projeto_integrado_grupo2.ipynb

{
 "cells": [
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# PROJETO INTEGRADO CDIA5 - GRUPO 2"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Coletando dados de uma API\n",
    "\n",
    "### Visão geral\n",
    "\n",
    "Esse caderno tem como objetivo coletar os dados da API publica CIViC (Clinical Interpretation of variants in cancer) e transforma-los em dataframes.\n",
    "\n",
    "### Objetivo do código\n",
    "\n",
    "Acessar a API CIViC e coletar os dados fornecidos."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "#Importações\n",
    "import requests\n",
    "import json\n",
    "import pandas as pd"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## API's\n",
    "\n",
    "Existem quatro tipos de informações diferentes que podem ser acessadas, cada um com seu próprio endereço na API. Por padrão, recebemos uma informação por vez. No entanto, estamos mudando o valor \"count\" para recebermos mais informações em uma unica chamada.\n",
    "\n",
    "URL's para coletar os valores*\n",
    "\n",
    "Evidences: \"https://civicdb.org/api/evidence_items\".\n",
    "\n",
    "Variant: \"https://civicdb.org/api/variants\".\n",
    "\n",
    "Gene: \"https://civicdb.org/api/genes\".\n",
    "\n",
    "Para cada uma das APIs, faremos uma solicitação GET, sendo que cada uma delas possui um retorno diferente. A fim de garantir que a solicitação de coleta tenha sido aceita, verificaremos o \"status_code\". Os códigos mais comuns incluem:\n",
    "\n",
    "200: tudo funcionou conforme o esperado e a validação dos dados foi realizada com sucesso.\n",
    "\n",
    "400: ocorreram problemas durante a autenticação.\n",
    "\n",
    "401: este erro ocorre quando há algum problema na requisição.\n",
    "\n",
    "500: ocorreu algum erro interno no servidor.\n",
    "\n",
    "Além disso, manteremos um padrão de 10.000 para o retorno."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "#URL da API\n",
    "Evidences = requests.get('https://civicdb.org/api/evidence_items?count=10000')\n",
    "Variant = requests.get('https://civicdb.org/api/variants?count=10000')\n",
    "Gene = requests.get('https://civicdb.org/api/genes?count=10000')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Status_code de Evidences: 200\n",
      "Status_code de Variant: 200\n",
      "Status_code de Gene: 200\n"
     ]
    }
   ],
   "source": [
    "#Codigo de status \n",
    "print(f\"Status_code de Evidences: {Evidences.status_code}\")\n",
    "print(f\"Status_code de Variant: {Variant.status_code}\")\n",
    "print(f\"Status_code de Gene: {Gene.status_code}\")"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Json.loads() É um método usado para decodificar uma string JSON em um objeto Python. O objeto Python resultante pode ser uma lista, um dicionário, um valor de string, um número, um valor booleano ou None. A função .content é usada para retornar o conteúdo de uma resposta HTTP em formato de bytes.\n",
    "\n",
    "Converteremos o conteúdo da resposta HTTP, que é originalmente uma string no formato JSON, para um dicionário Python utilizando a função .loads."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "#Json.load: Pega um objeto em arquivo e retorna o objeto json;\n",
    "#contente: Retorna o contéudo da resposta em dicionario.\n",
    "Evidences_dict=json.loads(Evidences.content)\n",
    "Variant_dict=json.loads(Variant.content)\n",
    "Gene_dict=json.loads(Gene.content)"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Utilizando o método pd.DataFrame iremos converter o dicionário Python em um dataframe pandas com os dados retornados pela API."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "#Pega apenas uma das chaves que a URL retorna e a transforma em um dataframe.\n",
    "df_evidences=pd.DataFrame(Evidences_dict[\"records\"])\n",
    "df_variant=pd.DataFrame(Variant_dict[\"records\"])\n",
    "df_gene=pd.DataFrame(Gene_dict[\"records\"])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "#Salva o dataframe criado.\n",
    "df_evidences.to_csv('Evidence.csv', index=False)\n",
    "df_variant.to_csv('Variants.csv', index=False)\n",
    "df_gene.to_csv('Genes.csv', index=False)"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Em virtude das informações citadas acima, entendemos que esse código busca um grande conjunto de dados em 3 URL's diferentes, Gene, Evidences, Variants e os transforma em dataset para que possam ser usados posteriormente."
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Junção CSV's CIViC\n",
    "\n",
    "Infelizmente, os dados que a API oferece não são suficientes para a realização da nossa análise. Por isso, usaremos dados fornecidos pelo CIViC. Assim como na API, os arquivos CSV também estão separados e precisam ser mesclados. Para isso, iremos utilizar a função \"merge\".\n",
    "\n",
    "Abaixo, definiremos os arquivos que iremos utilizar."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [],
   "source": [
    "#Definiçao dos arquivos que serão utilizados\n",
    "df_Variant = pd.read_csv('01-Apr-2022-VariantSummaries.csv')\n",
    "df_Evidences = pd.read_csv('01-Apr-2022-ClinicalEvidenceSummaries.csv')\n",
    "df_Gene = pd.read_csv('01-Apr-2022-GeneSummaries.csv')"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Possuímos três DataFrames diferentes: df_Variant, que possui 1533 linhas e 33 colunas, df_evidences, que possui 3897 linhas e 42 colunas e df_gene com 477 linhas e 7 colunas.\n",
    "\n",
    "Antes de podermos realizar o merge, precisamos fazer um pequeno tratamento nos dados fornecidos. Inicialmente, iremos trocar o nome de duas colunas, que possue o mesmo valor e significado, porém com nome diferente em outros df's."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [],
   "source": [
    "#Trocando o nome da coluna name para gene.\n",
    "df_Variant.rename(columns={'summary': 'variant_summary'}, inplace = True)\n",
    "df_Gene.rename(columns={'name': 'gene'}, inplace = True)"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Também precisamos apagar algumas colunas, pois elas não se encontram em nenhum outro df, e por conta disso, a mesclagem se torna um problema. Essas colunas serão excluídas, mas nenhum dado importante será perdido."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [],
   "source": [
    "#Apagando colunas\n",
    "df_Variant = df_Variant.drop(['assertion_ids', 'is_flagged'],axis=1)\n",
    "df_Gene = df_Gene.drop(['gene_civic_url', 'last_review_date', 'is_flagged'],axis=1)"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Com os df's tratados, podemos realizar o merge. Inicialmente, iremos juntar o df_Variant tratado e o df_Evidences. Assim, criaremos um df nomeado CIViC que possui a junção de todos os registros dos dois conjuntos de dados originais.\n",
    "\n",
    "Para que essa mesclagem funcione, usaremos como chave a coluna 'variant_id'.\n",
    "\n",
    "Para que nossos dados não fiquem poluídos e que não precisemos ter um trabalho extra excluindo colunas repetidas que serão geradas com o merge, pois nosso conjunto de dados possuem colunas com o mesmo nome, iremos definir o parâmetro \"suffixes\" como uma tupla vazia e uma tupla com o sufixo \"_y\", o que garantirá que não haverá colunas duplicadas após a junção.\n",
    "\n",
    "Após a junção, usaremos o método \"filter\" para selecionar apenas as colunas que possuem o sufixo \"_y\" no nome e usaremos o método \"drop\" para removê-las."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [],
   "source": [
    "#Merge entre Civic e df_Variant\n",
    "civic = pd.merge(df_Evidences, df_Variant,  left_on='variant_id', right_on='variant_id', how='left', suffixes=('','_y'))\n",
    "civic.drop(civic.filter(regex='_y$').columns, axis=1, inplace=True)"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Em seguida juntaremos ao conjunto total ao df_Gene. Usaremos as colunas \"gene\" como chave de junção e passaremos o mesmo parâmetro \"suffixes\" para apagar colunas duplicadas."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [],
   "source": [
    "#Merge entre Civic e df_gene\n",
    "civic = pd.merge(civic, df_Gene,  left_on='gene', right_on='gene', how='left', suffixes=('','_y'))\n",
    "civic.drop(civic.filter(regex='_y$').columns, axis=1, inplace=True)"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Agora, com o dataframe pronto, realizaremos o último tratamento dessa etapa. Aqui, apagaremos colunas sem valor para nossa análise, como URLs e a última edição (\"last_review_date\"), colunas com valores nulos em todos os dataframes."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>gene</th>\n",
       "      <th>entrez_id</th>\n",
       "      <th>variant</th>\n",
       "      <th>disease</th>\n",
       "      <th>doid</th>\n",
       "      <th>phenotypes</th>\n",
       "      <th>drugs</th>\n",
       "      <th>drug_interaction_type</th>\n",
       "      <th>evidence_type</th>\n",
       "      <th>evidence_direction</th>\n",
       "      <th>...</th>\n",
       "      <th>variant_summary</th>\n",
       "      <th>variant_origin</th>\n",
       "      <th>is_flagged</th>\n",
       "      <th>variant_types</th>\n",
       "      <th>hgvs_expressions</th>\n",
       "      <th>civic_variant_evidence_score</th>\n",
       "      <th>allele_registry_id</th>\n",
       "      <th>clinvar_ids</th>\n",
       "      <th>variant_aliases</th>\n",
       "      <th>description</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>JAK2</td>\n",
       "      <td>3717</td>\n",
       "      <td>V617F</td>\n",
       "      <td>Lymphoid Leukemia</td>\n",
       "      <td>1037.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Diagnostic</td>\n",
       "      <td>Supports</td>\n",
       "      <td>...</td>\n",
       "      <td>JAK2 V617F is a highly recurrent mutation in m...</td>\n",
       "      <td>Somatic</td>\n",
       "      <td>False</td>\n",
       "      <td>missense_variant,gain_of_function_variant</td>\n",
       "      <td>NC_000009.11:g.5073770G&gt;T,NM_004972.3:c.1849G&gt;...</td>\n",
       "      <td>83.0</td>\n",
       "      <td>CA124183</td>\n",
       "      <td>14662</td>\n",
       "      <td>VAL617PHE,RS77375493</td>\n",
       "      <td>JAK2 is a kinase that is misregulated or mutat...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>PDGFRA</td>\n",
       "      <td>5156</td>\n",
       "      <td>D842V</td>\n",
       "      <td>Gastrointestinal Stromal Tumor</td>\n",
       "      <td>9253.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Diagnostic</td>\n",
       "      <td>Supports</td>\n",
       "      <td>...</td>\n",
       "      <td>PDGFRA D842 mutations are characterized broadl...</td>\n",
       "      <td>Somatic</td>\n",
       "      <td>False</td>\n",
       "      <td>missense_variant</td>\n",
       "      <td>NM_006206.4:c.2525A&gt;T,NP_006197.1:p.Asp842Val,...</td>\n",
       "      <td>100.5</td>\n",
       "      <td>CA123194</td>\n",
       "      <td>13543</td>\n",
       "      <td>ASP842VAL,RS121908585</td>\n",
       "      <td>Commonly mutated in GI tract tumors, PDGFR fam...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>DNMT3A</td>\n",
       "      <td>1788</td>\n",
       "      <td>R882</td>\n",
       "      <td>Acute Myeloid Leukemia</td>\n",
       "      <td>9119.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Diagnostic</td>\n",
       "      <td>Supports</td>\n",
       "      <td>...</td>\n",
       "      <td>DNMT3A R882 mutations are associated with cyto...</td>\n",
       "      <td>Somatic</td>\n",
       "      <td>False</td>\n",
       "      <td>missense_variant</td>\n",
       "      <td>NaN</td>\n",
       "      <td>510.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>375882,375883,375884,375879,375880,375881</td>\n",
       "      <td>R693,ARG882,ARG693</td>\n",
       "      <td>DNMT3A is one of several epigenetic modifiers ...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>DNMT3A</td>\n",
       "      <td>1788</td>\n",
       "      <td>R882</td>\n",
       "      <td>Acute Myeloid Leukemia</td>\n",
       "      <td>9119.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Diagnostic</td>\n",
       "      <td>Supports</td>\n",
       "      <td>...</td>\n",
       "      <td>DNMT3A R882 mutations are associated with cyto...</td>\n",
       "      <td>Somatic</td>\n",
       "      <td>False</td>\n",
       "      <td>missense_variant</td>\n",
       "      <td>NaN</td>\n",
       "      <td>510.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>375882,375883,375884,375879,375880,375881</td>\n",
       "      <td>R693,ARG882,ARG693</td>\n",
       "      <td>DNMT3A is one of several epigenetic modifiers ...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>JAK2</td>\n",
       "      <td>3717</td>\n",
       "      <td>V617F</td>\n",
       "      <td>Chronic Myeloid Leukemia</td>\n",
       "      <td>8552.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Diagnostic</td>\n",
       "      <td>Supports</td>\n",
       "      <td>...</td>\n",
       "      <td>JAK2 V617F is a highly recurrent mutation in m...</td>\n",
       "      <td>Somatic</td>\n",
       "      <td>False</td>\n",
       "      <td>missense_variant,gain_of_function_variant</td>\n",
       "      <td>NC_000009.11:g.5073770G&gt;T,NM_004972.3:c.1849G&gt;...</td>\n",
       "      <td>83.0</td>\n",
       "      <td>CA124183</td>\n",
       "      <td>14662</td>\n",
       "      <td>VAL617PHE,RS77375493</td>\n",
       "      <td>JAK2 is a kinase that is misregulated or mutat...</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 39 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "     gene  entrez_id variant                         disease    doid   \n",
       "0    JAK2       3717   V617F               Lymphoid Leukemia  1037.0  \\\n",
       "1  PDGFRA       5156   D842V  Gastrointestinal Stromal Tumor  9253.0   \n",
       "2  DNMT3A       1788    R882          Acute Myeloid Leukemia  9119.0   \n",
       "3  DNMT3A       1788    R882          Acute Myeloid Leukemia  9119.0   \n",
       "4    JAK2       3717   V617F        Chronic Myeloid Leukemia  8552.0   \n",
       "\n",
       "  phenotypes drugs drug_interaction_type evidence_type evidence_direction   \n",
       "0        NaN   NaN                   NaN    Diagnostic           Supports  \\\n",
       "1        NaN   NaN                   NaN    Diagnostic           Supports   \n",
       "2        NaN   NaN                   NaN    Diagnostic           Supports   \n",
       "3        NaN   NaN                   NaN    Diagnostic           Supports   \n",
       "4        NaN   NaN                   NaN    Diagnostic           Supports   \n",
       "\n",
       "   ...                                    variant_summary variant_origin   \n",
       "0  ...  JAK2 V617F is a highly recurrent mutation in m...        Somatic  \\\n",
       "1  ...  PDGFRA D842 mutations are characterized broadl...        Somatic   \n",
       "2  ...  DNMT3A R882 mutations are associated with cyto...        Somatic   \n",
       "3  ...  DNMT3A R882 mutations are associated with cyto...        Somatic   \n",
       "4  ...  JAK2 V617F is a highly recurrent mutation in m...        Somatic   \n",
       "\n",
       "  is_flagged                              variant_types   \n",
       "0      False  missense_variant,gain_of_function_variant  \\\n",
       "1      False                           missense_variant   \n",
       "2      False                           missense_variant   \n",
       "3      False                           missense_variant   \n",
       "4      False  missense_variant,gain_of_function_variant   \n",
       "\n",
       "                                    hgvs_expressions   \n",
       "0  NC_000009.11:g.5073770G>T,NM_004972.3:c.1849G>...  \\\n",
       "1  NM_006206.4:c.2525A>T,NP_006197.1:p.Asp842Val,...   \n",
       "2                                                NaN   \n",
       "3                                                NaN   \n",
       "4  NC_000009.11:g.5073770G>T,NM_004972.3:c.1849G>...   \n",
       "\n",
       "  civic_variant_evidence_score  allele_registry_id   \n",
       "0                         83.0            CA124183  \\\n",
       "1                        100.5            CA123194   \n",
       "2                        510.0                 NaN   \n",
       "3                        510.0                 NaN   \n",
       "4                         83.0            CA124183   \n",
       "\n",
       "                                 clinvar_ids        variant_aliases   \n",
       "0                                      14662   VAL617PHE,RS77375493  \\\n",
       "1                                      13543  ASP842VAL,RS121908585   \n",
       "2  375882,375883,375884,375879,375880,375881     R693,ARG882,ARG693   \n",
       "3  375882,375883,375884,375879,375880,375881     R693,ARG882,ARG693   \n",
       "4                                      14662   VAL617PHE,RS77375493   \n",
       "\n",
       "                                         description  \n",
       "0  JAK2 is a kinase that is misregulated or mutat...  \n",
       "1  Commonly mutated in GI tract tumors, PDGFR fam...  \n",
       "2  DNMT3A is one of several epigenetic modifiers ...  \n",
       "3  DNMT3A is one of several epigenetic modifiers ...  \n",
       "4  JAK2 is a kinase that is misregulated or mutat...  \n",
       "\n",
       "[5 rows x 39 columns]"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Removendo colunas\n",
    "civic = civic.drop(['gene_civic_url', 'Unnamed: 32', 'Unnamed: 31', 'Unnamed: 29',\n",
    "                    'Unnamed: 30', 'assertion_civic_urls', 'variant_civic_url', \n",
    "                    'evidence_civic_url', 'last_review_date', 'asco_abstract_id',\n",
    "                    'nct_ids', 'chromosome2', 'start2', 'stop2', 'representative_transcript2',\n",
    "                    'variant_groups'],axis=1)\n",
    "civic.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index(['gene', 'entrez_id', 'variant', 'disease', 'doid', 'phenotypes',\n",
