Notebooks/Drug_discovery_Guilherme_Prado.ipynb

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      "name": "Drug_discovery - Guilherme Prado.ipynb",
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      "cell_type": "markdown",
      "metadata": {
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      "source": [
        "<a href=\"https://colab.research.google.com/github/guilherme-prado/imersao-dados-desafio-final/blob/main/Notebooks/Drug_discovery_Guilherme_Prado.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "8fAFTWtASt0t"
      },
      "source": [
        "# **Drug Discovery - Predição de mecanismos de ação possivelmente relacionados a tratamento de ansiedade, depressão e distúrbios alimentares.**"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "zB4UU3kDNQaP"
      },
      "source": [
        "#Sumário\n",
        "1. Introdução\n",
        "2. Observações relevantes\n",
        "3. Preparação dos dados\n",
        "4. Análise dos dados\n",
        "5. Introdução de modelos de predição\n",
        "6. Conclusão\n",
        "7. Referências"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "B-MwcAsxS2pB"
      },
      "source": [
        "#**1. Introdução**\n",
        "\n",
        "> Nesse estudo utilizando uma base de dados de mecanismos de ação e suas respectivas expressões gênicas dado uma lista de compostos aplicados em 2 dosagens diferentes em tempos de 24, 48 e 72 horas, desejo checar a acurácia de previsão para mecanismos de ação relacionados com tratamento de ansiedade, depressão e distúrbios alimentares.\n",
        "\n",
        "> Minha análise, apesar de direcionada aos mecanismos de ação descritos em artigos da Wikipedia sobre os tratamentos listados acima, será bem ingênua pois posso estar excluindo fatores extremamente relevantes para uma análise melhor direcionada.\n",
        "\n",
        "> Dito isso, minha análise será baseada nos seguintes mecanismos de ação:\n",
        "* serotonin_receptor_agonist\n",
        "* serotonin_receptor_antagonist\n",
        "\n",
        "\n",
        "\n",
        "\n",
        "\n",
        "\n",
        "\n"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "M6G1i0u9F4Qu"
      },
      "source": [
        "#**2. Observações relevantes**\n",
        "> **Mecanismos de ativação** serão chamados de **mda**\n",
        "\n",
        "> A viabilidade celular não será considerada nesse processo de análise. Um estudo futuro poderia analisar a viabilidade celular e considerar as amostras com maior viabilidade celular.\n",
        "\n"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "Lnj76-5Da9Qc"
      },
      "source": [
        "#**3. Preparação dos dados**\n"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "W5R8DSxdbLTu"
      },
      "source": [
        "### Passo 1: Carregando bibliotecas e dados que serão utilizados na análise.\n",
        "\n"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "hV2ZFe-HbZA4"
      },
      "source": [
        "import numpy as np\n",
        "import pandas as pd\n",
        "import matplotlib.pyplot as plt \n",
        "import seaborn as sns\n",
        "import re\n",
        "\n",
        "sns.set()\n",
        "\n",
        "url_experimento = 'https://github.com/guilherme-prado/imersao-dados-desafio-final/blob/main/Dados/dados_experimentos.zip?raw=true'\n",
        "url_resultados = 'https://github.com/guilherme-prado/imersao-dados-desafio-final/blob/main/Dados/dados_resultados.csv?raw=true'\n",
        "\n",
        "experimento = pd.read_csv(url_experimento, compression = 'zip')\n",
        "resultados = pd.read_csv(url_resultados)\n"
      ],
      "execution_count": 155,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "-dZUuMtKl-j2"
      },
      "source": [
        "### Passo 2: Verificando que os dados foram carregados corretamente\n",
        "\n"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 253
        },
        "id": "SyUv8LxPmJf2",
        "outputId": "0af3d69a-9ff2-41ba-98be-af5bbafcf17b"
      },
      "source": [
        "experimento.head()"
      ],
      "execution_count": 156,
      "outputs": [
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              "      <td>-2.3520</td>\n",
              "      <td>2.1200</td>\n",
              "      <td>-1.1580</td>\n",
              "      <td>-0.7191</td>\n",
              "      <td>-0.8004</td>\n",
              "      <td>-1.4670</td>\n",
              "      <td>-0.0107</td>\n",
              "      <td>-0.8995</td>\n",
              "      <td>0.2406</td>\n",
              "      <td>-0.2479</td>\n",
              "      <td>-1.0890</td>\n",
              "      <td>-0.7575</td>\n",
              "      <td>0.0881</td>\n",
              "      <td>-2.7370</td>\n",
              "      <td>0.8745</td>\n",
              "      <td>0.5787</td>\n",
              "      <td>...</td>\n",
              "      <td>-2.1220</td>\n",
              "      <td>-0.3752</td>\n",
              "      <td>-2.3820</td>\n",
              "      <td>-3.7350</td>\n",
              "      <td>-2.9740</td>\n",
              "      <td>-1.4930</td>\n",
              "      <td>-1.6600</td>\n",
              "      <td>-3.1660</td>\n",
              "      <td>0.2816</td>\n",
              "      <td>-0.2990</td>\n",
              "      <td>-1.1870</td>\n",
              "      <td>-0.5044</td>\n",
              "      <td>-1.7750</td>\n",
              "      <td>-1.6120</td>\n",
              "      <td>-0.9215</td>\n",
              "      <td>-1.0810</td>\n",
              "      <td>-3.0520</td>\n",
              "      <td>-3.4470</td>\n",
              "      <td>-2.7740</td>\n",
              "      <td>-1.8460</td>\n",
              "      <td>-0.5568</td>\n",
              "      <td>-3.3960</td>\n",
              "      <td>-2.9510</td>\n",
              "      <td>-1.1550</td>\n",
              "      <td>-3.2620</td>\n",
              "      <td>-1.5390</td>\n",
              "      <td>-2.4600</td>\n",
              "      <td>-0.9417</td>\n",
              "      <td>-1.5550</td>\n",
              "      <td>0.2431</td>\n",
              "      <td>-2.0990</td>\n",
              "      <td>-0.6441</td>\n",
              "      <td>-5.6300</td>\n",
              "      <td>-1.3780</td>\n",
              "      <td>-0.8632</td>\n",
              "      <td>-1.2880</td>\n",
              "      <td>-1.6210</td>\n",
              "      <td>-0.8784</td>\n",
              "      <td>-0.3876</td>\n",
              "      <td>-0.8154</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>id_001626bd3</td>\n",
              "      <td>com_droga</td>\n",
              "      <td>72</td>\n",
              "      <td>D2</td>\n",
              "      <td>7cbed3131</td>\n",
              "      <td>-0.3254</td>\n",
              "      <td>-0.4009</td>\n",
              "      <td>0.9700</td>\n",
              "      <td>0.6919</td>\n",
              "      <td>1.4180</td>\n",
              "      <td>-0.8244</td>\n",
              "      <td>-0.2800</td>\n",
              "      <td>-0.1498</td>\n",
              "      <td>-0.8789</td>\n",
              "      <td>0.8630</td>\n",
              "      <td>-0.2219</td>\n",
              "      <td>-0.5121</td>\n",
              "      <td>-0.9577</td>\n",
              "      <td>1.1750</td>\n",
              "      <td>0.2042</td>\n",
              "      <td>0.1970</td>\n",
              "      <td>0.1244</td>\n",
              "      <td>-1.7090</td>\n",
              "      <td>-0.3543</td>\n",
              "      <td>-0.5160</td>\n",
              "      <td>-0.3330</td>\n",
              "      <td>-0.2685</td>\n",
              "      <td>0.7649</td>\n",
              "      <td>0.2057</td>\n",
              "      <td>1.3720</td>\n",
              "      <td>0.6835</td>\n",
              "      <td>0.8056</td>\n",
              "      <td>-0.3754</td>\n",
              "      <td>-1.2090</td>\n",
              "      <td>0.2965</td>\n",
              "      <td>-0.0712</td>\n",
              "      <td>0.6389</td>\n",
              "      <td>0.6674</td>\n",
              "      <td>-0.0783</td>\n",
              "      <td>1.1740</td>\n",
              "      <td>...</td>\n",
              "      <td>-0.2274</td>\n",
              "      <td>0.3215</td>\n",
              "      <td>0.1535</td>\n",
              "      <td>-0.4640</td>\n",
              "      <td>-0.5943</td>\n",
              "      <td>0.3973</td>\n",
              "      <td>0.1500</td>\n",
              "      <td>0.5178</td>\n",
