838174551e-05, loss=0.361999765742704\n",
            "[Training] Epoch=71, lr=2.9067013123128613e-05, loss=0.3613100841725932\n",
            "[Training] Epoch=72, lr=2.7300475013022663e-05, loss=0.3603874738477804\n",
            "[Training] Epoch=73, lr=2.556893792515227e-05, loss=0.3596765146025638\n",
            "[Training] Epoch=74, lr=2.3875071764202563e-05, loss=0.3595350498513148\n",
            "[Training] Epoch=75, lr=2.2221488349019903e-05, loss=0.36008472653327683\n",
            "[Training] Epoch=76, lr=2.061073738537635e-05, loss=0.35924928574796433\n",
            "[Training] Epoch=77, lr=1.9045302534508297e-05, loss=0.3575950663233975\n",
            "[Training] Epoch=78, lr=1.7527597583490822e-05, loss=0.3578269688979439\n",
            "[Training] Epoch=79, lr=1.605996272335291e-05, loss=0.3574448504497514\n",
            "[Training] Epoch=80, lr=1.4644660940672627e-05, loss=0.3577045652388625\n",
            "[Training] Epoch=81, lr=1.3283874528215733e-05, loss=0.35804072816791066\n",
            "[Training] Epoch=82, lr=1.1979701719998453e-05, loss=0.3569799617130049\n",
            "[Training] Epoch=83, lr=1.0734153455962765e-05, loss=0.35745569810074085\n",
            "[Training] Epoch=84, lr=9.549150281252633e-06, loss=0.3564740904450191\n",
            "[Training] Epoch=85, lr=8.426519384872733e-06, loss=0.35660197942153266\n",
            "[Training] Epoch=86, lr=7.367991782295391e-06, loss=0.35762171709244556\n",
            "[Training] Epoch=87, lr=6.375199646360142e-06, loss=0.3561835256889322\n",
            "[Training] Epoch=88, lr=5.449673790581611e-06, loss=0.35651489681016746\n",
            "[Training] Epoch=89, lr=4.592841308745932e-06, loss=0.35628084058121606\n",
            "[Training] Epoch=90, lr=3.8060233744356633e-06, loss=0.35591269784478474\n",
            "[Training] Epoch=91, lr=3.0904332038757977e-06, loss=0.3560102956619515\n",
            "[Training] Epoch=92, lr=2.4471741852423237e-06, loss=0.35705451461678417\n",
            "[Training] Epoch=93, lr=1.8772381773176417e-06, loss=0.35641336491743425\n",
            "[Training] Epoch=94, lr=1.3815039801161721e-06, loss=0.35622010953259603\n",
            "[Training] Epoch=95, lr=9.607359798384785e-07, loss=0.35695527834793117\n",
            "[Training] Epoch=96, lr=6.15582970243117e-07, loss=0.356619160657569\n",
            "[Training] Epoch=97, lr=3.465771522536854e-07, loss=0.35726403840773496\n",
            "[Training] Epoch=98, lr=1.5413331334360182e-07, loss=0.35781737035298844\n",
            "[Training] Epoch=99, lr=3.8548187963854956e-08, loss=0.3580132657503586\n",
            "[Training] Epoch=100, lr=0.0, loss=0.3578559486987685\n"
          ]
        }
      ],
      "source": [
        "model.train(pos_train_df, epoch=100, warmup_epoch=20, verbose=True)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "xj1LIuSj6zfF"
      },
      "outputs": [],
      "source": [
        "model.save(save_as)"
      ]
    },
    {
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        "id": "jXnrrszq6zfF",
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        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 424
        }
      },
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        {
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          "data": {
            "text/plain": [
              "                                                 sequence  nAA  HLA  \\\n",
              "0                                           AAAAAAAAAAAAA   13    1   \n",
              "1                                               AAAATCALV    9    1   \n",
              "2                                               AAAKAAAAV    9    1   \n",
              "3                                              AAAKTPVIVV   10    1   \n",
              "4                                              AAAYFVGYLK   10    1   \n",
              "...                                                   ...  ...  ...   \n",
              "282293       GGRPRRTGLLSVELRVLDENDNPPVFEQDEYRAAVREDAQPGAE   44    0   \n",
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              "282297  APPIYQLNAPWLKGQERADLSNSLEEIYIQNIGESILYLWVEKIRD...   62    0   \n",
              "\n",
              "        HLA_prob_pred  \n",
              "0            0.753656  \n",
              "1            0.140352  \n",
              "2            0.848738  \n",
              "3            0.798607  \n",
              "4            0.598994  \n",
              "...               ...  \n",
              "282293       0.804847  \n",
              "282294       0.637659  \n",
              "282295       0.043602  \n",
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              "      <td>AAAKTPVIVV</td>\n",
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              "      <th>282293</th>\n",
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              "      <td>GHQDSDSDLSPPRNRPRHRSSDSDLSPPRRRQRTKSSDSDLSPPR</td>\n",
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              "      <th>282295</th>\n",
              "      <td>LPQRGQSNRGFHGFLPEDIKKEAARASRKICFVCKKKGAAINCQKD...</td>\n",
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              "      <td>APPIYQLNAPWLKGQERADLSNSLEEIYIQNIGESILYLWVEKIRD...</td>\n",
              "      <td>62</td>\n",
              "      <td>0</td>\n",
              "      <td>0.674268</td>\n",
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              "<p>282298 rows × 4 columns</p>\n",
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              "\n",
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            ]
          },
          "metadata": {},
          "execution_count": 13
        }
      ],
      "source": [
        "test_df = concat_neg_df(pos_test_df, model.protein_df)\n",
        "model.predict(test_df)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "Sc9zSL1S6zfG",
        "outputId": "1658832c-76dc-4b3b-ab51-0273ddc8a745",
        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Precision = 0.8812696811009239\n",
            "Recall = 0.7177450778964073\n",