       "       'drugs', 'drug_interaction_type', 'evidence_type', 'evidence_direction',\n",
       "       'evidence_level', 'clinical_significance', 'evidence_statement',\n",
       "       'citation_id', 'source_type', 'citation', 'rating', 'evidence_status',\n",
       "       'evidence_id', 'variant_id', 'gene_id', 'chromosome', 'start', 'stop',\n",
       "       'reference_bases', 'variant_bases', 'representative_transcript',\n",
       "       'ensembl_version', 'reference_build', 'variant_summary',\n",
       "       'variant_origin', 'is_flagged', 'variant_types', 'hgvs_expressions',\n",
       "       'civic_variant_evidence_score', 'allele_registry_id', 'clinvar_ids',\n",
       "       'variant_aliases', 'description'],\n",
       "      dtype='object')"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "civic.columns"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Diante disso, podemos ver que nosso código consiste na leitura de três arquivos csv fornecidos pelo proprio CIViC, e na sua mesclagem em um dataframe final chamado civic, tambem possuimos alguns tratamentos sendo realizados nos dados, como a exclusão de colunas, a renomeação de colunas e a remoçao de valores duplicados. Ao final o Dataframe civic é salvo em um arquivo csv para ser utilizado posteriormente."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [],
   "source": [
    "#Salvando o df Civic como um csv\n",
    "civic.to_csv('civic.csv', index=False)"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Análise Descritiva"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "O Conjunto de dados possui 3897 linhas e 39 colunas.\n"
     ]
    }
   ],
   "source": [
    "#Consultando numero de colunas e linhas.\n",
    "print(f'O Conjunto de dados possui {civic.shape[0]} linhas e {civic.shape[1]} colunas.')"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Descrição das colunas:\n",
    "\n",
    "- **Gene:** Nome do gene que sofreu a multação;\n",
    "- **entrez_id:** ID do gene;\n",
    "- **variant:** Descrição do tipo de variante;\n",
    "- **disease:** Doença específica ou subtipo de doença associada;\n",
    "- **doid:** ID de disease;\n",
    "- **phenotypes:** Fenótipo específico associado a declaração de evidências;\n",
    "- **drugs:** Droga usada durante o tratamento;\n",
    "- **evidence_type:** Categoria da ação clínica/relevância implicada pelo evento. (Predictive - Evidência pertencente ao efeito de um perfil molecular na resposta terapêutica. Diagnostic - Evidência relativa ao impacto de um perfil molecular no diagnóstico do paciente. Prognostic - Evidência pertencente ao impacto de um perfil molecular na progressão da doença, gravidade ou sobrevida do paciente; Predisposing - A evidência refere-se ao papel de um perfil molecular da linhagem germinativa em conferir suscetibilidade à doença (incluindo avaliações de patogenicidade); Oncogenic - A evidência refere-se ao envolvimento de um perfil molecular somático na patogênese do tumor; Functional - A evidência refere-se ao impacto de um perfil molecular na função do produto gênico(por exemplo, proteína));\n",
    "- **evidence_direction:** Um indicador de se a declaração de evidência apóia ou refuta a importância de um evento. (Supports ou Does not support);\n",
    "- **clinical_significance:** O impacto do perfil molecular (uma ou mais variantes) para tipos de evidências preditivas, prognósticas, diagnósticas, oncogênicas ou funcionais. (Sensitivity/Response - Associada a uma resposta clínica ou pré-clínica ao tratamento; Reduced Sensitivity - A resposta ao tratamento é menor do que a observada em outros contextos de tratamento; Resistance - Associada á resistência clínica ou pré-clínica ao tratamento; Adverse Response - Associada a uma resposta adversa ao tratamento medicamentoso; N/A - A variante não informa o interpretação clínica; Positive - Associada ao diagnóstico de doença ou subtipo; Negative - Associada á ausência de doença ou subtipo; Better outcome - Demonstra resultado clínico melhor do que o esperado; Poor outcome - Demonstra resultado clínico pior do que o esperado; Pathogenic - As evidências podem contribuir para uma interpretação de patogênico/benigno no nível de afirmação; Proteção - A presença de um perfil molecular germinativo reduz a chance de cancêr;\n",
    "- **variant_id:** ID da variante.\n",
    "- **gene_id:** ID do gene (Exibido automaticamente pela interface CIVIC.)\n",
    "- **is_flagged:** Se o paciente deu algum sinal de que possuia cancêr antes do inicio do tratamento. (Fácil detecção);\n",
    "- **drug_interaction_type:** Se a droga usada durante o tratamento teve que ser substituida ou combinada/complementada;\n",
    "- **evidence_level:** Descreve a robustez do tipo de estudo que suporta o item de evidência; (A - Associação validad, B - Evidência clínica, C - Estudo de caso, D - Evidência pré-clínica, E - Associação inferencial);\n",
    "- **evidence_statement:** Detalhes de tipos específicos de declarações de evidências: Uma reiteração do tipo de evidência, perfil molecular (variante), gene e doença;\n",
    "- **citation_id:** ID de citation\n",
    "- **source_type:** Tipo de fonte e ID, vincula o item de evidência à publicação original que suporta as reivindicações de evidência.\n",
    "- **citation:** Publicação onde o evento foi descrito/explorado gerado automaticamente a partir do ID PudMed fornecido pelo curador e links para a página interna da publicaçãi CIVIC mostrando todos os itens de evidência da publicação;\n",
    "- **rating:** Discernir rapidamente o quão bem a evidência derivada do artigo ou fonte suporta uma determinada declaração de evidência, um sistema de classificação de cinco estrelas é usado, (5 -Evidência fortes e bem fundamentadas de um laboratório ou periódico com reputação acadêmica respeitada. Os experimentos são bem controlados e os resultados são limpos e reprodutíveis em várias réplicas. Evidências confirmadas por métodos independentes. O estudo é estatisticamente bem alimentado, 4 - Evidência forte e bem suportada. As experiências são bem controladas e os resultados são convincentes. Quaisquer discrepâncias dos resultados esperados são bem explicadas e não são preocupantes, 3 - As evidências são convincentes, mas não apoiadas por uma variedade de experimentos. Podem ser projetos de menor escala ou novos resultados sem muitos experimentos de acompanhamento. As discrepâncias dos resultados esperados são explicadas e não são preocupantes, 2 - As evidências não são bem apoiadas por dados experimentais e poucos dados de acompanhamento estão disponíveis. A publicação é de um periódico de baixo impacto acadêmico. Os experimentos podem carecer de controles adequados, ter tamanho de amostra pequeno ou não serem estatisticamente convincentes, 1 - A alegação não é bem apoiada por evidências experimentais. Os resultados não são reprodutíveis ou têm um tamanho de amostra muito pequeno. Nenhum acompanhamento é feito para validar novas reivindicações.);\n",
    "- **evidence_status:** Se o experimento apoio ou não apoia a variante usada;\n",
    "- **evidence_id:** ID da evidencia;\n",
    "- **chromosome:** Nome do cromossomo no qual ocorre a variante;\n",
    "- **stat:** Posições inicial da variante (coordenada do genoma baseada em 1).\n",
    "- **stop:** Posições final da variante. A partida deve ser menor ou igual à parada.\n",
    "- **reference_bases:** A base nucleotídica do alelo de referência e variante (por exemplo, 'C', 'A');\n",
    "- **variant_bases:** A base nucleotídica do alelo de referência e variante (por exemplo, 'C', 'A');\n",
    "- **representative_transcript:** ID da transcrição do conjunto e número da versão para uma transcrição conhecida do gene que contém a variante;\n",
    "- **ensembl_version:** Versão de compilação da anotação Ensembl.\n",
    "- **reference_build:** Versão de montagem de referência humana NCBI ou GRC.\n",
    "- **variant_summary:** Descrição curta da variante; (Explicação do gene e do cancêr);\n",
    "- **variant_origin:** Como a variante começou. (Somatic - Essas variantes surgem pontualmente em algumas células do corpo e não são passadas de pai para filho, causa mais comum de câncer e resultam de danos acumulados no DNA ao longo da vida, Rare Germline- Tipo menos comum de cancêr, está presente nas células germinativas(gametas:espermatozoides e óvulos) e é passada diretamente de pai pra fiho no momento da concepção);\n",
    "- **variant_types:** Os tipos de variantes são usados para classificar variantes por termos de Ontologia de Sequência. (missense_variant - Uma variante de sequência, que muda uma ou mais bases, resultando em uma sequência de aminoácidos diferente, mas onde o comprimento é preservado, stop_gained - Uma variante de sequência em que pelo menos uma base de um códon é alterada, resultando em um códon de parada prematuro, levando a um polipeptídeo encurtado, protein_altering_variant - Uma sequência_variante que é prevista para alterar a proteína codificada na sequência de codificação, frameshift_truncamento - Uma variante de deslocamento de quadro que faz com que o quadro de leitura translacional seja encurtado em relação ao recurso de referência, inframe_deletion - Uma variante não sinônima de quadro que exclui bases da sequência de codificação, inframe_insertion - Uma variante não sinônima do quadro que insere bases na sequência de codificação, splice_acceptor_variant - Uma variante que afeta o local aceitador de splicing de RNA (ou seja, a montante ou a montante/3prime de uma borda de exon), splice_donor_variant - Uma variante que afeta o local receptor do doador de RNA (ou seja, a jusante/5prime de uma borda de exon), gene_variant - Uma variante de sequência em que a estrutura do gene é alterada, transcrição_variante - Uma variante de sequência que altera a estrutura da transcrição, loss_of_function_variant - Uma variante de sequência pela qual o produto do gene diminuiu ou aboliu a função, gain_of_function_variant - Uma variante de sequência pela qual uma função nova ou aprimorada é conferida ao produto gênico);\n",
    "- **hgvs_expressions:** Strings HGVS definidas pelo usuário seguindo a nomenclatura HGVS que representam esta Variante no nível de DNA, RNA ou proteína;\n",
    "- **civic_variant_evidence_score:** \n",
    "- **allele_registry_id:** Identificador de registro de alelo (id de CA) vinculado à página de registro de alelo ClinGen correspondente. Este link é gerado automaticamente usando as coordenadas primárias selecionadas (cromossomo, início, parada, base de referência, base variante).\n",
    "- **clinvar_ids:** ID ClinVar definido pelo usuário referenciando esta variante que será vinculada diretamente ao ClinVar. Um valor de “Nenhum especificado” indica que a variante não foi avaliada para uma ID ClinVar. Um valor de “None Found” indica que foi feita uma tentativa de encontrar uma entrada ClinVar correspondente, mas não existe nenhuma. Um valor de “N/A” indica que um registro ClinVar não é aplicável à variante\n",
    "- **variant_aliases:** Nomes alternativos para esta variante. Podem ser versões mais detalhadas (por exemplo, 'Val600Glu'), IDs dbSNP ou nomenclatura alternativa usada na literatura.\n",
    "- **description:** Descrição do gene e cancêr que será tratado."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "gene                             object\n",
       "entrez_id                         int64\n",
       "variant                          object\n",
       "disease                          object\n",
       "doid                            float64\n",
       "phenotypes                       object\n",
       "drugs                            object\n",
       "drug_interaction_type            object\n",
       "evidence_type                    object\n",
       "evidence_direction               object\n",
       "evidence_level                   object\n",
       "clinical_significance            object\n",
       "evidence_statement               object\n",
       "citation_id                       int64\n",
       "source_type                      object\n",
       "citation                         object\n",
       "rating                          float64\n",
       "evidence_status                  object\n",
       "evidence_id                       int64\n",
       "variant_id                        int64\n",
       "gene_id                           int64\n",
       "chromosome                       object\n",
       "start                           float64\n",
       "stop                            float64\n",
       "reference_bases                  object\n",
       "variant_bases                    object\n",
       "representative_transcript        object\n",
       "ensembl_version                 float64\n",
       "reference_build                  object\n",
       "variant_summary                  object\n",
       "variant_origin                   object\n",
       "is_flagged                         bool\n",
       "variant_types                    object\n",
       "hgvs_expressions                 object\n",
       "civic_variant_evidence_score    float64\n",
       "allele_registry_id               object\n",
       "clinvar_ids                      object\n",
       "variant_aliases                  object\n",
       "description                      object\n",
       "dtype: object"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Verificando o tipo dos dados.\n",
    "civic.dtypes"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "A maior parte dos nossos dados está no formato \"object\", o que indica que antes do treinamento será necessário um tratamento para transformá-los em valores numéricos, tambem possuimos valores como \"bool\", \"int64\" e \"float64\""
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "gene                               0\n",
       "entrez_id                          0\n",
       "variant                            0\n",
       "disease                           23\n",
       "doid                              32\n",
       "phenotypes                      3389\n",
       "drugs                           1448\n",
       "drug_interaction_type           3278\n",
       "evidence_type                      0\n",
       "evidence_direction               179\n",
       "evidence_level                     0\n",
       "clinical_significance            222\n",
       "evidence_statement                 0\n",
       "citation_id                        0\n",
       "source_type                        0\n",
       "citation                           0\n",
       "rating                            68\n",
       "evidence_status                    0\n",
       "evidence_id                        0\n",
       "variant_id                         0\n",
       "gene_id                            0\n",
       "chromosome                       435\n",
       "start                            436\n",
       "stop                             436\n",
       "reference_bases                 2000\n",
       "variant_bases                   2055\n",
       "representative_transcript        466\n",
       "ensembl_version                  475\n",
       "reference_build                  426\n",
       "variant_summary                 2510\n",
       "variant_origin                   348\n",
       "is_flagged                         0\n",
       "variant_types                    632\n",
       "hgvs_expressions                1954\n",
       "civic_variant_evidence_score       0\n",
       "allele_registry_id              1954\n",
       "clinvar_ids                     1832\n",
       "variant_aliases                 1501\n",
       "description                      759\n",
       "dtype: int64"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Verificando valores nulo\n",
    "civic.isnull().sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "gene                             332\n",
       "entrez_id                        332\n",
       "variant                         1184\n",
       "disease                          237\n",
       "doid                             233\n",
       "phenotypes                       162\n",
       "drugs                            607\n",
       "drug_interaction_type              3\n",
       "evidence_type                      6\n",
       "evidence_direction                 2\n",
       "evidence_level                     5\n",
       "clinical_significance             15\n",
       "evidence_statement              3675\n",
       "citation_id                     1705\n",
       "source_type                        2\n",
       "citation                        1695\n",
       "rating                             5\n",
       "evidence_status                    1\n",
       "evidence_id                     3897\n",
       "variant_id                      1533\n",
       "gene_id                          332\n",
       "chromosome                        23\n",
       "start                            983\n",
       "stop                             988\n",
       "reference_bases                   54\n",
       "variant_bases                     42\n",
       "representative_transcript        333\n",
       "ensembl_version                    3\n",
       "reference_build                    2\n",
       "variant_summary                  239\n",
       "variant_origin                     4\n",
       "is_flagged                         2\n",
       "variant_types                     95\n",
       "hgvs_expressions                 749\n",
       "civic_variant_evidence_score     176\n",
       "allele_registry_id               751\n",
       "clinvar_ids                      479\n",
       "variant_aliases                  887\n",
       "description                       61\n",
       "dtype: int64"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Consultando se há dados duplicados\n",
    "civic.nunique()"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Ao consultar a quantidade de dados duplicados em nossa base, foi constatado que há diversos valores repetidos. Isso pode ser explicado pelo fato de que várias pessoas podem apresentar o mesmo tipo de câncer em um determinado gene, além de serem tratadas com a mesma droga, entre outras situações que podem gerar informações similares."