              "      <td>0.5159</td>\n",
              "      <td>0.6091</td>\n",
              "      <td>0.1813</td>\n",
              "      <td>-0.4249</td>\n",
              "      <td>0.7832</td>\n",
              "      <td>0.6529</td>\n",
              "      <td>0.5648</td>\n",
              "      <td>0.4817</td>\n",
              "      <td>0.0587</td>\n",
              "      <td>0.5303</td>\n",
              "      <td>0.6376</td>\n",
              "      <td>-0.3966</td>\n",
              "      <td>-1.4950</td>\n",
              "      <td>-0.9625</td>\n",
              "      <td>-0.0541</td>\n",
              "      <td>0.6273</td>\n",
              "      <td>0.4563</td>\n",
              "      <td>0.0698</td>\n",
              "      <td>0.8134</td>\n",
              "      <td>0.1924</td>\n",
              "      <td>0.6054</td>\n",
              "      <td>-0.1824</td>\n",
              "      <td>0.0042</td>\n",
              "      <td>0.0048</td>\n",
              "      <td>0.6670</td>\n",
              "      <td>1.0690</td>\n",
              "      <td>0.5523</td>\n",
              "      <td>-0.3031</td>\n",
              "      <td>0.1094</td>\n",
              "      <td>0.2885</td>\n",
              "      <td>-0.3786</td>\n",
              "      <td>0.7125</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>5 rows × 877 columns</p>\n",
              "</div>"
            ],
            "text/plain": [
              "             id tratamento  tempo dose  ...    c-96    c-97    c-98    c-99\n",
              "0  id_000644bb2  com_droga     24   D1  ... -0.3981  0.2139  0.3801  0.4176\n",
              "1  id_000779bfc  com_droga     72   D1  ...  0.1522  0.1241  0.6077  0.7371\n",
              "2  id_000a6266a  com_droga     48   D1  ... -0.6417 -0.2187 -1.4080  0.6931\n",
              "3  id_0015fd391  com_droga     48   D1  ... -1.6210 -0.8784 -0.3876 -0.8154\n",
              "4  id_001626bd3  com_droga     72   D2  ...  0.1094  0.2885 -0.3786  0.7125\n",
              "\n",
              "[5 rows x 877 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 156
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 270
        },
        "id": "rrLrpBAEmJUq",
        "outputId": "39f2185c-7c91-4359-d7ab-d2d403b40947"
      },
      "source": [
        "resultados.head()"
      ],
      "execution_count": 157,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
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              "      <th>id</th>\n",
              "      <th>5-alpha_reductase_inhibitor</th>\n",
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              "      <th>atp_synthase_inhibitor</th>\n",
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              "      <th>thrombin_inhibitor</th>\n",
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              "      <th>tropomyosin_receptor_kinase_inhibitor</th>\n",
              "      <th>trpv_agonist</th>\n",
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              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>5 rows × 207 columns</p>\n",
              "</div>"
            ],
            "text/plain": [
              "             id  ...  wnt_inhibitor\n",
              "0  id_000644bb2  ...              0\n",
              "1  id_000779bfc  ...              0\n",
              "2  id_000a6266a  ...              0\n",
              "3  id_0015fd391  ...              0\n",
              "4  id_001626bd3  ...              0\n",
              "\n",
              "[5 rows x 207 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 157
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "t82X4JjBmYT-"
      },
      "source": [
        "###Passo 3: Reformatando colunas que utilizam hífen."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "cHYaIJyXmXNT"
      },
      "source": [
        "experimento.rename(columns=lambda x: x.replace('-', '_'), inplace=True)\n",
        "resultados.rename(columns=lambda x: x.replace('-', '_'), inplace=True)"
      ],
      "execution_count": 158,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "iGPYzmT9nfxu"
      },
      "source": [
        "#**4. Análise dos dados**"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "R9C8erBdq64i"
      },
      "source": [
        "### Combinando dados da tabela experimento e resultado.\n",
        "> Para iniciar a análise dos dados, eu vou criar uma tabela com dados combinados que contenham os **mdas relevantes** para esse estudo junto de suas expressões gênicas e viabilidade celular."
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "4cwiuIlpt-gA"
      },
      "source": [
        "### Buscando todos os mdas relacionados com **serotonina**."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "lkb0JOQ3t57X",
        "outputId": "ca511697-f4c3-4e6d-c080-047e43623aaa"
      },
      "source": [
        "r = re.compile(\".*serotonin*.\")\n",
        "m_ativ_ser = list(filter(r.match, resultados.columns))\n",
        "m_ativ_ser"
      ],
      "execution_count": 159,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "['serotonin_receptor_agonist',\n",
              " 'serotonin_receptor_antagonist',\n",
              " 'serotonin_reuptake_inhibitor']"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 159
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "5wRbSl_00SGp"
      },
      "source": [
        "### Abaixo temos um total de ativações dos mdas selecionados.\n",
        "\n",
        "> Vale notar aqui que nas combinações de ativações de mdas relacionados com serotonina, uma combinação chama a atenção pela sua característica contraditória.\n",
        "> `serotonin_receptor_agonist` e `serotonin_receptor_antagonist` foram ativados 7 vezes na mesma amostra."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "ceLed0ugLFgL",
        "outputId": "0e7e4f68-89e5-44df-a85d-833e802e7b7e"
      },
      "source": [
        "# Total de combinações de de ativações de mdas relacionados com serotonina.\n",
        "resultados[m_ativ_ser].value_counts()"
      ],
      "execution_count": 160,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "serotonin_receptor_agonist  serotonin_receptor_antagonist  serotonin_reuptake_inhibitor\n",
              "0                           0                              0                               23143\n",
              "                            1                              0                                 391\n",
              "1                           0                              0                                 229\n",
              "0                           0                              1                                  38\n",
              "1                           1                              0                                   7\n",
              "0                           1                              1                                   6\n",
              "dtype: int64"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 160
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "EYY1XrQgznvZ",
        "outputId": "758aa2e7-b94f-4ed3-8dd2-c769b8c9b5ca"
      },
      "source": [
        "# Total de ativações por mda relacionado com serotonina\n",
        "series_total = resultados[m_ativ_ser].sum(axis=0)\n",
        "series_total"
      ],
      "execution_count": 161,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "serotonin_receptor_agonist       236\n",
              "serotonin_receptor_antagonist    404\n",
              "serotonin_reuptake_inhibitor      44\n",
              "dtype: int64"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 161
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "J1KmK4UO6G4O"
      },
      "source": [
        "# Criando um dataframe com o total de ativações por mda relacionado com serotonina\n",
        "serot_total = pd.DataFrame({'mdas':series_total.index, 'total':series_total.values})"
      ],
      "execution_count": 162,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 649
        },
        "id": "vLuT14CU2RPb",
        "outputId": "48f14ae7-1512-4091-be5a-f9dae2262b94"
      },
      "source": [
        "plot = sns.catplot(x='mdas', y='total', kind = 'bar', data=serot_total)\n",
        "plot.set_xlabels('Mecanismos de ativação',fontsize=20)\n",
        "plot.set_ylabels('Ativações',fontsize=20)\n",
        "plot.fig.suptitle('Número de ativações',fontsize=40)\n",
        "plot.fig.subplots_adjust(top=0.9)\n",
        "plot.fig.set_figheight(9)\n",
        "plot.fig.set_figwidth(10)"
      ],