            "False Positive Rate = 0.09669923272570125\n"
          ]
        }
      ],
      "source": [
        "prob=0.7\n",
        "print(\"Precision =\", test_df[test_df.HLA_prob_pred>prob]['HLA'].mean())\n",
        "print(\"Recall =\", test_df[test_df.HLA_prob_pred>prob]['HLA'].sum()/len(test_df)*2)\n",
        "print(\"False Positive Rate =\", 1-(1-test_df[test_df.HLA_prob_pred<prob]['HLA']).sum()/len(test_df)*2)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "o5qapWNl6zfG"
      },
      "source": [
        "## Get all sequences with >0.7 predicted probabilities"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "g_D4R1qU6zfG",
        "outputId": "141d0a90-8be2-400d-f334-53316eb09208",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 515
        }
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "/usr/local/lib/python3.8/dist-packages/pydivsufsort/divsufsort.py:76: UserWarning: converting str argument uses more memory\n",
            "  warnings.warn(\"converting str argument uses more memory\")\n",
            "/usr/local/lib/python3.8/dist-packages/alphabase/protein/lcp_digest.py:7: UserWarning: converting str argument uses more memory\n",
            "  lcp_array = kasai(cat_prot, suffix_array)\n",
            "100%|██████████| 72/72 [1:00:02<00:00, 50.03s/it]\n"
          ]
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "         start_pos  end_pos  nAA  HLA_prob_pred        sequence\n",
              "0          4709940  4709948    8       0.791963        ASVDYIRK\n",
              "1          4709941  4709949    8       0.864838        SVDYIRKL\n",
              "2          4709943  4709951    8       0.875017        DYIRKLQR\n",
              "3          4709951  4709959    8       0.884643        EQQRAKEL\n",
              "4          4709958  4709966    8       0.834065        LENRQKKL\n",
              "...            ...      ...  ...            ...             ...\n",
              "6025465    2563511  2563525   14       0.947904  SRLDTPLGQTLIRM\n",
              "6025466     535531   535545   14       0.814335  SADEFGILETDVTF\n",
              "6025467    4323141  4323155   14       0.712731  MGMMNNPNPYGSPY\n",
              "6025468     984573   984587   14       0.777980  AKAVYDFKAQTSKE\n",
              "6025469    8588867  8588881   14       0.708901  DNLVAIFDVNRLGH\n",
              "\n",
              "[6025470 rows x 5 columns]"
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              "      <th>1</th>\n",
              "      <td>4709941</td>\n",
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              "      <td>SRLDTPLGQTLIRM</td>\n",
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              "      <td>535531</td>\n",
              "      <td>535545</td>\n",
              "      <td>14</td>\n",
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              "      <td>SADEFGILETDVTF</td>\n",
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              "      <td>984573</td>\n",
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              "      <td>8588867</td>\n",
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              "      <td>0.708901</td>\n",
              "      <td>DNLVAIFDVNRLGH</td>\n",
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              "<p>6025470 rows × 5 columns</p>\n",
              "</div>\n",
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              "    .colab-df-convert {\n",
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              "    [theme=dark] .colab-df-convert {\n",
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              "\n",
              "    [theme=dark] .colab-df-convert:hover {\n",
              "      background-color: #434B5C;\n",
              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
              "      fill: #FFFFFF;\n",
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              "  </style>\n",
              "\n",
              "      <script>\n",
              "        const buttonEl =\n",
              "          document.querySelector('#df-20ab2382-5809-40b8-84df-2c4753dad8d3 button.colab-df-convert');\n",
              "        buttonEl.style.display =\n",
              "          google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
              "        async function convertToInteractive(key) {\n",
              "          const element = document.querySelector('#df-20ab2382-5809-40b8-84df-2c4753dad8d3');\n",
              "          const dataTable =\n",
              "            await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                     [key], {});\n",
              "          if (!dataTable) return;\n",
              "\n",
              "          const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "            '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "            + ' to learn more about interactive tables.';\n",
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              "  </div>\n",
              "  "
            ]
          },
          "metadata": {},
          "execution_count": 15
        }
      ],
      "source": [
        "hla_df = model.predict_from_proteins(prob_threshold=prob)\n",
        "hla_df"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "F35HJxte6zfG"
      },
      "outputs": [],
      "source": [
        "# hla_df.to_csv('Predicted_HLA.tsv',index=False, sep=\"\\t\")"
      ]
    }
  ],
  "metadata": {
    "kernelspec": {
      "display_name": "Python 3.8.3 ('base')",
      "language": "python",
      "name": "python3"
    },
    "language_info": {
      "codemirror_mode": {
        "name": "ipython",
        "version": 3
      },
      "file_extension": ".py",
      "mimetype": "text/x-python",
      "name": "python",
      "nbconvert_exporter": "python",
      "pygments_lexer": "ipython3",
      "version": "3.8.3"
    },
    "vscode": {
      "interpreter": {
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      }
    },
    "colab": {
      "provenance": [],
      "include_colab_link": true
    },
    "accelerator": "GPU",
    "gpuClass": "standard"
  },
  "nbformat": 4,
  "nbformat_minor": 0
}