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Bibliografia:**\n",
    "\n",
    "https://docs.civicdb.org/en/latest/api.html#v1-api-deprecated https://civic.readthedocs.io/en/latest/model/variants/overview.html https://civic.readthedocs.io/en/latest/model/assertions/overview.html https://civic.readthedocs.io/en/latest/model/evidence.html https://civic.readthedocs.io/en/latest/model/genes.html https://civic.readthedocs.io/en/latest/model/evidence/type.html#evidence-type https://civic.readthedocs.io/en/latest/model/evidence/significance.html#evidence-significance"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Análise Exploratória"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [],
   "source": [
    "#bibliotecas necessárias para os gráficos\n",
    "import seaborn as sns\n",
    "import matplotlib.pyplot as plt\n",
    "import plotly.express as px"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Análise Gráfica Exploratória das Principais Variáveis"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#gráfico de genes com maior incidência\n",
    "sns.barplot(x = civic['gene'].value_counts()[:15],\n",
    "            y = civic['gene'].value_counts()[:15].index)\n",
    "plt.title(\"Genes com maior incidência\")\n",
    "plt.xlabel(\"Número de vezes relatados\")\n",
    "plt.ylabel(\"Gene\")\n",
    "plt.show()"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Esse gráfico tem como objetivo mostrar os 15 primeiros genes com maior incidência, dessa forma podemos ver qual gene sofre mutação com mais frequência. \n",
    "\n",
    "OBS: O gene VHL resposavel pelo câncer renal secundário está em primeiro lugar na lista enquanto o gene TP53 responsavel pelo câncer de pele esta em 10° lugar e é o mais frequente no Brasil enquanto o de pulmão que é o mais comum no mundo e está em 21° lugar."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#gráfico de variantes com maior incidência\n",
    "sns.barplot(x = civic['variant'].value_counts()[:15],\n",
    "            y = civic['variant'].value_counts()[:15].index)\n",
    "plt.title(\"Variantes com maior incidência\")\n",
    "plt.xlabel(\"Número de vezes relatados\")\n",
    "plt.ylabel(\"Variantes\")\n",
    "plt.show()"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Nossa base de dados esta repleta de tipos de variants, nesse gráfico aptamos por mostrar apenas as 15° com maior incidência para uma melhor visualização."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "\n",
    "#gráfico de doenças com maior incidência\n",
    "sns.barplot(x = civic['disease'].value_counts()[:15],\n",
    "            y = civic['disease'].value_counts()[:15].index)\n",
    "plt.title(\"Doenças com Maior incidência\")\n",
    "plt.xlabel(\"Número de vezes relatados\")\n",
    "plt.ylabel(\"Doenças\")\n",
    "plt.show()"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "\n",
    "Assim como o gráfico de Variant optamos por mostrar apenas 15 itens pra melhor visualização. \n",
    "\n",
    "OBS: Podemos observar, por exemplo, que a doença Von Hippel-Lindau Disease esta diretamente ligada ao gene VHL, por isso ambos esta em primeiro lugar e com maior incidência."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#gráfico de drogas com maior incidência\n",
    "sns.barplot(x = civic['drugs'].value_counts()[:20],\n",
    "            y = civic['drugs'].value_counts()[:20].index)\n",
    "plt.title(\"Tipo de Drogas com Maior incidência\")\n",
    "plt.xlabel(\"Número de vezes relatados\")\n",
    "plt.ylabel(\"Drogas\")\n",
    "plt.show()"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Em nossa base observamos que varias drogas foram usadas durante os tratamentos, nesse gráfico plotamos apenas 20 das drogas mais usadas."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#gráfico de evidências\n",
    "sns.barplot(x = civic['evidence_level'].value_counts()[:5],\n",
    "            y = civic['evidence_level'].value_counts()[:5].index)\n",
    "plt.title(\"Evidências\")\n",
    "plt.xlabel(\"Número de vezes relatados\")\n",
    "plt.ylabel(\"Nivel de Evidencias\")\n",
    "plt.show()"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Aqui visualizarmos a robustez do estudo suportado pela evidência, podemos ver que \"C - Estudo de caso\" possui a maior incidência com 200 itens a mais e \"E - Associanção inferencial\" possui a menor quantidade com menos de 200 itens."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#Gráfico de origem das variantes\n",
    "sns.barplot(x = civic['variant_origin'].value_counts()[:3],\n",
    "            y = civic['variant_origin'].value_counts()[:3].index)\n",
    "plt.title(\"incidência de origem de Variantes\")\n",
    "plt.xlabel(\"Número de vezes relatados\")\n",
    "plt.ylabel(\"Origem da Variante\")\n",
    "plt.show()"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Como a variante começou ?\n",
    "\n",
    "Nesse gráfico visualizamos que a maior parte das doenças cancerigenas começaram como somaticas, ou seja que surgem pontualmente em alguma célula do corpo e a menor parte como common gemline, ou seja presente nas células germinativas, cancer que pode ser transferido de pais para filhos."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#Gráfico de resposta da evidência\n",
    "sns.barplot(x = civic['clinical_significance'].value_counts()[:6],\n",
    "            y = civic['clinical_significance'].value_counts()[:6].index)\n",
    "plt.title(\"Tipo de resposta da evicência\")\n",
    "plt.xlabel(\"Número de vezes relatados\")\n",
    "plt.ylabel(\"Significancia\")\n",
    "plt.show()"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Como o perfil molecular reagiu ao tratamento ?\n",
    "\n",
    "A maior parte teve uma resposta \"Sensitivity/Responde\", significa que o organismo respondeu ao estímulo, isso não quer dizer que a resposta foi possitiva ou negativa, apenas indica que ouve uma minima resposta. A menor parte \"Better Outcome\", ou seja, Demosntra resultado clínico melhor do que o esperado."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#Gráfico das incidências\n",
    "sns.barplot(x = civic['rating'].value_counts()[:5],\n",
    "            y = civic['rating'].value_counts()[:5].index)\n",
    "plt.title(\"Ranking com maior incidência\")\n",
    "plt.xlabel(\"Número de vezes relatados\")\n",
    "plt.ylabel(\"Rating\")\n",