      "execution_count": 163,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 720x648 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "rI3FrFmjFFMy"
      },
      "source": [
        "### Combinando os resultados dos mdas selecionados com os dados experimentais."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 439
        },
        "id": "33_QHhPYhOt0",
        "outputId": "2f26e5ab-476c-4210-95a4-a78f3b3b381c"
      },
      "source": [
        "experimento[m_ativ_ser] = resultados[m_ativ_ser]\n",
        "experimento"
      ],
      "execution_count": 164,
      "outputs": [
        {
          "output_type": "execute_result",
          "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>tratamento</th>\n",
              "      <th>tempo</th>\n",
              "      <th>dose</th>\n",
              "      <th>droga</th>\n",
              "      <th>g_0</th>\n",
              "      <th>g_1</th>\n",
              "      <th>g_2</th>\n",
              "      <th>g_3</th>\n",
              "      <th>g_4</th>\n",
              "      <th>g_5</th>\n",
              "      <th>g_6</th>\n",
              "      <th>g_7</th>\n",
              "      <th>g_8</th>\n",
              "      <th>g_9</th>\n",
              "      <th>g_10</th>\n",
              "      <th>g_11</th>\n",
              "      <th>g_12</th>\n",
              "      <th>g_13</th>\n",
              "      <th>g_14</th>\n",
              "      <th>g_15</th>\n",
              "      <th>g_16</th>\n",
              "      <th>g_17</th>\n",
              "      <th>g_18</th>\n",
              "      <th>g_19</th>\n",
              "      <th>g_20</th>\n",
              "      <th>g_21</th>\n",
              "      <th>g_22</th>\n",
              "      <th>g_23</th>\n",
              "      <th>g_24</th>\n",
              "      <th>g_25</th>\n",
              "      <th>g_26</th>\n",
              "      <th>g_27</th>\n",
              "      <th>g_28</th>\n",
              "      <th>g_29</th>\n",
              "      <th>g_30</th>\n",
              "      <th>g_31</th>\n",
              "      <th>g_32</th>\n",
              "      <th>g_33</th>\n",
              "      <th>g_34</th>\n",
              "      <th>...</th>\n",
              "      <th>c_63</th>\n",
              "      <th>c_64</th>\n",
              "      <th>c_65</th>\n",
              "      <th>c_66</th>\n",
              "      <th>c_67</th>\n",
              "      <th>c_68</th>\n",
              "      <th>c_69</th>\n",
              "      <th>c_70</th>\n",
              "      <th>c_71</th>\n",
              "      <th>c_72</th>\n",
              "      <th>c_73</th>\n",
              "      <th>c_74</th>\n",
              "      <th>c_75</th>\n",
              "      <th>c_76</th>\n",
              "      <th>c_77</th>\n",
              "      <th>c_78</th>\n",
              "      <th>c_79</th>\n",
              "      <th>c_80</th>\n",
              "      <th>c_81</th>\n",
              "      <th>c_82</th>\n",
              "      <th>c_83</th>\n",
              "      <th>c_84</th>\n",
              "      <th>c_85</th>\n",
              "      <th>c_86</th>\n",
              "      <th>c_87</th>\n",
              "      <th>c_88</th>\n",
              "      <th>c_89</th>\n",
              "      <th>c_90</th>\n",
              "      <th>c_91</th>\n",
              "      <th>c_92</th>\n",
              "      <th>c_93</th>\n",
              "      <th>c_94</th>\n",
              "      <th>c_95</th>\n",
              "      <th>c_96</th>\n",
              "      <th>c_97</th>\n",
              "      <th>c_98</th>\n",
              "      <th>c_99</th>\n",
              "      <th>serotonin_receptor_agonist</th>\n",
              "      <th>serotonin_receptor_antagonist</th>\n",
              "      <th>serotonin_reuptake_inhibitor</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>id_000644bb2</td>\n",
              "      <td>com_droga</td>\n",
              "      <td>24</td>\n",
              "      <td>D1</td>\n",
              "      <td>b68db1d53</td>\n",
              "      <td>1.0620</td>\n",
              "      <td>0.5577</td>\n",
              "      <td>-0.2479</td>\n",
              "      <td>-0.6208</td>\n",
              "      <td>-0.1944</td>\n",
              "      <td>-1.0120</td>\n",
              "      <td>-1.0220</td>\n",
              "      <td>-0.0326</td>\n",
              "      <td>0.5548</td>\n",
              "      <td>-0.0921</td>\n",
              "      <td>1.1830</td>\n",
              "      <td>0.1530</td>\n",
              "      <td>0.5574</td>\n",
              "      <td>-0.4015</td>\n",
              "      <td>0.1789</td>\n",
              "      <td>-0.6528</td>\n",
              "      <td>-0.7969</td>\n",
              "      <td>0.6342</td>\n",
              "      <td>0.1778</td>\n",
              "      <td>-0.3694</td>\n",
              "      <td>-0.5688</td>\n",
              "      <td>-1.1360</td>\n",
              "      <td>-1.1880</td>\n",
              "      <td>0.6940</td>\n",
              "      <td>0.4393</td>\n",
              "      <td>0.2664</td>\n",
              "      <td>0.1907</td>\n",
              "      <td>0.1628</td>\n",
              "      <td>-0.2853</td>\n",
              "      <td>0.5819</td>\n",
              "      <td>0.2934</td>\n",
              "      <td>-0.5584</td>\n",
              "      <td>-0.0916</td>\n",
              "      <td>-0.3010</td>\n",
              "      <td>-0.1537</td>\n",
              "      <td>...</td>\n",
              "      <td>0.8427</td>\n",
              "      <td>0.1042</td>\n",
              "      <td>0.1403</td>\n",
              "      <td>0.1758</td>\n",
              "      <td>1.2570</td>\n",
              "      <td>-0.5979</td>\n",
              "      <td>1.2250</td>\n",
              "      <td>-0.0553</td>\n",
              "      <td>0.7351</td>\n",
              "      <td>0.5810</td>\n",
              "      <td>0.9590</td>\n",
              "      <td>0.2427</td>\n",
              "      <td>0.0495</td>\n",
              "      <td>0.4141</td>\n",
              "      <td>0.8432</td>\n",
              "      <td>0.6162</td>\n",
              "      <td>-0.7318</td>\n",
              "      <td>1.2120</td>\n",
              "      <td>0.6362</td>\n",
              "      <td>-0.4427</td>\n",
              "      <td>0.1288</td>\n",
              "      <td>1.4840</td>\n",
              "      <td>0.1799</td>\n",
              "      <td>0.5367</td>\n",
              "      <td>-0.1111</td>\n",
              "      <td>-1.0120</td>\n",
              "      <td>0.6685</td>\n",
              "      <td>0.2862</td>\n",
              "      <td>0.2584</td>\n",
              "      <td>0.8076</td>\n",
              "      <td>0.5523</td>\n",
              "      <td>-0.1912</td>\n",
              "      <td>0.6584</td>\n",
              "      <td>-0.3981</td>\n",
              "      <td>0.2139</td>\n",
              "      <td>0.3801</td>\n",
              "      <td>0.4176</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>id_000779bfc</td>\n",
              "      <td>com_droga</td>\n",
              "      <td>72</td>\n",
              "      <td>D1</td>\n",
              "      <td>df89a8e5a</td>\n",
              "      <td>0.0743</td>\n",
              "      <td>0.4087</td>\n",
              "      <td>0.2991</td>\n",
              "      <td>0.0604</td>\n",
              "      <td>1.0190</td>\n",
              "      <td>0.5207</td>\n",
              "      <td>0.2341</td>\n",
              "      <td>0.3372</td>\n",
              "      <td>-0.4047</td>\n",
              "      <td>0.8507</td>\n",
              "      <td>-1.1520</td>\n",
              "      <td>-0.4201</td>\n",
              "      <td>-0.0958</td>\n",
              "      <td>0.4590</td>\n",
              "      <td>0.0803</td>\n",
              "      <td>0.2250</td>\n",
              "      <td>0.5293</td>\n",
              "      <td>0.2839</td>\n",
              "      <td>-0.3494</td>\n",
              "      <td>0.2883</td>\n",
              "      <td>0.9449</td>\n",
              "      <td>-0.1646</td>\n",
              "      <td>-0.2657</td>\n",
              "      <td>-0.3372</td>\n",
              "      <td>0.3135</td>\n",
              "      <td>-0.4316</td>\n",
              "      <td>0.4773</td>\n",
              "      <td>0.2075</td>\n",
              "      <td>-0.4216</td>\n",
              "      <td>-0.1161</td>\n",
              "      <td>-0.0499</td>\n",
              "      <td>-0.2627</td>\n",
              "      <td>0.9959</td>\n",
              "      <td>-0.2483</td>\n",
              "      <td>0.2655</td>\n",
              "      <td>...</td>\n",
              "      <td>0.7099</td>\n",
              "      <td>0.2912</td>\n",
              "      <td>0.4151</td>\n",
              "      <td>-0.2840</td>\n",
              "      <td>-0.3104</td>\n",
              "      <td>-0.6373</td>\n",
              "      <td>0.2887</td>\n",
              "      <td>-0.0765</td>\n",
              "      <td>0.2539</td>\n",
              "      <td>0.4443</td>\n",
              "      <td>0.5932</td>\n",
              "      <td>0.2031</td>\n",
              "      <td>0.7639</td>\n",
              "      <td>0.5499</td>\n",
              "      <td>-0.3322</td>\n",
              "      <td>-0.0977</td>\n",
              "      <td>0.4329</td>\n",
              "      <td>-0.2782</td>\n",
              "      <td>0.7827</td>\n",
              "      <td>0.5934</td>\n",
              "      <td>0.3402</td>\n",
              "      <td>0.1499</td>\n",
              "      <td>0.4420</td>\n",
              "      <td>0.9366</td>\n",
              "      <td>0.8193</td>\n",
              "      <td>-0.4236</td>\n",
              "      <td>0.3192</td>\n",
              "      <td>-0.4265</td>\n",
              "      <td>0.7543</td>\n",
              "      <td>0.4708</td>\n",
              "      <td>0.0230</td>\n",
              "      <td>0.2957</td>\n",
              "      <td>0.4899</td>\n",
              "      <td>0.1522</td>\n",
              "      <td>0.1241</td>\n",
              "      <td>0.6077</td>\n",
              "      <td>0.7371</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>id_000a6266a</td>\n",
              "      <td>com_droga</td>\n",
              "      <td>48</td>\n",
              "      <td>D1</td>\n",
              "      <td>18bb41b2c</td>\n",
              "      <td>0.6280</td>\n",
              "      <td>0.5817</td>\n",
              "      <td>1.5540</td>\n",
              "      <td>-0.0764</td>\n",
              "      <td>-0.0323</td>\n",
              "      <td>1.2390</td>\n",
              "      <td>0.1715</td>\n",
              "      <td>0.2155</td>\n",
              "      <td>0.0065</td>\n",
              "      <td>1.2300</td>\n",
              "      <td>-0.4797</td>\n",
              "      <td>-0.5631</td>\n",
              "      <td>-0.0366</td>\n",
              "      <td>-1.8300</td>\n",
              "      <td>0.6057</td>\n",
              "      <td>-0.3278</td>\n",
              "      <td>0.6042</td>\n",
              "      <td>-0.3075</td>\n",
              "      <td>-0.1147</td>\n",
              "      <td>-0.0570</td>\n",
              "      <td>-0.0799</td>\n",
              "      <td>-0.8181</td>\n",
              "      <td>-1.5320</td>\n",
              "      <td>0.2307</td>\n",
              "      <td>0.4901</td>\n",
              "      <td>0.4780</td>\n",
              "      <td>-1.3970</td>\n",
              "      <td>4.6240</td>\n",
              "      <td>-0.0437</td>\n",
              "      <td>1.2870</td>\n",
              "      <td>-1.8530</td>\n",
              "      <td>0.6069</td>\n",
              "      <td>0.4290</td>\n",
              "      <td>0.1783</td>\n",
              "      <td>0.0018</td>\n",
              "      <td>...</td>\n",
              "      <td>0.1894</td>\n",
              "      <td>-0.0014</td>\n",
              "      <td>-2.3640</td>\n",
              "      <td>-0.4682</td>\n",
              "      <td>0.1210</td>\n",
              "      <td>-0.5177</td>\n",
              "      <td>-0.0604</td>\n",
              "      <td>0.1682</td>\n",
              "      <td>-0.4436</td>\n",
              "      <td>0.4963</td>\n",
              "      <td>0.1363</td>\n",
              "      <td>0.3335</td>\n",
              "      <td>0.9760</td>\n",
              "      <td>-0.0427</td>\n",
              "      <td>-0.1235</td>\n",
              "      <td>0.0959</td>\n",
              "      <td>0.0690</td>\n",
              "      <td>-0.9416</td>\n",
              "      <td>-0.7548</td>\n",
              "      <td>-0.1109</td>\n",
              "      <td>-0.6272</td>\n",
              "      <td>0.3019</td>\n",
              "      <td>0.1172</td>\n",
              "      <td>0.1093</td>\n",
              "      <td>-0.3113</td>\n",
              "      <td>0.3019</td>\n",
              "      <td>-0.0873</td>\n",
              "      <td>-0.7250</td>\n",
              "      <td>-0.6297</td>\n",
              "      <td>0.6103</td>\n",
              "      <td>0.0223</td>\n",
              "      <td>-1.3240</td>\n",
              "      <td>-0.3174</td>\n",
              "      <td>-0.6417</td>\n",
              "      <td>-0.2187</td>\n",
              "      <td>-1.4080</td>\n",
              "      <td>0.6931</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>id_0015fd391</td>\n",
              "      <td>com_droga</td>\n",
              "      <td>48</td>\n",
              "      <td>D1</td>\n",
              "      <td>8c7f86626</td>\n",
              "      <td>-0.5138</td>\n",
              "      <td>-0.2491</td>\n",
              "      <td>-0.2656</td>\n",
              "      <td>0.5288</td>\n",
              "      <td>4.0620</td>\n",
              "      <td>-0.8095</td>\n",
              "      <td>-1.9590</td>\n",
              "      <td>0.1792</td>\n",
              "      <td>-0.1321</td>\n",
              "      <td>-1.0600</td>\n",
              "      <td>-0.8269</td>\n",
              "      <td>-0.3584</td>\n",
              "      <td>-0.8511</td>\n",
              "      <td>-0.5844</td>\n",
              "      <td>-2.5690</td>\n",
              "      <td>0.8183</td>\n",
              "      <td>-0.0532</td>\n",
              "      <td>-0.8554</td>\n",
              "      <td>0.1160</td>\n",
              "      <td>-2.3520</td>\n",
              "      <td>2.1200</td>\n",
              "      <td>-1.1580</td>\n",
              "      <td>-0.7191</td>\n",
              "      <td>-0.8004</td>\n",
              "      <td>-1.4670</td>\n",
              "      <td>-0.0107</td>\n",
              "      <td>-0.8995</td>\n",
              "      <td>0.2406</td>\n",
              "      <td>-0.2479</td>\n",
              "      <td>-1.0890</td>\n",
              "      <td>-0.7575</td>\n",
              "      <td>0.0881</td>\n",
              "      <td>-2.7370</td>\n",
              "      <td>0.8745</td>\n",
              "      <td>0.5787</td>\n",
              "      <td>...</td>\n",
              "      <td>-3.7350</td>\n",
              "      <td>-2.9740</td>\n",
              "      <td>-1.4930</td>\n",
              "      <td>-1.6600</td>\n",
              "      <td>-3.1660</td>\n",
              "      <td>0.2816</td>\n",
              "      <td>-0.2990</td>\n",
              "      <td>-1.1870</td>\n",
              "      <td>-0.5044</td>\n",
              "      <td>-1.7750</td>\n",
              "      <td>-1.6120</td>\n",
              "      <td>-0.9215</td>\n",
              "      <td>-1.0810</td>\n",
              "      <td>-3.0520</td>\n",
              "      <td>-3.4470</td>\n",
              "      <td>-2.7740</td>\n",
              "      <td>-1.8460</td>\n",
              "      <td>-0.5568</td>\n",
              "      <td>-3.3960</td>\n",
              "      <td>-2.9510</td>\n",
              "      <td>-1.1550</td>\n",
              "      <td>-3.2620</td>\n",
              "      <td>-1.5390</td>\n",
              "      <td>-2.4600</td>\n",
              "      <td>-0.9417</td>\n",
              "      <td>-1.5550</td>\n",
              "      <td>0.2431</td>\n",
              "      <td>-2.0990</td>\n",
              "      <td>-0.6441</td>\n",
              "      <td>-5.6300</td>\n",
              "      <td>-1.3780</td>\n",
              "      <td>-0.8632</td>\n",
              "      <td>-1.2880</td>\n",
              "      <td>-1.6210</td>\n",
              "      <td>-0.8784</td>\n",
              "      <td>-0.3876</td>\n",
              "      <td>-0.8154</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>id_001626bd3</td>\n",
              "      <td>com_droga</td>\n",
              "      <td>72</td>\n",
              "      <td>D2</td>\n",
              "      <td>7cbed3131</td>\n",
              "      <td>-0.3254</td>\n",
              "      <td>-0.4009</td>\n",
              "      <td>0.9700</td>\n",