    "plt.show()"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Esse gráfico serve para nos mostrar rapidamente o quão bem foi o tratamento escolhido. A maior parte dos tratamentos receberam 5 \"estrelas\", forma como foram avaliados, tendo eviências fortes e bem fundamentadas com resultados limpos. A menor parte recebeu 1 \"estrela\", significando que a alegação não foi bem sucedida, os resultados são sujos e não reprodutíveis ou têm um tamanho de amostra muito pequeno."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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abM9E0V/Tfn5+MDExwYIFC9T+wl66dCmysrI0OpbKULS34N+1Z2VllevLsHv37rh58yZ++eUXqS0/Px8xMTGwtLSEr6+v5gt+jq5du8LKygpRUVF4/Pix2rKy9oB0794d+fn5apedFxQUICYmRq2fnZ0dOnbsiO+//x43btwotp6SLquurJoNDQ2hUChQUFAgtV25cgUJCQkvXAPpHu6RIa1xc3PDqlWr8O6776Jx48Zqd/Y9ePAg1qxZo9FnyHh7eyMkJARLlixBZmYmfH19ceTIESxfvhx9+vRBp06dNPZes2bNQkBAAHx8fDBkyBA8evQIMTExUKlUiIyMBPD0/KA6deqgX79+8Pb2hqWlJbZv346jR49i9uzZAJ7+NTtjxgx88MEH8PX1RVBQEG7duoX58+fD1dUVo0eP1ljNEydOxM8//4yAgACMGjUKtra2WL58OdLS0rB27doy7x5bv359fPHFF/j666/Rvn179O3bF0qlEkePHoWjoyOioqJQs2ZNREREYMqUKejWrRt69eqF1NRULFq0CC1btsR7772nsbFUhq5du8LExASBgYH46KOPkJOTgx9++AF2dnYlflH/27Bhw/D9998jNDQUx44dg6urK3777TccOHAA8+bNe+GT3jXB2toac+fOxdChQ9GyZUvpnksnT57Ew4cPi93jpkhgYCDatWuHiRMn4sqVK/Dw8EB8fHyJ55rExsbijTfegJeXFz788EPUq1cPt27dwqFDh/C///0PJ0+erJKae/TogTlz5qBbt24YOHAgMjIyEBsbi/r16+PUqVMvVAPpHgYZ0qpevXrh1KlTmDVrFtavX4/FixdDqVSiadOmmD17Nj788EONvt+PP/6IevXqYdmyZVi3bh1q1aqFiIgI6eRDTfHz88OWLVswefJkTJo0CcbGxvD19cWMGTOkEznNzc3x8ccfY+vWrYiPj0dhYSHq16+PRYsWqV0REhoaCnNzc0RHR2PChAnSjcJmzJgBGxsbjdVsb2+PgwcPYsKECYiJicHjx4/RtGlT/P777+XaWzJ16lTUrVsXMTEx+OKLL2Bubo6mTZvi/fffl/pERkaiZs2aWLhwIUaPHg1bW1sMGzYM06dP1/m7sbq7u+O3337Dl19+iXHjxqFWrVoYMWIEatasWeyKp2eZmZlh9+7dmDhxIpYvX47s7Gy4u7sjLi6uUh/4+DxDhgyBnZ0doqOj8fXXX8PY2BiNGjUqMyAbGBhgw4YNCA8Px4oVK6BQKNCrVy/Mnj0bzZo1U+vr4eGBpKQkTJkyBcuWLcPdu3dhZ2eHZs2aYdKkSVVWc+fOnbF06VJER0cjPDwcdevWxYwZM3DlyhUGGT2gELp8th8RERFRGXiODBEREckWgwwRERHJFoMMERERyRaDDBEREckWgwwRERHJFoMMERERyZbe30emsLAQf//9N6ysrMr9jB0iIiLSLiEEHjx4AEdHxzJvyKn3Qebvv/8u9lRbIiIikof09HTUqVOn1OV6H2SKbv2dnp4Oa2trLVdDRERE5ZGdnQ0nJ6fnPsJD74NM0eEka2trBhkiIiKZed5pITzZl4iIiGRL7/fIFOnw5c8wVJppuwwiIiK9cWzWIG2XwD0yREREJF8MMkRERCRbDDJEREQkWwwyREREJFsMMkRERCRbDDJEREQkWwwyREREJFsMMkRERCRbDDJEREQkWzodZBYvXoymTZtKz0ny8fHBH3/8oe2yiIiISEfodJCpU6cOoqOjcezYMSQlJaFz587o3bs3/vrrL22XRkRERDpAp5+1FBgYqDY/bdo0LF68GH/++SeaNGmipaqIiIhIV+h0kPm3goICrFmzBrm5ufDx8dF2OURERKQDdD7InD59Gj4+Pnj8+DEsLS2xbt06eHh4lNo/Ly8PeXl50nx2dnZVlElERERaoNPnyACAu7s7kpOTcfjwYYwYMQIhISE4c+ZMqf2joqKgUqmkycnJqQqrJSIioqqkEEIIbRfxIvz8/ODm5obvv/++xOUl7ZFxcnKC9yffwVBpVlVlEhER6b1jswZV2rqzs7OhUqmQlZUFa2vrUvvp/KGlZxUWFqoFlWcplUoolcoqrIiIiIi0RaeDTEREBAICAuDs7IwHDx5g1apV2L17NxITE7VdGhEREekAnQ4yGRkZGDRoEG7cuAGVSoWmTZsiMTERb775prZLIyIiIh2g00Fm6dKl2i6BiIiIdJjOX7VEREREVBoGGSIiIpItBhkiIiKSLQYZIiIiki0GGSIiIpItBhkiIiKSLQYZIiIiki0GGSIiIpItnb4hnibt/SaozIdOERERkfxwjwwRERHJFoMMERERyRaDDBEREckWgwwRERHJFoMMERERyRaDDBEREckWgwwRERHJ1itzH5n06DawMjXUdhlERKVynnRa2yUQyQ73yBAREZFsMcgQERGRbDHIEBERkWwxyBAREZFsMcgQERGRbDHIEBERkWwxyBAREZFsMcgQERGRbDHIEBERkWzpfJBxdXWFQqEoNoWFhWm7NCIiItIynX9EwdGjR1FQUCDNp6Sk4M0338Q777yjxaqIiIhIF+h8kKlZs6bafHR0NNzc3ODr66ulioiIiEhX6PyhpX978uQJVqxYgcGDB0OhUGi7HCIiItIynd8j828JCQnIzMxEaGhoqX3y8vKQl5cnzWdnZ1dBZURERKQNstojs3TpUgQEBMDR0bHUPlFRUVCpVNLk5ORUhRUSERFRVZJNkLl69Sq2b9+OoUOHltkvIiICWVlZ0pSenl5FFRIREVFVk82hpbi4ONjZ2aFHjx5l9lMqlVAqlVVUFREREWmTLPbIFBYWIi4uDiEhITAykk32IiIiokomiyCzfft2XLt2DYMHD9Z2KURERKRDZLF7o2vXrhBCaLsMIiIi0jGy2CNDREREVBIGGSIiIpItBhkiIiKSLQYZIiIiki0GGSIiIpItBhkiIiKSLQYZIiIiki0GGSIiIpItWdwQTxOcJv4Ja2trbZdBREREGsQ9MkRERCRbDDJEREQkWwwyREREJFsMMkRERCRbDDJEREQkWwwyREREJFsMMkRERCRbr8x9ZN787k0Ymb0ywyWShQOfHNB2CUQkc9wjQ0RERLLFIENERESyxSBDREREssUgQ0RERLLFIENERESyxSBDREREssUgQ0RERLLFIENERESyxSBDREREsqXzQebBgwcIDw+Hi4sLzMzM0LZtWxw9elTbZREREZEO0PkgM3ToUGzbtg3//e9/cfr0aXTt2hV+fn64fv26tksjIiIiLdPpIPPo0SOsXbsWM2fORIcOHVC/fn1ERkaifv36WLx4sbbLIyIiIi3T6SCTn5+PgoICmJqaqrWbmZlh//79WqqKiIiIdIVOBxkrKyv4+Pjg66+/xt9//42CggKsWLEChw4dwo0bN0p8TV5eHrKzs9UmIiIi0k86HWQA4L///S+EEKhduzaUSiUWLFiAoKAgGBiUXHpUVBRUKpU0OTk5VXHFREREVFV0Psi4ublhz549yMnJQXp6Oo4cOYJ//vkH9erVK7F/REQEsrKypCk9Pb2KKyYiIqKqYqTtAsrLwsICFhYWuH//PhITEzFz5swS+ymVSiiVyiqujoiIiLRB54NMYmIihBBwd3fHxYsXMX78eDRq1AgffPCBtksjIiIiLdP5Q0tZWVkICwtDo0aNMGjQILzxxhtITEyEsbGxtksjIiIiLdP5PTL9+/dH//79tV0GERER6SCd3yNDREREVBoGGSIiIpItBhkiIiKSLQYZIiIiki0GGSIiIpItBhkiIiKSLQYZIiIiki0GGSIiIpItnb8hnqZsG74N1tbW2i6DiIiINIh7ZIiIiEi2GGSIiIhIthhkiIiISLYYZIiIiEi2GGSIiIhIthhkiIiISLYYZIiIiEi2Xpn7yOzvFgALo1dmuFSJfPfu0XYJRET0/3GPDBEREckWgwwRERHJFoMMERERyRaDDBEREckWgwwRERHJFoMMERERyRaDDBEREckWgwwRERHJFoMMERERyZZWg8zevXsRGBgIR0dHKBQKJCQkqC1XKBQlTrNmzdJOwURERKRTtBpkcnNz4e3tjdjY2BKX37hxQ2366aefoFAo8Pbbb1dxpURERKSLtPrwoYCAAAQEBJS6vFatWmrz69evR6dOnVCvXr3KLo2IiIhkQDZPUbx16xY2bdqE5cuXl9kvLy8PeXl50nx2dnZll0ZERERaIpuTfZcvXw4rKyv07du3zH5RUVFQqVTS5OTkVEUVEhERUVWTTZD56aefEBwcDFNT0zL7RUREICsrS5rS09OrqEIiIiKqarI4tLRv3z6kpqbil19+eW5fpVIJpVJZBVURERGRtslij8zSpUvRvHlzeHt7a7sUIiIi0iFa3SOTk5ODixcvSvNpaWlITk6Gra0tnJ2dATw9WXfNmjWYPXu2tsokIiIiHaXVIJOUlIROnTpJ82PGjAEAhISEYNmyZQCA1atXQwiBoKAgbZRIREREOkwhhBDaLqIyZWdnQ6VSYZNPW1gYyeKUINJxvnv3aLsEIiK9V/T9nZWVBWtr61L7yeIcGSIiIqKSMMgQERGRbDHIEBERkWwxyBAREZFsMcgQERGRbL3UZTy3b99GamoqAMDd3R01a9bUSFFERERE5VGhPTK5ubkYPHgwHB0d0aFDB3To0AGOjo4YMmQIHj58qOkaiYiIiEpUoSAzZswY7NmzBxs2bEBmZiYyMzOxfv167NmzB2PHjtV0jUREREQlqtAN8WrUqIHffvsNHTt2VGvftWsX+vfvj9u3b2uqvpdW3hvqEBERke6o1BviPXz4EPb29sXa7ezseGiJiIiIqkyFgoyPjw8mT56Mx48fS22PHj3ClClT4OPjo7HiiIiIiMpSoauW5s+fD39/f9SpUwfe3t4AgJMnT8LU1BSJiYkaLZCIiIioNBV+aOTDhw+xcuVKnDt3DgDQuHFjBAcHw8zMTKMFviyeI0NERCQ/5f3+rvB9ZMzNzfHhhx9W9OVEREREL63CQebvv//G/v37kZGRgcLCQrVlo0aNeunCiIiIiJ6nQkFm2bJl+Oijj2BiYoLq1atDoVBIyxQKBYMMERERVYkKnSPj5OSE4cOHIyIiAgYGuv24pqJjbDPDVsNMaa7tckhmRs4O1HYJRESvpEq/j8yAAQN0PsQQERGRfqtQEhkyZAjWrFmj6VqIiIiIXkiFzpGJiopCz549sWXLFnh5ecHY2Fht+Zw5czRSHBEREVFZKhxkEhMT4e7uDgDFTvYlIiIiqgoVCjKzZ8/GTz/9hNDQUA2XQ0RERFR+FTpHRqlUol27dpquhYiIiOiFVCjIfPrpp4iJidF0LUREREQvpEKHlo4cOYKdO3di48aNaNKkSbGTfePj4zVSHBEREVFZKhRkbGxs0LdvX03XQkRERPRCKhRk4uLiNPLme/fuxaxZs3Ds2DHcuHED69atQ58+fQAA//zzD7788kts3rwZly9fhkqlgp+fH6Kjo+Ho6KiR9yciIiJ5e6lb896+fRv79+/H/v37cfv27Rd+fW5uLry9vREbG1ts2cOHD3H8+HF89dVXOH78OOLj45GamopevXq9TMlERESkRyq0RyY3NxeffPIJ/vOf/0hPvjY0NMSgQYMQExMDc/PyPdMoICAAAQEBJS5TqVTYtm2bWtvChQvRqlUrXLt2Dc7OzhUpnYiIiPRIhfbIjBkzBnv27MHvv/+OzMxMZGZmYv369dizZw/Gjh2r6RolWVlZUCgUsLGxKbVPXl4esrOz1SYiIiLSTxUKMmvXrsXSpUsREBAAa2trWFtbo3v37vjhhx/w22+/abpGAMDjx48xYcIEBAUFlfkUzKioKKhUKmlycnKqlHqIiIhI+yr89Gt7e/ti7XZ2dnj48OFLF/Wsf/75B/3794cQAosXLy6zb0REBLKysqQpPT1d4/UQERGRbqhQkPHx8cHkyZPx+PFjqe3Ro0eYMmUKfHx8NFYc8H8h5urVq9i2bVuZe2OAp3cdLtpLVDQRERGRfqrQyb7z58+Hv78/6tSpA29vbwDAyZMnYWpqisTERI0VVxRiLly4gF27dqF69eoaWzcRERHJX4WCjKenJy5cuICVK1fi3LlzAICgoCAEBwfDzMys3OvJycnBxYsXpfm0tDQkJyfD1tYWDg4O6NevH44fP46NGzeioKAAN2/eBADY2trCxMSkIqUTERGRHlEIIYS23nz37t3o1KlTsfaQkBBERkaibt26Jb5u165d6NixY7neIzs7GyqVCjPDVsNMWb7LwomKjJwdqO0SiIheSUXf31lZWWWeJlKhPTLLly9HjRo10KNHDwDAZ599hiVLlsDDwwM///wzXFxcyrWejh07oqwcpcWMRURERDJQoZN9p0+fLh1COnToEBYuXIiZM2eiRo0aGD16tEYLJCIiIipNhfbIpKeno379+gCAhIQE9OvXD8OGDUO7du3KfciHiIiI6GVVaI+MpaUl7t69CwDYunUr3nzzTQCAqakpHj16pLnqiIiIiMpQoT0yb775JoYOHYpmzZrh/Pnz6N69OwDgr7/+gqurqybrIyIiIipVhfbIxMbGwsfHB7dv38batWul+7scO3YMQUFBGi2QiIiIqDQV2iNjY2ODhQsXFmufMmXKSxdEREREVF4VCjIAkJmZiaVLl+Ls2bMAgCZNmmDw4MFQqVQaK46IiIioLBU6tJSUlAQ3NzfMnTsX9+7dw7179zBnzhy4ubnh+PHjmq6RiIiIqEQVurNv+/btUb9+ffzwww8wMnq6Uyc/Px9Dhw7F5cuXsXfvXo0XWlHlvTMgERER6Y7yfn9XKMiYmZnhxIkTaNSokVr7mTNn0KJFCzx8+PDFK64kDDJERETyU97v7wodWrK2tsa1a9eKtaenp8PKyqoiqyQiIiJ6YRUKMu+++y6GDBmCX375Benp6UhPT8fq1asxdOhQXn5NREREVaZCVy19++23UCgUGDRoEPLz8wEAxsbGGDFiBKKjozVaIBEREVFpKnSOTJGHDx/i0qVLAAA3NzeYm5trrDBN4TkyRERE8lPe7+8K30cGAMzNzeHl5fUyqyAiIiKqsAoFmcePHyMmJga7du1CRkYGCgsL1ZbzXjJERERUFSoUZIYMGYKtW7eiX79+aNWqFRQKhabr0rhZH74PU2NjbZdBMvHFit+0XQIREZVDhYLMxo0bsXnzZrRr107T9RARERGVW4Uuv65duzbvF0NERERaV6EgM3v2bEyYMAFXr17VdD1ERERE5VahQ0stWrTA48ePUa9ePZibm8P4mXNP7t27p5HiiIiIiMpSoSATFBSE69evY/r06bC3t5fFyb5ERESkfyoUZA4ePIhDhw7B29tb0/UQERERlVuFzpFp1KgRHj16pOlaiIiIiF5IhYJMdHQ0xo4di927d+Pu3bvIzs5Wm4iIiIiqQoUOLXXr1g0A0KVLF7V2IQQUCgUKCgpevjIiIiKi56hQkNm1a5em6yhRVFQU4uPjce7cOZiZmaFt27aYMWMG3N3dq+T9iYiISLdVKMj4+vpquo4S7dmzB2FhYWjZsiXy8/Px+eefo2vXrjhz5gwsLCyqpAYiIiLSXRV++nVmZiaWLl2Ks2fPAgCaNGmCwYMHQ6VSaay4LVu2qM0vW7YMdnZ2OHbsGDp06KCx9yEiIiJ5qtDJvklJSXBzc8PcuXNx79493Lt3D3PmzIGbm1ulPvk6KysLAGBra1tqn7y8PJ58TERE9IqoUJAZPXo0evXqhStXriA+Ph7x8fFIS0tDz549ER4eruESnyosLER4eDjatWsHT0/PUvtFRUVBpVJJk5OTU6XUQ0RERNpX4T0yEyZMgJHR/x2ZMjIywmeffYakpCSNFfdvYWFhSElJwerVq8vsFxERgaysLGlKT0+vlHqIiIhI+yp0joy1tTWuXbuGRo0aqbWnp6dXylOxR44ciY0bN2Lv3r2oU6dOmX2VSiWUSqXGayAiIiLdU6E9Mu+++y6GDBmCX375Benp6UhPT8fq1asxdOhQBAUFaaw4IQRGjhyJdevWYefOnahbt67G1k1ERETyV6E9Mt9++y0UCgUGDRqE/Px8AICxsTFGjBiB6OhojRUXFhaGVatWYf369bCyssLNmzcBACqVCmZmZhp7HyIiIpInhRBCvMgLCgoKcODAAXh5eUGpVOLSpUsAADc3N5ibm2u2uFKeqh0XF4fQ0NByrSM7OxsqlQpf9u8FU2NjDVZH+uyLFb9puwQiolda0fd3VlYWrK2tS+33wntkDA0N0bVrV5w9exZ169aFl5fXSxValhfMWERERPSKqdA5Mp6enrh8+bKmayEiIiJ6IRUKMt988w3GjRuHjRs34saNG7wBHREREWlFhU727d69OwCgV69eauex8OnXREREVJV0+unXRERERGXR6adfExEREZXlpZ5+feTIEWRkZKCwsFBt2aBBg166MCIiIqLnqVCQ+f333xEcHIycnBxYW1urnSdTdKM8IiIiospWoauWxo4di8GDByMnJweZmZm4f/++NN27d0/TNRIRERGV6IXv7AsAFhYWOH36NOrVq1cZNWlUee8MSERERLqjvN/fFdoj4+/vj6SkpAoXR0RERKQJ5T5HZsOGDdK/e/TogfHjx+PMmTPw8vKC8TPPMOrVq5fmKiQiIiIqRbkPLRkYlG/nja7dEI+HloiIiORH4w+NfPYSayIiIiJte6FzZHbu3AkPD48Sn6eUlZWFJk2aYN++fRorjoiIiKgsLxRk5s2bhw8//LDEXTwqlQofffQR5syZo7HiiIiIiMryQkHm5MmT6NatW6nLu3btimPHjr10UURERETl8UJ39r1161axK5TUVmZkhNu3b790UZUhddYeWJpaaLsMegGNv+is7RKIiEjHvdAemdq1ayMlJaXU5adOnYKDg8NLF0VERERUHi8UZLp3746vvvoKjx8/Lrbs0aNHmDx5Mnr27Kmx4oiIiIjK8kKHlr788kvEx8ejYcOGGDlyJNzd3QEA586dQ2xsLAoKCvDFF19USqFEREREz3qhIGNvb4+DBw9ixIgRiIiIQNG99BQKBfz9/REbGwt7e/tKKZSIiIjoWS8UZADAxcUFmzdvxv3793Hx4kUIIdCgQQNUq1atMuojIiIiKtULB5ki1apVQ8uWLTVZCxEREdELqdDTr4mIiIh0AYMMERERyRaDDBEREcmWrIJMdHQ0FAoFwsPDtV0KERER6QDZBJmjR4/i+++/R9OmTbVdChEREekIWQSZnJwcBAcH44cffuBl3kRERCSRRZAJCwtDjx494Ofn99y+eXl5yM7OVpuIiIhIP1X4PjJVZfXq1Th+/DiOHj1arv5RUVGYMmVKJVdFREREukCn98ikp6fj008/xcqVK2Fqalqu10RERCArK0ua0tPTK7lKIiIi0had3iNz7NgxZGRk4PXXX5faCgoKsHfvXixcuBB5eXkwNDRUe41SqYRSqazqUomIiEgLdDrIdOnSBadPn1Zr++CDD9CoUSNMmDChWIghIiKiV4tOBxkrKyt4enqqtVlYWKB69erF2omIiOjVo9PnyBARERGVRaf3yJRk9+7d2i6BiIiIdAT3yBAREZFsMcgQERGRbDHIEBERkWwxyBAREZFsMcgQERGRbDHIEBERkWwxyBAREZFsMcgQERGRbMnuhngV5T7eF9bW1toug4iIiDSIe2SIiIhIthhkiIiISLYYZIiIiEi2GGSIiIhIthhkiIiISLYYZIiIiEi2GGSIiIhItl6Z+8hERUVBqVRquwxZiIyM1HYJRERE5cI9MkRERCRbDDJEREQkWwwyREREJFsMMkRERCRbDDJEREQkWwwyREREJFsMMkRERCRbDDJEREQkWwwyREREJFtaDTJ79+5FYGAgHB0doVAokJCQoLZcCIFJkybBwcEBZmZm8PPzw4ULF7RTLBEREekcrQaZ3NxceHt7IzY2tsTlM2fOxIIFC/Ddd9/h8OHDsLCwgL+/Px4/flzFlRIREZEu0uqzlgICAhAQEFDiMiEE5s2bhy+//BK9e/cGAPznP/+Bvb09EhISMGDAgKoslYiIiHSQzp4jk5aWhps3b8LPz09qU6lUaN26NQ4dOlTq6/Ly8pCdna02ERERkX7S2SBz8+ZNAIC9vb1au729vbSsJFFRUVCpVNLk5ORUqXUSERGR9uhskKmoiIgIZGVlSVN6erq2SyIiIqJKorNBplatWgCAW7duqbXfunVLWlYSpVIJa2trtYmIiIj0k84Gmbp166JWrVrYsWOH1JadnY3Dhw/Dx8dHi5URERGRrtDqVUs5OTm4ePGiNJ+Wlobk5GTY2trC2dkZ4eHh+Oabb9CgQQPUrVsXX331FRwdHdGnTx/tFU1EREQ6Q6tBJikpCZ06dZLmx4wZAwAICQnBsmXL8NlnnyE3NxfDhg1DZmYm3njjDWzZsgWmpqbaKpmIiIh0iFaDTMeOHSGEKHW5QqHA1KlTMXXq1CqsioiIiORCZ8+RISIiInoeBhkiIiKSLQYZIiIiki0GGSIiIpItBhkiIiKSLQYZIiIiki0GGSIiIpItBhkiIiKSLYUo6450eiA7OxsqlQpZWVl8gCQREZFMlPf7m3tkiIiISLYYZIiIiEi2GGSIiIhIthhkiIiISLYYZIiIiEi2GGSIiIhItoy0XUBViV/XCebmhtouQ2f1f+eItksgIiJ6YdwjQ0RERLLFIENERESyxSBDREREssUgQ0RERLLFIENERESyxSBDREREssUgQ0RERLLFIENERESyxSBDREREssUgQ0RERLKl80Fm7969CAwMhKOjIxQKBRISErRdEhEREekInQ8yubm58Pb2RmxsrLZLISIiIh2j8w+NDAgIQEBAgLbLICIiIh2k80HmReXl5SEvL0+az87O1mI1REREVJl0/tDSi4qKioJKpZImJycnbZdERERElUTvgkxERASysrKkKT09XdslERERUSXRu0NLSqUSSqVS22UQERFRFdC7PTJERET06tD5PTI5OTm4ePGiNJ+Wlobk5GTY2trC2dlZi5URERGRtul8kElKSkKnTp2k+TFjxgAAQkJCsGzZMi1VRURERLpA54NMx44dIYTQdhlERESkg3iODBEREckWgwwRERHJFoMMERERyRaDDBEREckWgwwRERHJFoMMERERyRaDDBEREckWgwwRERHJls7fEE9T+r61C9bW1toug4iIiDSIe2SIiIhIthhkiIiISLYYZIiIiEi2GGSIiIhIthhkiIiISLYYZIiIiEi2XpnLr9smbIehuYW2y6hyJ/v5a7sEIiKiSsM9MkRERCRbDDJEREQkWwwyREREJFsMMkRERCRbDDJEREQkWwwyREREJFsMMkRERCRbDDJEREQkWwwyREREJFsMMkRERCRbWg0ye/fuRWBgIBwdHaFQKJCQkKC2PDIyEo0aNYKFhQWqVasGPz8/HD58WDvFEhERkc7RapDJzc2Ft7c3YmNjS1zesGFDLFy4EKdPn8b+/fvh6uqKrl274vbt21VcKREREekirT40MiAgAAEBAaUuHzhwoNr8nDlzsHTpUpw6dQpdunSp7PKIiIhIx8nm6ddPnjzBkiVLoFKp4O3tXWq/vLw85OXlSfPZ2dlVUR4RERFpgc6f7Ltx40ZYWlrC1NQUc+fOxbZt21CjRo1S+0dFRUGlUkmTk5NTFVZLREREVUnng0ynTp2QnJyMgwcPolu3bujfvz8yMjJK7R8REYGsrCxpSk9Pr8JqiYiIqCrpfJCxsLBA/fr10aZNGyxduhRGRkZYunRpqf2VSiWsra3VJiIiItJPOh9knlVYWKh2DgwRERG9urR6sm9OTg4uXrwozaelpSE5ORm2traoXr06pk2bhl69esHBwQF37txBbGwsrl+/jnfeeUeLVRMREZGu0GqQSUpKQqdOnaT5MWPGAABCQkLw3Xff4dy5c1i+fDnu3LmD6tWro2XLlti3bx+aNGmirZKJiIhIh2g1yHTs2BFCiFKXx8fHV2E1REREJDeyO0eGiIiIqAiDDBEREckWgwwRERHJFoMMERERyRaDDBEREckWgwwRERHJFoMMERERyRaDDBEREcmWVm+IV5UO9vHjAySJiIj0DPfIEBERkWwxyBAREZFs6f2hpaJnOWVnZ2u5EiIiIiqvou/tsp7JCLwCQebu3bsAACcnJy1XQkRERC/qwYMHUKlUpS7X+yBja2sLALh27VqZH4S+yM7OhpOTE9LT0/X+5OZXaawAx6vvOF799SqNFdDceIUQePDgARwdHcvsp/dBxsDg6WlAKpXqlfgBKmJtbf3KjPdVGivA8eo7jld/vUpjBTQz3vLsgODJvkRERCRbDDJEREQkW3ofZJRKJSZPngylUqntUqrEqzTeV2msAMer7zhe/fUqjRWo+vEqxPOuayIiIiLSUXq/R4aIiIj0F4MMERERyRaDDBEREckWgwwRERHJll4HmdjYWLi6usLU1BStW7fGkSNHtF1ShezduxeBgYFwdHSEQqFAQkKC2nIhBCZNmgQHBweYmZnBz88PFy5cUOtz7949BAcHw9raGjY2NhgyZAhycnKqcBTlExUVhZYtW8LKygp2dnbo06cPUlNT1fo8fvwYYWFhqF69OiwtLfH222/j1q1ban2uXbuGHj16wNzcHHZ2dhg/fjzy8/OrcijlsnjxYjRt2lS6cZSPjw/++OMPabk+jbUk0dHRUCgUCA8Pl9r0acyRkZFQKBRqU6NGjaTl+jRWALh+/Tree+89VK9eHWZmZvDy8kJSUpK0XJ9+V7m6uhbbtgqFAmFhYQD0b9sWFBTgq6++Qt26dWFmZgY3Nzd8/fXXas9B0tr2FXpq9erVwsTERPz000/ir7/+Eh9++KGwsbERt27d0nZpL2zz5s3iiy++EPHx8QKAWLdundry6OhooVKpREJCgjh58qTo1auXqFu3rnj06JHUp1u3bsLb21v8+eefYt++faJ+/foiKCioikfyfP7+/iIuLk6kpKSI5ORk0b17d+Hs7CxycnKkPsOHDxdOTk5ix44dIikpSbRp00a0bdtWWp6fny88PT2Fn5+fOHHihNi8ebOoUaOGiIiI0MaQyrRhwwaxadMmcf78eZGamio+//xzYWxsLFJSUoQQ+jXWZx05ckS4urqKpk2bik8//VRq16cxT548WTRp0kTcuHFDmm7fvi0t16ex3rt3T7i4uIjQ0FBx+PBhcfnyZZGYmCguXrwo9dGn31UZGRlq23Xbtm0CgNi1a5cQQr+2rRBCTJs2TVSvXl1s3LhRpKWliTVr1ghLS0sxf/58qY+2tq/eBplWrVqJsLAwab6goEA4OjqKqKgoLVb18p4NMoWFhaJWrVpi1qxZUltmZqZQKpXi559/FkIIcebMGQFAHD16VOrzxx9/CIVCIa5fv15ltVdERkaGACD27NkjhHg6NmNjY7FmzRqpz9mzZwUAcejQISHE0+BnYGAgbt68KfVZvHixsLa2Fnl5eVU7gAqoVq2a+PHHH/V6rA8ePBANGjQQ27ZtE76+vlKQ0bcxT548WXh7e5e4TN/GOmHCBPHGG2+Uulzff1d9+umnws3NTRQWFurdthVCiB49eojBgwertfXt21cEBwcLIbS7ffXy0NKTJ09w7Ngx+Pn5SW0GBgbw8/PDoUOHtFiZ5qWlpeHmzZtqY1WpVGjdurU01kOHDsHGxgYtWrSQ+vj5+cHAwACHDx+u8ppfRFZWFoD/e/jnsWPH8M8//6iNt1GjRnB2dlYbr5eXF+zt7aU+/v7+yM7Oxl9//VWF1b+YgoICrF69Grm5ufDx8dHrsYaFhaFHjx5qYwP0c/teuHABjo6OqFevHoKDg3Ht2jUA+jfWDRs2oEWLFnjnnXdgZ2eHZs2a4YcffpCW6/PvqidPnmDFihUYPHgwFAqF3m1bAGjbti127NiB8+fPAwBOnjyJ/fv3IyAgAIB2t69ePjTyzp07KCgoUPsBAQB7e3ucO3dOS1VVjps3bwJAiWMtWnbz5k3Y2dmpLTcyMoKtra3URxcVFhYiPDwc7dq1g6enJ4CnYzExMYGNjY1a32fHW9LnUbRM15w+fRo+Pj54/PgxLC0tsW7dOnh4eCA5OVnvxgoAq1evxvHjx3H06NFiy/Rt+7Zu3RrLli2Du7s7bty4gSlTpqB9+/ZISUnRu7FevnwZixcvxpgxY/D555/j6NGjGDVqFExMTBASEqLXv6sSEhKQmZmJ0NBQAPr3cwwAEydORHZ2Nho1agRDQ0MUFBRg2rRpCA4OBqDd7yK9DDKkH8LCwpCSkoL9+/dru5RK5e7ujuTkZGRlZeG3335DSEgI9uzZo+2yKkV6ejo+/fRTbNu2Daamptoup9IV/bUKAE2bNkXr1q3h4uKCX3/9FWZmZlqsTPMKCwvRokULTJ8+HQDQrFkzpKSk4LvvvkNISIiWq6tcS5cuRUBAABwdHbVdSqX59ddfsXLlSqxatQpNmjRBcnIywsPD4ejoqPXtq5eHlmrUqAFDQ8NiZ4jfunULtWrV0lJVlaNoPGWNtVatWsjIyFBbnp+fj3v37uns5zFy5Ehs3LgRu3btQp06daT2WrVq4cmTJ8jMzFTr/+x4S/o8ipbpGhMTE9SvXx/NmzdHVFQUvL29MX/+fL0c67Fjx5CRkYHXX38dRkZGMDIywp49e7BgwQIYGRnB3t5e78b8bzY2NmjYsCEuXryod9vXwcEBHh4eam2NGzeWDqXp6++qq1evYvv27Rg6dKjUpm/bFgDGjx+PiRMnYsCAAfDy8sL777+P0aNHIyoqCoB2t69eBhkTExM0b94cO3bskNoKCwuxY8cO+Pj4aLEyzatbty5q1aqlNtbs7GwcPnxYGquPjw8yMzNx7Ngxqc/OnTtRWFiI1q1bV3nNZRFCYOTIkVi3bh127tyJunXrqi1v3rw5jI2N1cabmpqKa9euqY339OnTav9htm3bBmtr62K/aHVRYWEh8vLy9HKsXbp0wenTp5GcnCxNLVq0QHBwsPRvfRvzv+Xk5ODSpUtwcHDQu+3brl27YrdKOH/+PFxcXADo3++qInFxcbCzs0OPHj2kNn3btgDw8OFDGBioRwZDQ0MUFhYC0PL2rfBpwjpu9erVQqlUimXLlokzZ86IYcOGCRsbG7UzxOXiwYMH4sSJE+LEiRMCgJgzZ444ceKEuHr1qhDi6SVvNjY2Yv369eLUqVOid+/eJV7y1qxZM3H48GGxf/9+0aBBA528pHHEiBFCpVKJ3bt3q13a+PDhQ6nP8OHDhbOzs9i5c6dISkoSPj4+wsfHR1pedFlj165dRXJystiyZYuoWbOmTl7WOHHiRLFnzx6RlpYmTp06JSZOnCgUCoXYunWrEEK/xlqaf1+1JIR+jXns2LFi9+7dIi0tTRw4cED4+fmJGjVqiIyMDCGEfo31yJEjwsjISEybNk1cuHBBrFy5Upibm4sVK1ZIffTpd5UQT6+GdXZ2FhMmTCi2TJ+2rRBChISEiNq1a0uXX8fHx4saNWqIzz77TOqjre2rt0FGCCFiYmKEs7OzMDExEa1atRJ//vmntkuqkF27dgkAxaaQkBAhxNPL3r766ithb28vlEql6NKli0hNTVVbx927d0VQUJCwtLQU1tbW4oMPPhAPHjzQwmjKVtI4AYi4uDipz6NHj8THH38sqlWrJszNzcVbb70lbty4obaeK1euiICAAGFmZiZq1Kghxo4dK/75558qHs3zDR48WLi4uAgTExNRs2ZN0aVLFynECKFfYy3Ns0FGn8b87rvvCgcHB2FiYiJq164t3n33XbX7qujTWIUQ4vfffxeenp5CqVSKRo0aiSVLlqgt16ffVUIIkZiYKAAUG4MQ+rdts7OzxaeffiqcnZ2FqampqFevnvjiiy/ULhXX1vZVCPGv2/IRERERyYheniNDRERErwYGGSIiIpItBhkiIiKSLQYZIiIiki0GGSIiIpItBhkiIiKSLQYZIi1bt24dfv31V22XQUQkSwwyRFp05MgRhIeHo02bNtou5aXt3r0bCoWi2PNlqLhly5YVezJyVerYsSPCw8O19v5EmsQgQ6QhoaGhUCgUiI6OVmtPSEiAQqEo1j8rKwtDhw7FunXr4OzsXFVlkky5urpi3rx52i6DSOcwyBBpkKmpKWbMmIH79+8/t69KpcKpU6fw+uuvV0FlJXvy5InW3lsfFRQUSA/RI6KqwSBDpEF+fn6oVauW9Gj7kkRGRuK1115Ta5s3bx5cXV2l+dDQUPTp0wfTp0+Hvb09bGxsMHXqVOTn52P8+PGwtbVFnTp1EBcXp7ae9PR09O/fHzY2NrC1tUXv3r1x5cqVYuudNm0aHB0d4e7uDgA4ffo0OnfuDDMzM1SvXh3Dhg1DTk5OmWPdvHkzGjZsCDMzM3Tq1EntfYrs378f7du3h5mZGZycnDBq1Cjk5uaWuL7z589DoVDg3Llzau1z586Fm5ubNJ+SkoKAgABYWlrC3t4e77//Pu7cuQMAuHLlChQKRbGpY8eO5a5p0aJFaNCgAUxNTWFvb49+/fqV+hkUHSLasGEDPDw8oFQqce3aNeTl5WHcuHGoXbs2LCws0Lp1a+zevbvU9Vy6dAm9e/eGvb09LC0t0bJlS2zfvl1a3rFjR1y9ehWjR4+WxgQAd+/eRVBQEGrXrg1zc3N4eXnh559/Vlt3bm4uBg0aBEtLSzg4OGD27NnF3v/+/fsYNGgQqlWrBnNzcwQEBODChQvS8qtXryIwMBDVqlWDhYUFmjRpgs2bN5c6HqKqxCBDpEGGhoaYPn06YmJi8L///e+l1rVz5078/fff2Lt3L+bMmYPJkyejZ8+eqFatGg4fPozhw4fjo48+kt7nn3/+gb+/P6ysrLBv3z4cOHAAlpaW6Natm9qelx07diA1NRXbtm3Dxo0bkZubC39/f1SrVg1Hjx7FmjVrsH37dowcObLU2tLT09G3b18EBgYiOTkZQ4cOxcSJE9X6XLp0Cd26dcPbb7+NU6dO4ZdffsH+/ftLXW/Dhg3RokULrFy5Uq195cqVGDhwIAAgMzMTnTt3RrNmzZCUlIQtW7bg1q1b6N+/PwDAyckJN27ckKYTJ06gevXq6NChQ7lqSkpKwqhRozB16lSkpqZiy5Yt0mtL8/DhQ8yYMQM//vgj/vrrL9jZ2WHkyJE4dOgQVq9ejVOnTuGdd95Bt27d1MLBv+Xk5KB79+7YsWMHTpw4gW7duiEwMBDXrl0DAMTHx6NOnTqYOnWqNDYAePz4MZo3b45NmzYhJSUFw4YNw/vvv48jR45I6x4/fjz27NmD9evXY+vWrdi9ezeOHz+u9v6hoaFISkrChg0bcOjQIQgh0L17d/zzzz8AgLCwMOTl5WHv3r04ffo0ZsyYAUtLyzI/F6Iq81KPnCQiSUhIiOjdu7cQQog2bdqIwYMHCyGEWLdunfj3f7XJkycLb29vtdfOnTtXuLi4qK3LxcVFFBQUSG3u7u6iffv20nx+fr6wsLAQP//8sxBCiP/+97/C3d1dFBYWSn3y8vKEmZmZSExMlNZrb2+v9sTaJUuWiGrVqomcnBypbdOmTcLAwEDcvHmzxLFGREQIDw8PtbYJEyYIAOL+/ftCCCGGDBkihg0bptZn3759wsDAQDx69KjE9c6dO1e4ublJ86mpqQKAOHv2rBBCiK+//lp07dpV7TXp6eklPoH40aNHonXr1qJnz57S5/i8mtauXSusra1FdnZ2ifU9Ky4uTgAQycnJUtvVq1eFoaGhuH79ulrfLl26iIiICOl1KpWqzHU3adJExMTESPMuLi5i7ty5z62pR48eYuzYsUIIIR48eCBMTEzEr7/+Ki2/e/euMDMzk544fv78eQFAHDhwQOpz584dYWZmJr3Oy8tLREZGPve9ibTBSJshikhfzZgxA507d8a4ceMqvI4mTZrAwOD/dpra29vD09NTmjc0NET16tWRkZEBADh58iQuXrwIKysrtfU8fvwYly5dkua9vLxgYmIizZ89exbe3t6wsLCQ2tq1a4fCwkKkpqbC3t6+WG1nz55F69at1dp8fHzU5k+ePIlTp06p7WERQqCwsBBpaWlo3LhxsfUOGDAA48aNw59//ok2bdpg5cqVeP3119GoUSNpnbt27Spxb8ClS5fQsGFDaX7w4MF48OABtm3bJn2Oz6vpzTffhIuLC+rVq4du3bqhW7dueOutt2Bubl7s/YqYmJigadOm0vzp06dRUFCgVgsA5OXloXr16iWuIycnB5GRkdi0aRNu3LiB/Px8PHr0SNojU5qCggJMnz4dv/76K65fv44nT54gLy9PqvfSpUt48uSJ2raytbWVDikCT7elkZGRWp/q1avD3d0dZ8+eBQCMGjUKI0aMwNatW+Hn54e3335bbcxE2sQgQ1QJOnToAH9/f0RERCA0NFRtmYGBAYQQam1Fu/D/zdjYWG1eoVCU2FZ0cmlOTg6aN29e7NAMANSsWVP6978DS2XKycnBRx99hFGjRhVbVtpVWrVq1ULnzp2xatUqtGnTBqtWrcKIESPU1hkYGIgZM2YUe62Dg4P072+++QaJiYk4cuSIWrB7Xk0mJiY4fvw4du/eja1bt2LSpEmIjIzE0aNHS71c2szMTO2qtJycHBgaGuLYsWMwNDRU61va4Zhx48Zh27Zt+Pbbb1G/fn2YmZmhX79+zz0Ze9asWZg/fz7mzZsHLy8vWFhYIDw8XOMncQ8dOhT+/v7YtGkTtm7diqioKMyePRuffPKJRt+HqCIYZIgqSXR0NF577TW1v36Bp6Hi5s2bEEJIX4DJyckv/X6vv/46fvnlF9jZ2cHa2rrcr2vcuDGWLVuG3NxcKeQcOHAABgYGxWr/92s2bNig1vbnn38Wq+fMmTOoX7/+C40jODgYn332GYKCgnD58mUMGDBAbZ1r166Fq6srjIxK/vW1du1aTJ06FX/88YfaScLlrcnIyAh+fn7w8/PD5MmTYWNjg507d6Jv377lqr9Zs2YoKChARkYG2rdvX67XHDhwAKGhoXjrrbcAPA1Dz548bWJigoKCgmKv6927N9577z0AQGFhIc6fPw8PDw8AgJubG4yNjXH48GEpPN6/fx/nz5+Hr68vgKfbMj8/H4cPH0bbtm0BPD2JODU1VVoP8PT8o+HDh2P48OGIiIjADz/8wCBDOoEn+xJVEi8vLwQHB2PBggVq7R07dsTt27cxc+ZMXLp0CbGxsfjjjz9e+v2Cg4NRo0YN9O7dG/v27UNaWhp2796NUaNGlXnicXBwMExNTRESEoKUlBTs2rULn3zyCd5///0SDysBwPDhw3HhwgWMHz8eqampWLVqFZYtW6bWZ8KECTh48CBGjhyJ5ORkXLhwAevXry/zJGIA6Nu3Lx48eIARI0agU6dOcHR0lJaFhYXh3r17CAoKwtGjR3Hp0iUkJibigw8+QEFBAVJSUjBo0CBMmDABTZo0wc2bN3Hz5k3cu3evXDVt3LgRCxYsQHJyMq5evYr//Oc/KCwsLDXQlaRhw4YIDg7GoEGDEB8fj7S0NBw5cgRRUVHYtGlTia9p0KAB4uPjkZycjJMnT2LgwIHFLuN2dXXF3r17cf36dekqrQYNGmDbtm04ePAgzp49i48++gi3bt2SXmNpaYkhQ4Zg/Pjx2LlzJ1JSUhAaGqp2yLJBgwbo3bs3PvzwQ+zfvx8nT57Ee++9h9q1a6N3794AgPDwcCQmJiItLQ3Hjx/Hrl27Sjw0SKQNDDJElWjq1KnFvpAaN26MRYsWITY2Ft7e3jhy5MhLnUtTxNzcHHv37oWzszP69u2Lxo0bY8iQIXj8+HGZe2jMzc2RmJiIe/fuoWXLlujXrx+6dOmChQsXlvoaZ2dnrF27FgkJCfD29sZ3332H6dOnq/Vp2rQp9uzZg/Pnz6N9+/Zo1qwZJk2apBZMSmJlZYXAwECcPHkSwcHBasscHR1x4MABFBQUoGvXrvDy8kJ4eDhsbGxgYGCApKQkPHz4EN988w0cHBykqWhvyvNqsrGxQXx8PDp37ozGjRvju+++w88//4wmTZqUWfOz4uLiMGjQIIwdOxbu7u7o06cPjh49WuohtTlz5qBatWpo27YtAgMD4e/vX+z+QlOnTsWVK1fg5uYmHSr88ssv8frrr8Pf3x8dO3ZErVq10KdPH7XXzZo1C+3bt0dgYCD8/PzwxhtvoHnz5sXqbd68OXr27AkfHx8IIbB582bpUGZBQQHCwsLQuHFjdOvWDQ0bNsSiRYte6DMhqiwK8ezBeiIiIiKZ4B4ZIiIiki0GGSIiIpItBhkiIiKSLQYZIiIiki0GGSIiIpItBhkiIiKSLQYZIiIiki0GGSIiIpItBhkiIiKSLQYZIiIiki0GGSIiIpItBhkiIiKSrf8HBfzpjnBGBUIAAAAASUVORK5CYII=",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#Gráfico dos chromossomos\n",
    "sns.barplot(x = civic['chromosome'].value_counts()[:10],\n",
    "            y = civic['chromosome'].value_counts()[:10].index)\n",
    "plt.title(\"Chromosomo com maior incidencia\")\n",
    "plt.xlabel(\"Número de vezes relatados\")\n",
    "plt.ylabel(\"Chromosomo\")\n",
    "plt.show()"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Esse gráfico é interessante pois nele podemos observar em quais cromossomos ouve a maior incidencia de mudanças, por exemplo o chromossomo 3. primeiro nesse gráfico, é onde ocorre a mutação que causa a doença VHL ou doença de Von Hippel-Lindau, os dois primeiros casos em gene e disease."