              "      <td>0.6919</td>\n",
              "      <td>1.4180</td>\n",
              "      <td>-0.8244</td>\n",
              "      <td>-0.2800</td>\n",
              "      <td>-0.1498</td>\n",
              "      <td>-0.8789</td>\n",
              "      <td>0.8630</td>\n",
              "      <td>-0.2219</td>\n",
              "      <td>-0.5121</td>\n",
              "      <td>-0.9577</td>\n",
              "      <td>1.1750</td>\n",
              "      <td>0.2042</td>\n",
              "      <td>0.1970</td>\n",
              "      <td>0.1244</td>\n",
              "      <td>-1.7090</td>\n",
              "      <td>-0.3543</td>\n",
              "      <td>-0.5160</td>\n",
              "      <td>-0.3330</td>\n",
              "      <td>-0.2685</td>\n",
              "      <td>0.7649</td>\n",
              "      <td>0.2057</td>\n",
              "      <td>1.3720</td>\n",
              "      <td>0.6835</td>\n",
              "      <td>0.8056</td>\n",
              "      <td>-0.3754</td>\n",
              "      <td>-1.2090</td>\n",
              "      <td>0.2965</td>\n",
              "      <td>-0.0712</td>\n",
              "      <td>0.6389</td>\n",
              "      <td>0.6674</td>\n",
              "      <td>-0.0783</td>\n",
              "      <td>1.1740</td>\n",
              "      <td>...</td>\n",
              "      <td>-0.4640</td>\n",
              "      <td>-0.5943</td>\n",
              "      <td>0.3973</td>\n",
              "      <td>0.1500</td>\n",
              "      <td>0.5178</td>\n",
              "      <td>0.5159</td>\n",
              "      <td>0.6091</td>\n",
              "      <td>0.1813</td>\n",
              "      <td>-0.4249</td>\n",
              "      <td>0.7832</td>\n",
              "      <td>0.6529</td>\n",
              "      <td>0.5648</td>\n",
              "      <td>0.4817</td>\n",
              "      <td>0.0587</td>\n",
              "      <td>0.5303</td>\n",
              "      <td>0.6376</td>\n",
              "      <td>-0.3966</td>\n",
              "      <td>-1.4950</td>\n",
              "      <td>-0.9625</td>\n",
              "      <td>-0.0541</td>\n",
              "      <td>0.6273</td>\n",
              "      <td>0.4563</td>\n",
              "      <td>0.0698</td>\n",
              "      <td>0.8134</td>\n",
              "      <td>0.1924</td>\n",
              "      <td>0.6054</td>\n",
              "      <td>-0.1824</td>\n",
              "      <td>0.0042</td>\n",
              "      <td>0.0048</td>\n",
              "      <td>0.6670</td>\n",
              "      <td>1.0690</td>\n",
              "      <td>0.5523</td>\n",
              "      <td>-0.3031</td>\n",
              "      <td>0.1094</td>\n",
              "      <td>0.2885</td>\n",
              "      <td>-0.3786</td>\n",
              "      <td>0.7125</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</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",
              "      <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",
              "      <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",
              "      <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>23809</th>\n",
              "      <td>id_fffb1ceed</td>\n",
              "      <td>com_droga</td>\n",
              "      <td>24</td>\n",
              "      <td>D2</td>\n",
              "      <td>df1d0a5a1</td>\n",
              "      <td>0.1394</td>\n",
              "      <td>-0.0636</td>\n",
              "      <td>-0.1112</td>\n",
              "      <td>-0.5080</td>\n",
              "      <td>-0.4713</td>\n",
              "      <td>0.7201</td>\n",
              "      <td>0.5773</td>\n",
              "      <td>0.3055</td>\n",
              "      <td>-0.4726</td>\n",
              "      <td>0.1269</td>\n",
              "      <td>0.2531</td>\n",
              "      <td>0.1730</td>\n",
              "      <td>-0.4532</td>\n",
              "      <td>-1.0790</td>\n",
              "      <td>0.2474</td>\n",
              "      <td>-0.4550</td>\n",
              "      <td>0.3588</td>\n",
              "      <td>0.1600</td>\n",
              "      <td>-0.7362</td>\n",
              "      <td>-0.1103</td>\n",
              "      <td>0.8550</td>\n",
              "      <td>-0.4139</td>\n",
              "      <td>0.5541</td>\n",
              "      <td>0.2310</td>\n",
              "      <td>-0.5573</td>\n",
              "      <td>-0.4397</td>\n",
              "      <td>-0.9260</td>\n",
              "      <td>-0.2424</td>\n",
              "      <td>-0.6686</td>\n",
              "      <td>0.2326</td>\n",
              "      <td>0.6456</td>\n",
              "      <td>0.0136</td>\n",
              "      <td>-0.5141</td>\n",
              "      <td>-0.6320</td>\n",
              "      <td>0.7166</td>\n",
              "      <td>...</td>\n",
              "      <td>0.4420</td>\n",
              "      <td>0.9641</td>\n",
              "      <td>-0.1846</td>\n",
              "      <td>-0.0014</td>\n",
              "      <td>0.3154</td>\n",
              "      <td>-0.2071</td>\n",
              "      <td>-0.6158</td>\n",
              "      <td>-0.2977</td>\n",
              "      <td>0.0992</td>\n",
              "      <td>0.6838</td>\n",
              "      <td>0.5259</td>\n",
              "      <td>0.7882</td>\n",
              "      <td>0.3119</td>\n",
              "      <td>-0.7697</td>\n",
              "      <td>0.2203</td>\n",
              "      <td>-1.0710</td>\n",
              "      <td>0.5979</td>\n",
              "      <td>0.0848</td>\n",
              "      <td>-0.2555</td>\n",
              "      <td>0.6293</td>\n",
              "      <td>1.1660</td>\n",
              "      <td>0.3329</td>\n",
              "      <td>0.2754</td>\n",
              "      <td>0.4108</td>\n",
              "      <td>-0.1252</td>\n",
              "      <td>-0.2340</td>\n",
              "      <td>0.2267</td>\n",
              "      <td>0.1969</td>\n",
              "      <td>0.0262</td>\n",
              "      <td>-0.8121</td>\n",
              "      <td>0.3434</td>\n",
              "      <td>0.5372</td>\n",
              "      <td>-0.3246</td>\n",
              "      <td>0.0631</td>\n",
              "      <td>0.9171</td>\n",
              "      <td>0.5258</td>\n",
              "      <td>0.4680</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>23810</th>\n",
              "      <td>id_fffb70c0c</td>\n",
              "      <td>com_droga</td>\n",
              "      <td>24</td>\n",
              "      <td>D2</td>\n",
              "      <td>ecf3b6b74</td>\n",
              "      <td>-1.3260</td>\n",
              "      <td>0.3478</td>\n",
              "      <td>-0.3743</td>\n",
              "      <td>0.9905</td>\n",
              "      <td>-0.7178</td>\n",
              "      <td>0.6621</td>\n",
              "      <td>-0.2252</td>\n",
              "      <td>-0.5565</td>\n",
              "      <td>0.5112</td>\n",
              "      <td>0.6727</td>\n",
              "      <td>-0.1851</td>\n",
              "      <td>2.8650</td>\n",
              "      <td>-0.2140</td>\n",
              "      <td>-0.6153</td>\n",
              "      <td>0.8362</td>\n",
              "      <td>0.5584</td>\n",
              "      <td>-0.2589</td>\n",
              "      <td>0.1292</td>\n",
              "      <td>0.0148</td>\n",
              "      <td>0.0949</td>\n",
              "      <td>-0.2182</td>\n",
              "      <td>-0.9235</td>\n",
              "      <td>0.0749</td>\n",
              "      <td>-1.5910</td>\n",
              "      <td>-0.8359</td>\n",
              "      <td>-0.9217</td>\n",
              "      <td>0.3013</td>\n",
              "      <td>0.1716</td>\n",
              "      <td>0.0880</td>\n",
              "      <td>0.1842</td>\n",
              "      <td>0.1835</td>\n",
              "      <td>0.5436</td>\n",
              "      <td>-0.0533</td>\n",
              "      <td>-0.0491</td>\n",
              "      <td>0.9543</td>\n",
              "      <td>...</td>\n",
              "      <td>0.8129</td>\n",
              "      <td>-0.1514</td>\n",
              "      <td>-0.4652</td>\n",
              "      <td>-0.7390</td>\n",
              "      <td>-1.3270</td>\n",
              "      <td>0.9925</td>\n",
              "      <td>1.0570</td>\n",
              "      <td>-0.3355</td>\n",
              "      <td>-0.2555</td>\n",
              "      <td>0.8219</td>\n",
              "      <td>0.6995</td>\n",
              "      <td>-0.2942</td>\n",
              "      <td>0.2408</td>\n",
              "      <td>-0.7781</td>\n",
              "      <td>-0.0929</td>\n",
              "      <td>-0.0329</td>\n",
              "      <td>0.0781</td>\n",
              "      <td>-1.4440</td>\n",
              "      <td>-0.0921</td>\n",
              "      <td>0.3188</td>\n",
              "      <td>-1.1080</td>\n",
              "      <td>0.4895</td>\n",
              "      <td>-0.2144</td>\n",
              "      <td>1.0960</td>\n",
              "      <td>0.3335</td>\n",
              "      <td>0.4444</td>\n",
              "      <td>-1.1130</td>\n",
              "      <td>0.4286</td>\n",
              "      <td>0.4426</td>\n",
              "      <td>0.0423</td>\n",
              "      <td>-0.3195</td>\n",
              "      <td>-0.8086</td>\n",
              "      <td>-0.9798</td>\n",
              "      <td>-0.2084</td>\n",
              "      <td>-0.1224</td>\n",
              "      <td>-0.2715</td>\n",
              "      <td>0.3689</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>23811</th>\n",
              "      <td>id_fffc1c3f4</td>\n",
              "      <td>com_controle</td>\n",
              "      <td>48</td>\n",
              "      <td>D2</td>\n",
              "      <td>cacb2b860</td>\n",
              "      <td>0.3942</td>\n",
              "      <td>0.3756</td>\n",
              "      <td>0.3109</td>\n",
              "      <td>-0.7389</td>\n",
              "      <td>0.5505</td>\n",
              "      <td>-0.0159</td>\n",
              "      <td>-0.2541</td>\n",
              "      <td>0.1745</td>\n",
              "      <td>-0.0340</td>\n",
              "      <td>0.4865</td>\n",
              "      <td>-0.1854</td>\n",
              "      <td>0.0716</td>\n",
              "      <td>0.1729</td>\n",
              "      <td>-0.0434</td>\n",
              "      <td>0.1542</td>\n",
              "      <td>-0.2192</td>\n",
              "      <td>-0.0302</td>\n",
              "      <td>-0.4218</td>\n",
              "      <td>0.4057</td>\n",
              "      <td>-0.5372</td>\n",
              "      <td>0.1521</td>\n",
              "      <td>-0.2651</td>\n",
              "      <td>0.2310</td>\n",
              "      <td>-0.8101</td>\n",
              "      <td>0.4943</td>\n",
              "      <td>0.6905</td>\n",
              "      <td>-0.3720</td>\n",
              "      <td>-1.4110</td>\n",
              "      <td>0.4516</td>\n",
              "      <td>1.2300</td>\n",
              "      <td>-0.1949</td>\n",
              "      <td>-1.3280</td>\n",
              "      <td>-0.4276</td>\n",
              "      <td>-0.0040</td>\n",
              "      <td>-0.3086</td>\n",
              "      <td>...</td>\n",
              "      <td>0.4718</td>\n",
              "      <td>-0.2020</td>\n",
              "      <td>0.2783</td>\n",
              "      <td>0.4934</td>\n",
              "      <td>0.4144</td>\n",
              "      <td>0.5449</td>\n",
              "      <td>1.4690</td>\n",
              "      <td>-0.6142</td>\n",
              "      <td>0.6068</td>\n",
              "      <td>0.3434</td>\n",
              "      <td>0.9880</td>\n",
              "      <td>-0.0468</td>\n",
              "      <td>-0.1882</td>\n",
              "      <td>-0.0087</td>\n",
              "      <td>-0.0356</td>\n",
              "      <td>0.5718</td>\n",
              "      <td>0.4971</td>\n",
              "      <td>0.1057</td>\n",
              "      <td>0.2111</td>\n",
              "      <td>0.6992</td>\n",
              "      <td>0.0708</td>\n",
              "      <td>0.6169</td>\n",
              "      <td>0.2248</td>\n",
              "      <td>0.5994</td>\n",
              "      <td>0.2689</td>\n",
              "      <td>0.0305</td>\n",
              "      <td>1.2320</td>\n",
              "      <td>0.5409</td>\n",
              "      <td>0.3755</td>\n",
              "      <td>0.7343</td>\n",
              "      <td>0.2807</td>\n",
              "      <td>0.4116</td>\n",
              "      <td>0.6422</td>\n",
              "      <td>0.2256</td>\n",
              "      <td>0.7592</td>\n",
              "      <td>0.6656</td>\n",
              "      <td>0.3808</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>23812</th>\n",
              "      <td>id_fffcb9e7c</td>\n",
              "      <td>com_droga</td>\n",
              "      <td>24</td>\n",
              "      <td>D1</td>\n",
              "      <td>8b87a7a83</td>\n",
              "      <td>0.6660</td>\n",
              "      <td>0.2324</td>\n",
              "      <td>0.4392</td>\n",
              "      <td>0.2044</td>\n",
              "      <td>0.8531</td>\n",
              "      <td>-0.0343</td>\n",
              "      <td>0.0323</td>\n",
              "      <td>0.0463</td>\n",
              "      <td>0.4299</td>\n",
              "      <td>-0.7985</td>\n",
              "      <td>0.5742</td>\n",
              "      <td>0.1421</td>\n",
              "      <td>2.2700</td>\n",
              "      <td>0.2046</td>\n",
              "      <td>0.5363</td>\n",
              "      <td>-1.7330</td>\n",
              "      <td>0.1450</td>\n",
              "      <td>0.6097</td>\n",
              "      <td>0.2024</td>\n",
              "      <td>0.9865</td>\n",
              "      <td>-0.7805</td>\n",
              "      <td>0.9608</td>\n",
              "      <td>0.3440</td>\n",
              "      <td>2.7650</td>\n",
              "      <td>0.4925</td>\n",
              "      <td>0.6698</td>\n",
              "      <td>0.2374</td>\n",
              "      <td>-0.3372</td>\n",
              "      <td>0.8771</td>\n",
              "      <td>-2.6560</td>\n",
              "      <td>-0.2000</td>\n",
              "      <td>-0.2043</td>\n",
              "      <td>0.6797</td>\n",
              "      <td>-0.0248</td>\n",
              "      <td>-0.0927</td>\n",
              "      <td>...</td>\n",
              "      <td>0.9261</td>\n",
              "      <td>0.4419</td>\n",
              "      <td>0.0295</td>\n",
              "      <td>0.4220</td>\n",
              "      <td>0.4677</td>\n",
              "      <td>-0.1184</td>\n",
              "      <td>0.4524</td>\n",
              "      <td>0.4135</td>\n",
              "      <td>0.1356</td>\n",
              "      <td>-0.5801</td>\n",
              "      <td>0.0411</td>\n",
              "      <td>1.0240</td>\n",
              "      <td>1.0340</td>\n",
              "      <td>-0.0270</td>\n",
              "      <td>-0.4194</td>\n",
              "      <td>0.7403</td>\n",
              "      <td>-0.6793</td>\n",
              "      <td>-0.1423</td>\n",
              "      <td>0.7307</td>\n",
              "      <td>0.7946</td>\n",
              "      <td>-0.0650</td>\n",
              "      <td>0.9038</td>\n",
              "      <td>0.2324</td>\n",
              "      <td>0.9676</td>\n",
              "      <td>1.0940</td>\n",
              "      <td>0.4290</td>\n",
              "      <td>0.5187</td>\n",
              "      <td>-0.1105</td>\n",
              "      <td>0.4258</td>\n",
              "      <td>-0.2012</td>\n",
              "      <td>0.1506</td>\n",
              "      <td>1.5230</td>\n",
              "      <td>0.7101</td>\n",
              "      <td>0.1732</td>\n",
              "      <td>0.7015</td>\n",
              "      <td>-0.6290</td>\n",
              "      <td>0.0740</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>23813</th>\n",
              "      <td>id_ffffdd77b</td>\n",
              "      <td>com_droga</td>\n",
              "      <td>72</td>\n",
              "      <td>D1</td>\n",
              "      <td>972f41291</td>\n",
              "      <td>-0.8598</td>\n",
              "      <td>1.0240</td>\n",
              "      <td>-0.1361</td>\n",
              "      <td>0.7952</td>\n",
              "      <td>-0.3611</td>\n",
              "      <td>-3.6750</td>\n",
              "      <td>-1.2420</td>\n",
              "      <td>0.9146</td>\n",
              "      <td>3.0790</td>\n",
              "      <td>1.2460</td>\n",
              "      <td>1.9460</td>\n",
              "      <td>1.4370</td>\n",
              "      <td>2.9780</td>\n",
              "      <td>2.2370</td>\n",
              "      <td>-0.6818</td>\n",