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Comparação de outras colunas com disease usando px"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "application/vnd.plotly.v1+json": {
       "config": {
        "plotlyServerURL": "https://plot.ly"
       },
       "data": [
        {
         "hovertemplate": "disease=%{x}<br>variant_origin=%{y}<br>count=%{marker.size}<extra></extra>",
         "legendgroup": "",
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   "source": [
    "# agrupa os dados pela combinação de \"variant_origin\" e \"disease\" e soma a quantidade de ocorrências de cada combinação\n",
    "grouped_origin_df = civic.groupby(['variant_origin', 'disease']).size().reset_index(name='count')\n",
    "# ordena os dados pela quantidade de ocorrências em ordem decrescente\n",
    "sorted_origin_df = grouped_origin_df.sort_values('count', ascending=False)\n",
    "# seleciona as informações mais relevantes\n",
    "top_origin_df = sorted_origin_df.head(30)\n",
    "\n",
    "# conta a frequência dos valores em cada coluna\n",
    "var_counts = civic['disease'].value_counts().reset_index()\n",
    "origin_counts = civic['variant_origin'].value_counts().reset_index()\n",
    "# renomeia as colunas\n",
    "origin_counts.columns = ['variant_origin', 'count']\n",
    "var_counts.columns = ['disease', 'count']\n",
    "\n",
    "# cria o gráfico de dispersão\n",
    "fig = px.scatter(top_origin_df, x='disease', y='variant_origin', size='count',title='Identificando a origen da doença',\n",
    "            color_discrete_sequence=px.colors.qualitative.Pastel,\n",
    "            template='plotly_dark')\n",
    "# mostra o gráfico\n",
    "fig.show()"
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   "source": [
    "\n",
    "Com esse gráfico interativo podemos observar 15 itens de disease comparados a coluna variant_origin, podemos ver que a maioria dos cânceres são somaticos enquando 584 são geminativo raro e 14 germinativo comum."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
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   ],
   "source": [
    "# agrupa os dados pela combinação de \"gene\" e \"disease\" e soma a quantidade de ocorrências de cada combinação\n",
    "grouped_genes_df = civic.groupby(['disease', 'gene']).size().reset_index(name='count')\n",
    "# ordena os dados pela quantidade de ocorrências em ordem decrescente\n",
    "sorted_genes_df = grouped_genes_df.sort_values('count', ascending=False)\n",
    "# seleciona as informações mais relevantes\n",
    "top_genes_df = sorted_genes_df.head(10)\n",
    "\n",
    "# Cria o gráfico de barras\n",
    "fig = px.bar(top_genes_df, x='gene', y='count', color='disease', \n",
    "             title='Frequência dos disease por gene (Top 10)',\n",
    "             color_discrete_sequence=px.colors.qualitative.Pastel)#, #template='plotly_dark')\n",
    "# Personaliza os rótulos do eixo x e y\n",
    "fig.update_xaxes(title='gene')\n",
    "fig.update_yaxes(title='Frequência')\n",
    "# Mostra o gráfico\n",
    "fig.show()"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Com que frequencia disease é associada a gene ? VHL e Von Hippeç-Lindau tem um interação de quase 600, enquanto FLT3 e Acute Myloid Leukemia tem pouco menos de 100 intereções."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
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    "# agrupa os dados pela combinação de \"disease\" e \"variant\" e soma a quantidade de ocorrências de cada combinação\n",
    "grouped_variant_df = civic.groupby(['disease', 'variant']).size().reset_index(name='count')\n",
    "# ordena os dados pela quantidade de ocorrências em ordem decrescente\n",
    "sorted_variant_df = grouped_variant_df.sort_values('count', ascending=False)\n",
    "# seleciona as informações mais relevantes\n",
    "top_variant_df = sorted_variant_df.head(10)\n",
    "\n",
    "# Cria o gráfico de barras\n",
    "fig = px.bar(top_variant_df, x='variant', y='count', color='disease', \n",
    "             title='Frequência dos disease por variant (Top 8)',\n",
    "             color_discrete_sequence=px.colors.qualitative.Pastel)#, #template='plotly_dark')\n",
    "# Personaliza os rótulos do eixo x e y\n",
    "fig.update_xaxes(title='variant')\n",
    "fig.update_yaxes(title='Frequência')\n",
    "# Mostra o gráfico\n",
    "fig.show()"
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   "metadata": {},
   "source": [
    "Cada doença possui sua variant, quais as maiores interações ? Por exemplo: Mutation se relaciona com 3 diseases, colorectal cancer com uma contagem de 41 aparições, acute myeloid leukemia com 37 aparições e lung non-small cell com 36 aparições"
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  },
  {
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   "source": [
    "# agrupa os dados pela combinação de \"disease\" e \"drugs\" e soma a quantidade de ocorrências de cada combinação\n",
    "grouped_drugs_df = civic.groupby(['disease', 'drugs']).size().reset_index(name='count')\n",
    "# ordena os dados pela quantidade de ocorrências em ordem decrescente\n",
    "sorted_drugs_df = grouped_drugs_df.sort_values('count', ascending=False)\n",
    "# seleciona as informações mais relevantes\n",
    "top_drugs_df = sorted_drugs_df.head(10)\n",
    "\n",
    "# Cria o gráfico de barras\n",
    "fig = px.bar(top_drugs_df, x='drugs', y='count', color='disease', \n",
    "             title='Frequência dos disease por drugs (Top 10)',\n",
    "             color_discrete_sequence=px.colors.qualitative.Pastel)#, #template='plotly_dark')\n",
    "# Personaliza os rótulos do eixo x e y\n",
    "fig.update_xaxes(title='drugs')\n",
    "fig.update_yaxes(title='Frequência')\n",
    "# Mostra o gráfico\n",
    "fig.show()"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Para cada câncer uma ou mais drogas são usadas em seus tratamentos, quais as melhores interações ?\n",
    "\n",
    "Ex: Chronic Myeloid Leukemia tem uma grande interação com dasatinib, imatinib, bosutinib, imatinib mesylate, ou seja, esse tipo de câncer pode ser tratado com todas essas drogas mas a que mais é usada é a imatinib."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [
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   "source": [
    "# agrupa os dados pela combinação de \"disease\" e \"evidence_type\" e soma a quantidade de ocorrências de cada combinação\n",
    "grouped_evidence_type_df = civic.groupby(['disease', 'evidence_type']).size().reset_index(name='count')\n",
    "# ordena os dados pela quantidade de ocorrências em ordem decrescente\n",
    "sorted_evidence_type_df = grouped_evidence_type_df.sort_values('count', ascending=False)\n",
    "# seleciona as informações mais relevantes\n",
    "top_evidence_type_df = sorted_evidence_type_df.head(10)\n",
    "\n",
    "# Cria o gráfico de barras\n",
    "fig = px.bar(top_evidence_type_df, x='evidence_type', y='count', color='disease', \n",
    "             title='Frequência dos disease por tipo de evidencia (Top 10)',\n",
    "             color_discrete_sequence=px.colors.qualitative.Pastel)#, #template='plotly_dark')\n",
    "# Personaliza os rótulos do eixo x e y\n",
    "fig.update_xaxes(title='evidence_type')\n",
    "fig.update_yaxes(title='Frequência')\n",
    "# Mostra o gráfico\n",
    "fig.show()"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Com esse gráfico visualizamos os tipos cancerigenos e como normalmente eles são diagnosticados. Ex: Von Hippel é na maior parte das vezes uma predisposição enquanto Lung Non-Small é um preditivo e Acute Myeloid Leukemia normalmente é um prognostico."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [
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   ],
   "source": [
    "# agrupa os dados pela combinação de \"disease\" e \"clinical_significance\" e soma a quantidade de ocorrências de cada combinação\n",
    "grouped_clinical_significance_type_df = civic.groupby(['disease', 'clinical_significance']).size().reset_index(name='count')\n",
    "# ordena os dados pela quantidade de ocorrências em ordem decrescente\n",
    "sorted_clinical_significance_type_df = grouped_clinical_significance_type_df.sort_values('count', ascending=False)\n",
    "# seleciona as informações mais relevantes\n",
    "top_clinical_significance_type_df = sorted_clinical_significance_type_df.head(10)\n",
    "\n",
    "# Cria o gráfico de barras\n",
    "fig = px.bar(top_clinical_significance_type_df, x='clinical_significance', y='count', color='disease', \n",
    "             title='Frequência dos disease por significancia clinica (Top 10)',\n",
    "             color_discrete_sequence=px.colors.qualitative.Pastel)#, #template='plotly_dark')\n",
    "# Personaliza os rótulos do eixo x e y\n",
    "fig.update_xaxes(title='clinical_significance')\n",
    "fig.update_yaxes(title='Frequência')\n",
    "# Mostra o gráfico\n",
    "fig.show()"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Como os tipos cancerigenos responderam ao tratamento ? Von Hippel tende a responder com significancia incerta, enquanto melanoma tente a ter uma resposta sensitiva e o proprio cancer tente a ser resistente."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {},
   "outputs": [
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         }
        },
        "title": {
         "text": "Frequência dos disease por chromosome (Top 10)"
        },
        "xaxis": {
         "anchor": "y",
         "domain": [
          0,
          1
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         "title": {
          "text": "chromosome"
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        "yaxis": {
         "anchor": "x",
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     },
     "metadata": {},
     "output_type": "display_data"
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   ],
   "source": [
    "# agrupa os dados pela combinação de \"disease\" e \"chromosome\" e soma a quantidade de ocorrências de cada combinação\n",
    "grouped_chromosome_type_df = civic.groupby(['disease', 'chromosome']).size().reset_index(name='count')\n",
    "# ordena os dados pela quantidade de ocorrências em ordem decrescente\n",
    "sorted_chromosome_type_df = grouped_chromosome_type_df.sort_values('count', ascending=False)\n",
    "# seleciona as informações mais relevantes\n",
    "top_chromosome_type_df = sorted_chromosome_type_df.head(10)\n",
    "\n",
    "# Cria o gráfico de barras\n",
    "fig = px.bar(top_chromosome_type_df, x='chromosome', y='count', color='disease', \n",
    "             title='Frequência dos disease por chromosome (Top 10)',\n",
    "             color_discrete_sequence=px.colors.qualitative.Pastel)#, #template='plotly_dark')\n",
    "# Personaliza os rótulos do eixo x e y\n",
    "fig.update_xaxes(title='chromosome')\n",
    "fig.update_yaxes(title='Frequência')\n",
    "# Mostra o gráfico\n",
    "fig.show()\n"
   ]
  },
  {
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   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Os tipos cancerigenos realmente tendem a atacar apenas um gene como padrão ? Melanoma ataca o gene 7 com incidencia de 7 itens, cartinoma ataca o gene 7 tambem mas com uma incidência de 134 itens e o Von Hippel ? Bom, ele ataca o gene 3 com uma incidência de 548 itens."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
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   "source": [
    "# agrupa os dados pela combinação de \"disease\" e \"rating\" e soma a quantidade de ocorrências de cada combinação\n",
    "grouped_rating_type_df = civic.groupby(['disease', 'rating']).size().reset_index(name='count')\n",
    "# ordena os dados pela quantidade de ocorrências em ordem decrescente\n",
    "sorted_rating_type_df = grouped_rating_type_df.sort_values('count', ascending=False)\n",
    "# seleciona zzas informações mais relevantes\n",
    "top_rating_type_df = sorted_rating_type_df.head(10)\n",
    "\n",
    "# conta a frequência dos valores em cada coluna\n",
    "var_counts = civic['variant_origin'].value_counts().reset_index()\n",
    "gene_counts = civic['disease'].value_counts().reset_index()\n",
    "# renomeia as colunas\n",
    "gene_counts.columns = ['disease', 'count']\n",
    "var_counts.columns = ['gene', 'count']\n",
    "\n",
    "# cria o gráfico de dispersão\n",
    "fig = px.scatter(top_genes_df, x='gene', y='disease', size='count',title='Identificando a doença do gene',\n",
    "            color_discrete_sequence=px.colors.qualitative.Pastel,\n",
    "            template='plotly_dark')\n",
    "# mostra o gráfico\n",
    "fig.show()"
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  },
  {
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           "bgcolor": "#E5ECF6",
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     },
     "metadata": {},
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    }
   ],
   "source": [
    "# Cria o gráfico de barras\n",
    "fig = px.bar(top_rating_type_df, x='rating', y='count', color='disease', \n",
    "             title='Frequência dos disease por rating (Top 10)',\n",
    "             color_discrete_sequence=px.colors.qualitative.Pastel)#, #template='plotly_dark')\n",
    "# Personaliza os rótulos do eixo x e y\n",
    "fig.update_xaxes(title='rating')\n",
    "fig.update_yaxes(title='Frequência')\n",
    "# Mostra o gráfico\n",
    "fig.show()"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Nem sempre o tratamento que funciona pra funciona pra todos, afinal cada pessoa é uma pessoa, por isso nesse gráfico visualizamos que o ranking pode variar dependendo da doença. Ex: Von Hippel recebeu entre 2 e 3 estrelas enquanto o carcinoma recebeu entre 2, 3 e 4 estrelas."
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Transformando em número"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {},
   "outputs": [],
   "source": [
    "#Transformando os dados não númericos em númericos.\n",
    "df = civic[['gene', 'disease', 'variant', 'clinical_significance', 'variant_origin', 'evidence_level', 'drugs']].apply(lambda x: pd.factorize(x)[0])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {},
   "outputs": [
    {
     "data": {
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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#Gráfico de dispersão (scattler plot)\n",
    "# Pontos do Gráfico nazz côr verde (c = 'g')\n",
    "plt.scatter(df['disease'], df['gene'], c='g')\n",
    "plt.xlabel(\"Câncer\")\n",
    "plt.ylabel(\"Gene\")\n",
    "plt.show()"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Usamos o scattler plot para observaro compartamento entre duas variáveis e sua relação, podemos ver que perto de 0 existe um grande aglomerado, pois essa são as váriais que mais aparencem em nossos dados."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Calcule a matriz de correlação\n",
    "correlation_matrix = df.corr()\n",
    "# Crie o heatmap para visualizar a correlação\n",
    "sns.heatmap(correlation_matrix, annot=True, cmap='coolwarm')\n",
    "plt.title(\"Correlação entre gene e doença\")\n",
    "plt.show()"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Por fim uma matriz de correlação entre as variaveis mais usadas durante esse caderno, os valores próximos de 1 representam uma correlação positiva forte entre as variaveis.\n",
    "Quando mais proximo de -1 mais forte é a correlação negativa entre duas variaveis.\n",
    "Se o valor se aproxima de 0 indica que não há uma correlação linear forte."