              "      <td>0.6870</td>\n",
              "      <td>-1.1060</td>\n",
              "      <td>0.0182</td>\n",
              "      <td>-0.9247</td>\n",
              "      <td>-0.0738</td>\n",
              "      <td>-0.1919</td>\n",
              "      <td>-0.7722</td>\n",
              "      <td>-1.4050</td>\n",
              "      <td>-1.0050</td>\n",
              "      <td>-1.1170</td>\n",
              "      <td>-0.5293</td>\n",
              "      <td>-1.1720</td>\n",
              "      <td>-0.2885</td>\n",
              "      <td>0.1599</td>\n",
              "      <td>-0.4250</td>\n",
              "      <td>0.3591</td>\n",
              "      <td>-0.1420</td>\n",
              "      <td>-0.9530</td>\n",
              "      <td>-0.2005</td>\n",
              "      <td>-1.8340</td>\n",
              "      <td>...</td>\n",
              "      <td>-4.7130</td>\n",
              "      <td>-1.7250</td>\n",
              "      <td>-3.8650</td>\n",
              "      <td>-3.0800</td>\n",
              "      <td>-4.1530</td>\n",
              "      <td>-1.2030</td>\n",
              "      <td>-1.1690</td>\n",
              "      <td>-4.1460</td>\n",
              "      <td>-1.2670</td>\n",
              "      <td>-1.1300</td>\n",
              "      <td>-2.4390</td>\n",
              "      <td>0.1591</td>\n",
              "      <td>-2.2490</td>\n",
              "      <td>-2.5860</td>\n",
              "      <td>-1.9520</td>\n",
              "      <td>-2.1810</td>\n",
              "      <td>-4.6690</td>\n",
              "      <td>-3.9450</td>\n",
              "      <td>-2.9560</td>\n",
              "      <td>-2.7930</td>\n",
              "      <td>-2.1560</td>\n",
              "      <td>-2.4100</td>\n",
              "      <td>-1.8190</td>\n",
              "      <td>-3.3480</td>\n",
              "      <td>-0.1414</td>\n",
              "      <td>-2.6430</td>\n",
              "      <td>-2.5810</td>\n",
              "      <td>-3.3890</td>\n",
              "      <td>-1.7450</td>\n",
              "      <td>-6.6300</td>\n",
              "      <td>-4.0950</td>\n",
              "      <td>-7.3860</td>\n",
              "      <td>-1.4160</td>\n",
              "      <td>-3.5770</td>\n",
              "      <td>-0.4775</td>\n",
              "      <td>-2.1500</td>\n",
              "      <td>-4.2520</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>23814 rows × 880 columns</p>\n",
              "</div>"
            ],
            "text/plain": [
              "                 id  ... serotonin_reuptake_inhibitor\n",
              "0      id_000644bb2  ...                            0\n",
              "1      id_000779bfc  ...                            0\n",
              "2      id_000a6266a  ...                            0\n",
              "3      id_0015fd391  ...                            0\n",
              "4      id_001626bd3  ...                            0\n",
              "...             ...  ...                          ...\n",
              "23809  id_fffb1ceed  ...                            0\n",
              "23810  id_fffb70c0c  ...                            0\n",
              "23811  id_fffc1c3f4  ...                            0\n",
              "23812  id_fffcb9e7c  ...                            0\n",
              "23813  id_ffffdd77b  ...                            0\n",
              "\n",
              "[23814 rows x 880 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 164
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "qBKsOap9jJYr"
      },
      "source": [
        "### Checando que nas amostras de controle, os mdas não são ativados"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "ZLGhxflIh71W",
        "outputId": "20357dc9-354b-4996-d7a5-8a1b399a5a48"
      },
      "source": [
        "experimento_controle = experimento.query('tratamento == \"com_controle\"')\n",
        "for mda in m_ativ_ser:\n",
        "    ativacoes = experimento_controle[mda].sum()\n",
        "    if ativacoes > 0 :\n",
        "        print('Mecanismo de ativação ' + mda + ' foi ativado ' + ativacoes + 'vezes')\n",
        "    else:\n",
        "        print('Mecanismo de ativação ' + mda + ' não foi ativado no controle')"
      ],
      "execution_count": 165,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Mecanismo de ativação serotonin_receptor_agonist não foi ativado no controle\n",
            "Mecanismo de ativação serotonin_receptor_antagonist não foi ativado no controle\n",
            "Mecanismo de ativação serotonin_reuptake_inhibitor não foi ativado no controle\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "mWCb_usQoPt6"
      },
      "source": [
        "### Preparando os dados para demonstrar diferenças entre médias de expressões gênicas em gráficos de heatmap"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "78oBlfHjDJHK"
      },
      "source": [
        "media_exp_gen_controle_series = experimento_controle.loc[:,'g_0':'g_771'].mean()\n",
        "media_exp_gen_controle = pd.DataFrame({'media_nao_ativado':media_exp_gen_controle_series.values})"
      ],
      "execution_count": 166,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "GqWebNs3nePA"
      },
      "source": [
        "query = 'tratamento == \"com_droga\" and serotonin_receptor_agonist == 1 and serotonin_receptor_antagonist == 0'\n",
        "\n",
        "media_exp_gen_ativ_series = experimento.query(query).loc[:,'g_0':'g_771'].mean()\n",
        "media_exp_gen_agonist = pd.DataFrame({'media_ativado(serotonin_receptor_agonist)':media_exp_gen_ativ_series.values})\n",
        "media_exp_gen_agonist['media_nao_ativado(Controle)'] = media_exp_gen_controle['media_nao_ativado']"
      ],
      "execution_count": 167,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "TpoOM79Ds7Bn"
      },
      "source": [
        "### **Gráficos comparativos entre controle e amostras com agentes de serotonina ativadas.**\n",
        "\n",
        "> Nesses gráficos comparativos destacados abaixo é possível notar grupos de expressões gênicas onde as médias de suas expressões diferem muito das médias contidas nas amostras. "
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "-0njz2j2nryw"
      },
      "source": [
        "#### Amostras com mda `serotonin_receptor_agonist` ativadas comparadas com o controle.\n",
        "\n"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 930
        },
        "id": "1RU8V9-5jmjh",
        "outputId": "225e52de-d5ee-4355-a77f-f23d4c6db895"
      },
      "source": [
        "f, ax = plt.subplots(figsize=(18, 14))\n",
        "f.suptitle('Amostras X controle', fontsize=40)\n",
        "sns.heatmap(media_exp_gen_agonist, cbar_kws={\"shrink\": .5})"
      ],
      "execution_count": 168,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.axes._subplots.AxesSubplot at 0x7fabefef7950>"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 168
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 1296x1008 with 2 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "JWcSQY_Cpoxf"
      },
      "source": [
        "query = 'tratamento == \"com_droga\" and (serotonin_receptor_agonist == 0 and serotonin_receptor_antagonist == 1)'\n",
        "\n",
        "media_exp_gen_ativ_series = experimento.query(query).loc[:,'g_0':'g_771'].mean()\n",
        "media_exp_gen_antagonist = pd.DataFrame({'media_ativado(serotonin_receptor_antagonist)':media_exp_gen_ativ_series.values})\n",
        "media_exp_gen_antagonist['media_nao_ativado(Controle)'] = media_exp_gen_controle['media_nao_ativado']"
      ],
      "execution_count": 169,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "by_PBlq4uccs"
      },
      "source": [
        "#### Amostras com mda `serotonin_receptor_antagonist` ativadas comparadas com o controle.\n"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 930
        },
        "id": "YvyCXNUYp3Li",
        "outputId": "4a758fef-fccc-4604-ba91-4aa894aa85fa"
      },
      "source": [
        "f, ax = plt.subplots(figsize=(18, 14))\n",
        "f.suptitle('Amostras X Controle', fontsize=40)\n",
        "sns.heatmap(media_exp_gen_antagonist, cbar_kws={\"shrink\": .5})"
      ],
      "execution_count": 170,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.axes._subplots.AxesSubplot at 0x7fabefd3cb10>"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 170
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 1296x1008 with 2 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "7EXh0F5lrBME"
      },
      "source": [
        "media_exp_gen_antagonist = media_exp_gen_antagonist.drop('media_nao_ativado(Controle)', 1)\n",
        "media_exp_gen_antagonist['media_ativado(serotonin_receptor_agonist)'] = media_exp_gen_agonist['media_ativado(serotonin_receptor_agonist)']"
      ],
      "execution_count": 171,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "DP6gxuaDux5f"
      },
      "source": [
        "#### Amostras com mda `serotonin_receptor_antagonist`  ativadas comparadas com Amostras com mda  `serotonin_receptor_agonist` ativadas.\n",
        "\n",
        "> Aqui é a diferença das médias das expressões gênicas entre os dois mdas (serotonin_receptor_antagonist e serotonin_receptor_agonist), é muito mais sutil, mas ainda assim é possível identificar pequenos grupos onde as médias divergem. É bem curioso notar que, apesar desses mdas terem ações contrárias suas expressões gênicas têm uma média similar com apenas algunas casos de divergência.\n"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 930
        },
        "id": "dtpvCOfGsfMC",
        "outputId": "3305e9c4-c514-49b4-90a2-aae796421ab2"
      },
      "source": [
        "f, ax = plt.subplots(figsize=(18, 14))\n",
        "f.suptitle('Amostra Antagosnista X Amostra Agonista', fontsize=40)\n",
        "sns.heatmap(media_exp_gen_antagonist, cbar_kws={\"shrink\": .5})"
      ],
      "execution_count": 172,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.axes._subplots.AxesSubplot at 0x7fabefb9f1d0>"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 172
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 1296x1008 with 2 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "EhVk0jYREAmX"
      },
      "source": [
        "##**5. Introdução de modelos de predição**"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "zyydlDEpFIHw"
      },
      "source": [
        "# Modelos de predição\n",
        "from sklearn.linear_model import LogisticRegression\n",
        "from sklearn.dummy import DummyClassifier\n",
        "from sklearn.tree import DecisionTreeClassifier\n",
        "from sklearn.linear_model import LinearRegression\n",
        "from sklearn.linear_model import Ridge\n",
        "# Utilizado para montar uma estrutura de teste e treino para os modelos de predição\n",
        "from sklearn.model_selection import train_test_split"
      ],
      "execution_count": 173,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "WWBQXTt_GOmA"
      },
      "source": [
        "# Dados para testagem e treinamento dos modelos\n",
        "x = experimento.loc[:,'g_0':'g_771']\n",
        "y = experimento['serotonin_receptor_agonist']\n",
        "\n",
        "# Geração de dados para teste e treino\n",
        "x_treino, x_teste, y_treino, y_teste = train_test_split(x, y, test_size = 0.2, stratify=y, random_state=100)"
      ],
      "execution_count": 174,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "YRcRiDyFGzQE"
      },
      "source": [
        "### Utilizando um modelo de classificação básico para utilizar como base para os outros modelos. DummyClassifier.\n",
        "> Vale notar que o modelo mais básico de classificação já retorna um média de acertos de aproximadamente 99%."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "gvHL4sXdvW09",
        "outputId": "796e98d3-c259-4c0a-f507-a9b3e2ed2695"
      },
      "source": [
        "modelo_dummy = DummyClassifier('most_frequent')\n",
        "modelo_dummy.fit(x_treino, y_treino)\n",
        "modelo_dummy.score(x_teste, y_teste)"
      ],
      "execution_count": 175,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0.990132269577997"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 175
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "QL8qyHBjHhqZ"
      },
      "source": [
        "### Utilizando um modelo de regressão logística\n",
        "> Também alcançamos um alto valor de acurácia (aproximadamente 98%), porém com uma performance menor que o modelo básico que utilizamos anteriormente"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "l_N2VZvDJO0E",
        "outputId": "63f8401b-097a-404b-aea2-04af7adf9ad0"
      },
      "source": [
        "modelo_rlogistica = LogisticRegression(max_iter=500)\n",
        "modelo_rlogistica.fit(x_treino, y_treino)\n",
        "modelo_rlogistica.score(x_teste, y_teste)"
      ],
      "execution_count": 176,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0.9819441528448457"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 176
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "U3lpVidpIeNz"
      },
      "source": [
        "### Utilizando um modelo de árvore de decisão\n",
        "> Esse modelo atingiu a mesma acurácia do modelo mais simples. Vale notar que a utilização do parâmetro `max_depth` não alterou os resultados em diversos valores testados, apenas aumentou o tempo de processamento para retornar o resultado, por isso foi mantido o valor igual a 1"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "PqFIpi_-yl4z",
        "outputId": "6b477775-a1d8-4612-a6a4-a510dd0f161b"
      },
      "source": [
        "modelo_arvore = DecisionTreeClassifier(max_depth = 1)\n",
        "modelo_arvore.fit(x_treino, y_treino)\n",
        "modelo_arvore.score(x_teste, y_teste)"
      ],
      "execution_count": 177,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0.990132269577997"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 177
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "rD7WMVpsKSBA"
      },
      "source": [
        "### Modelos que tiveram uma performance abaixo dos modelo básico.\n",
        "* Linear Regression\n",
        "* Ridge regression and classification"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "Em5n2-kSschP",
        "outputId": "b3755d55-932f-4699-acb1-5cf4a209034d"
      },
      "source": [
        "modelo_linearRegr = LinearRegression()\n",
        "modelo_linearRegr.fit(x_treino, y_treino)\n",
        "modelo_linearRegr.score(x_teste, y_teste)"
      ],
      "execution_count": 178,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "-0.03148319335559746"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 178
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "9NmsCQagKmjZ",
        "outputId": "298aa982-057c-4b75-a65a-2be21a79fbfb"
      },
      "source": [
        "modelo_ridge = Ridge()\n",
        "modelo_ridge.fit(x_treino, y_treino)\n",
        "modelo_ridge.score(x_teste, y_teste)"
      ],
      "execution_count": 179,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "-0.03147231170109488"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 179
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "gm69TLOz6fS2"
      },
      "source": [
        "##**6. Conclusão**\n",
        "> Esse foi um estudo inicial para demonstrar que é possível, utilizando os mais adequados modelos de predição, determinar que a partir das expressões gênicas, determinar que os mdas do nosso estudo serão ativados. Verificamos que um modelo de árvore de decisão foi capaz de alcançar aproximadamente 99% de acurácia.\n",
        "\n",
        "> Verificamos também que é possível, desde que seja feita uma análise mais detalhada, dizer quais expressões gênicas podem estar relacionadas com a ativação desses mdas. \n",
        "\n",
        "> Nesse estudo, não foi levado em consideração a viabilidade celular das amostras, mas seria relevante filtrar nos resultados, as amostras que apresentaram maior viabilidade celular."
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "7auvZbvhLi6p"
      },
      "source": [
        "> "
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "yYQIJEssWMa8"
      },
      "source": [
        "##**7. Referências**\n",
        "1. Anti-depressivos: https://en.wikipedia.org/wiki/Antidepressant\n",
        "2. Ansiolíticos: https://en.wikipedia.org/wiki/Anxiolytic\n",
        "3. Redução de apetitite: https://en.wikipedia.org/wiki/Anorectic\n",
        "4. Serotonin_receptor_agonist - https://en.wikipedia.org/wiki/Serotonin_receptor_agonist\n",
        "5. Machine Learning in Drug Discovery and Development Part 1: A Primer - https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7080529/\n",
        "6. Drug discovery: passado, presente e futuro - https://docs.google.com/document/d/10EhrQBChlyYIcff3to7PrCQi5HcNk2r-zd2ZCKPtcz8/edit\n",
        "7. Sci-kit Learn - https://scikit-learn.org/stable/index.html\n",
        "8. A Next Generation Connectivity Map: L1000 platform and the first 1,000,000 profiles - https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5990023/"
      ]
    }
  ]
}