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Banco de Dados"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Criando um banco de dados"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Para o banco de dados, utilizamos o PostgreSQL, criando 3 tabelas, genes, variantes e evidencias. A tabela genes contém os genes que foram analisados, a tabela variantes contém as variantes encontradas e a tabela evidencias contém as evidências encontradas para cada variante."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<img src= \"https://raw.githubusercontent.com/perolatai/projeto_CDIA_CIVIC/main/database/civic_erdpng.png\">"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Dump do Banco de Dados\n",
    "\n",
    "https://raw.githubusercontent.com/perolatai/projeto_CDIA_CIVIC/main/database/civicdb"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "```python\n",
    "CREATE TABLE genes\n",
    "(\n",
    "    entrez_id INTEGER PRIMARY KEY,\n",
    "    gene VARCHAR(255),\n",
    "    description TEXT\n",
    ");\n",
    "\n",
    "CREATE TABLE variantes\n",
    "(\n",
    "    variant_id INTEGER PRIMARY KEY,\n",
    "    variant VARCHAR(255),\n",
    "    summary TEXT,\n",
    "    start VARCHAR(255),\n",
    "    stop VARCHAR(255),\n",
    "    reference_bases VARCHAR(255),\n",
    "    variant_bases VARCHAR(255),\n",
    "    representative_transcript VARCHAR(255),\n",
    "    ensembl_version FLOAT,\n",
    "    reference_build VARCHAR(255),\n",
    "    variant_types VARCHAR(255),\n",
    "    hgvs_expressions VARCHAR(255),\n",
    "    civic_variant_evidence_score FLOAT,\n",
    "    allele_registry_id VARCHAR(255),\n",
    "    clinvar_ids VARCHAR(255),\n",
    "    variant_aliases VARCHAR(255)\n",
    ");\n",
    "\n",
    "CREATE TABLE evidencias\n",
    "(\n",
    "    evidence_id INTEGER PRIMARY KEY,\n",
    "    entrez_id INTEGER,\n",
    "    disease VARCHAR(255),\n",
    "    doid FLOAT,\n",
    "    phenotypes VARCHAR(255),\n",
    "    drugs VARCHAR(255),\n",
    "    drug_interaction_type VARCHAR(255),\n",
    "    evidence_type VARCHAR(255),\n",
    "    evidence_direction VARCHAR(255),\n",
    "    evidence_level VARCHAR(255),\n",
    "    clinical_significance VARCHAR(255),\n",
    "    evidence_statement TEXT,\n",
    "    citation_id INTEGER,\n",
    "    source_type VARCHAR(255),\n",
    "    citation VARCHAR(255),\n",
    "    rating FLOAT,\n",
    "    evidence_status VARCHAR(255),\n",
    "    variant_id INTEGER,\n",
    "    chromosome VARCHAR(255),\n",
    "    variant_origin VARCHAR(255),\n",
    "    is_flagged BOOLEAN,\n",
    "    FOREIGN KEY (entrez_id) REFERENCES genes(entrez_id),\n",
    "    FOREIGN KEY (variant_id) REFERENCES variantes(variant_id)\n",
    ");\n",
    "```"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "A tabela evidencias possui uma chave estrangeira para a tabela variantes, que é a variante_id e uma chave estrangeira para a tabela genes, que é a gene_id, possibilitando a ligação entre as tabelas."
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Inserindo dados"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Ao extrairmosos dados da API, utilizamos os arquivos para inserir os dados em nosso banco de dados, atraves de um script em python."
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "```python\n",
    "def insert_dataframe_into_mysql(df, table_name, connection):\n",
    "    cursor = connection.cursor()\n",
    "    placeholders = ', '.join(['%s'] * len(df.columns))\n",
    "    columns = ', '.join(df.columns)\n",
    "    sql = f\"INSERT INTO {table_name} ({columns}) VALUES ({placeholders})\"\n",
    "    for _, row in df.iterrows():\n",
    "        row = pd.concat([pd.Series([0]), row])\n",
    "        row = row.iloc[1:]\n",
    "        values = tuple(row.fillna('None'))\n",
    "        cursor.execute(sql, values)\n",
    "    connection.commit()\n",
    "\n",
    "\n",
    "insert_dataframe_into_mysql(df_Gene, 'genes', conn)\n",
    "insert_dataframe_into_mysql(df_Variant, 'variantes', conn)\n",
    "insert_dataframe_into_mysql(df_Evidences, 'evidencias', conn)\n",
    "```"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Disponibilizando os dados"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Para que o grupo tenha acesso a todo momento aos dados, disponibilizamos um banco de dados na nuvem, através do site [https://www.elephantsql.com/](https://www.elephantsql.com/), que utilizam o PostgreSQL como banco de dados.\n",
    "\n",
    "Para acessarmos os dados, utilizamos o seguitne comando:"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "```python\n",
    "conn = psycopg2.connect(\n",
    "    database=database,\n",
    "    host=host,\n",
    "    user=user,\n",
    "    password=password,\n",
    "    port=port)\n",
    "```"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# API"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "No nosso trabalho, a função da API é extrair todas as informações do banco de dados CIViC, feito com base numa planilha de mesmo nome, documentando várias informações sobre pacientes com câncer.\n",
    "\n",
    "Os dados estão divididos em três tabelas: Genes, Evidências e Variantes."
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "```python\n",
    "from flask import Flask, make_response, jsonify, request\n",
    "```"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "O Flask traz o comandos GET, POST, PUT e DELETE, utilizados para interagir com bancos de dados. Assim, conseguimos utilizar também a sintaxe do SQL para fazer as requisições na nossa base.   \n",
    "O MySQL.connector liga a API ao banco de dados, bastando fornecer as credenciais criadas pelo gerente do database.   \n",
    "O Settings é um arquivo .py com as credenciais citadas acima.\n",
    "```python\n",
    "conn = psycopg2.connect(\n",
    "    database=database,\n",
    "    host=host,\n",
    "    user=user,\n",
    "    password=password,\n",
    "    port=port)\n",
    "\n",
    "app = Flask(__name__)\n",
    "app.config['JSON_SORT_KEYS'] = False\n",
    "\n",
    "cursor = conn.cursor()\n",
    "```"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Colocamos o Flask na variável 'app' e importamos o cursor para poder \"navegar\" pelo banco de dados."
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "```python\n",
    "\n",
    "@app.route('/genes', methods=['GET'])\n",
    "def get_genes():\n",
    "    cursor.execute('SELECT * FROM genes')\n",
    "    lista_genes = cursor.fetchall()\n",
    "\n",
    "    genes = list()\n",
    "    for gene in lista_genes:\n",
    "        genes.append(\n",
    "            {\n",
    "                'id':gene[0],\n",
    "                'gene':gene[1],\n",
    "                'descrição':gene[2]\n",
    "            }\n",
    "        )\n",
    "\n",
    "    return make_response(\n",
    "        jsonify(\n",
    "            message ='Genes:',\n",
    "            data = genes\n",
    "        )\n",
    "    )\n",
    "```"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Os comandos do Flask para definir a rota (/genes) e o método (GET) sempre começam com @.   \n",
    "Depois fizemos uma função para extrair tudo da tabela GENES, separar o rótulo das colunas e depois aplicá-los nos dados para torná-los mais fáceis de entender. Nessa junção final, utilizamos o **jsonify**."
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "O mesmo foi feito com as duas outras tabelas, EVIDENCIAS e VARIÁVEIS. O código é o mesmo, mudando apenas as váriaveis e a quantidade de rótulos extraídos, pois as tabelas têm números diferentes de colunas. De qualquer forma, segue o código:"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "```python\n",
    "@app.route('/evidencias', methods=['GET'])\n",
    "def get_evidencias():\n",
    "    cursor.execute('SELECT * FROM evidencias')\n",
    "    lista_evidencias = cursor.fetchall()\n",
    "\n",
    "    # Adicionando os rótulos das colunas;\n",
    "    evidencias = list()\n",
    "    for evidencia in lista_evidencias:\n",
    "        evidencias.append(\n",
    "            {\n",
    "                'evidence_id': evidencia[0],\n",
    "                'entrez_id': evidencia[1],\n",
    "                'disease': evidencia[2],\n",
    "                'doid': evidencia[3],\n",
    "                'phenotypes': evidencia[4],\n",
    "                'drugs': evidencia[5],\n",
    "                'drug_interaction_type': evidencia[6],\n",
    "                'evidence_type': evidencia[7],\n",
    "                'evidence_direction': evidencia[8],\n",
    "                'evidence_level': evidencia[9],\n",
    "                'clinical_significance': evidencia[10],\n",
    "                'evidence_statement': evidencia[11],\n",
    "                'citation_id': evidencia[12],\n",
    "                'source_type': evidencia[13],\n",
    "                'citation': evidencia[14],\n",
    "                'rating': evidencia[15],\n",
    "                'evidence_status': evidencia[16],\n",
    "                'variant_id': evidencia[17],\n",
    "                'chromosome': evidencia[18],\n",
    "                'variant_origin': evidencia[19],\n",
    "                'is_flagged': evidencia[20]\n",
    "            }\n",
    "        )\n",
    "\n",
    "    return make_response(\n",
    "        jsonify(\n",
    "            message ='Evidencias',\n",
    "            data = evidencias\n",
    "        )\n",
    "    )\n",
    "```"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "```python\n",
    "@app.route('/variantes', methods=['GET'])\n",
    "def get_variantes():\n",
    "    cursor.execute('SELECT * FROM variantes')\n",
    "    lista_variantes = cursor.fetchall()\n",
    "\n",
    "    # Adicionando os rótulos das colunas;\n",
    "    variantes = list()\n",
    "    for variante in lista_variantes:\n",
    "        variantes.append(\n",
    "            {\n",
    "                'variant_id':variante[0],\n",
    "                'variant':variante[1],\n",
    "                'summary':variante[2],\n",
    "                'start':variante[3],\n",
    "                'stop':variante[4],\n",
    "                'reference_bases':variante[5],\n",
    "                'variant_bases':variante[6],\n",
    "                'representative_transcript':variante[7],\n",
    "                'ensembl_version':variante[8],\n",
    "                'reference_build':variante[9],\n",
    "                'variant_types':variante[10],\n",
    "                'hgvs_expressions':variante[11],\n",
    "                'civic_variant_evidence_score':variante[12],\n",
    "                'allele_registry_id':variante[13],\n",
    "                'clinvar_ids':variante[14],\n",
    "                'variant_aliases':variante[15]\n",
    "            }\n",
    "        )\n",
    "\n",
    "    return make_response(\n",
    "        jsonify(\n",
    "            message ='Variantes:',\n",
    "            data = variantes\n",
    "        )\n",
    "    )\n",
    "```"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Por fim, rodamos a API com um comando do Flask:\n",
    "\n",
    "```python\n",
    "if __name__ == \"__main__\":\n",
    "    app.run()\n",
    "```"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Modelo Preditivo"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Vamos utilizar a biblioteca PyTorch para o modelo de rede neural.\n",
    "\n",
    "Com o PyTorch, podemos facilmente criar modelos de redes neurais, definir a função de perda, otimizar os pesos do modelo usando um otimizador, e iterar sobre seus dados de treinamento para ajustar o modelo. PyTorch é uma escolha popular devido à sua facilidade de uso e flexibilidade.\n",
    "\n",
    "A implementação do modelo foi baseada na documentação oficial do PyTorch que possui uma seção de tutoriais com exemplos práticos e passo a passo detalhado.\n",
    "\n",
    "https://learn.microsoft.com/pt-br/windows/ai/windows-ml/tutorials/pytorch-analysis-train-model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {},
   "outputs": [],
   "source": [
    "#importações necessárias para o modelo preditivo\n",
    "import torch \n",
    "import torch.nn as nn \n",
    "from torch.utils.data import random_split, DataLoader, TensorDataset \n",
    "import torch.nn.functional as F \n",
    "import numpy as np \n",
    "import torch.optim as optim \n",
    "from torch.optim import Adam \n",
    "import warnings\n",
    "warnings.filterwarnings(\"ignore\")"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Iremos excluir dados que causarão ruídos no nosso treinamento como colunas que representam o gene mas com códigos diferentes e até citações que ocorreram em algum projeto ou artigo.\n",
    "\n",
    "Também definiremos a nossa variável alvo que é a coluna 'evidence_type', nela temos as categorias de ações que queremos prever."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {},
   "outputs": [],
   "source": [
    "#separando X e y\n",
    "y = civic[['evidence_type']]\n",
    "\n",
    "# cria a lista para deletar colunas que não servem para o modelo:\n",
    "toDrop = ['entrez_id', 'evidence_type', 'gene_id', 'evidence_level', 'citation_id', 'source_type', 'citation', \n",
    "          'evidence_id', 'representative_transcript','ensembl_version', 'allele_registry_id']\n",
    "\n",
    "#cria a função para deletar as colunas:\n",
    "def deletaColuna(dataframe):\n",
    "    for coluna in toDrop:\n",
    "        del dataframe[coluna]\n",
    "\n",
    "#chama a função:\n",
    "deletaColuna(civic)\n",
    "\n",
    "#chama o fatorize para transformar as colunas categóricas em número:\n",
    "X = civic.apply(lambda x: pd.factorize(x)[0])\n",
    "\n",
    "#converte para número as features:\n",
    "labels = {'Predictive':0, 'Predisposing':1, 'Prognostic':2, 'Diagnostic':3, 'Oncogenic':4, 'Functional':5} \n",
    "\n",
    "#adiciona os numeros para as features categóricas:\n",
    "y['evidence_type_num'] = y['evidence_type']   \n",
    "y.evidence_type_num = [labels[item] for item in y.evidence_type_num]\n",
    "\n",
    "#output: pega a ultima coluna do Y  \n",
    "output = y.loc[:, 'evidence_type_num'] "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "Input format:  torch.Size([3897, 28]) torch.float32\n",
      "Output format:  torch.Size([3897]) torch.int64\n"
     ]
    }
   ],
   "source": [
    "#Converte o dado de Input e o Output para tensor e criar o dataset de Tensor:\n",
    "input = torch.Tensor(X.to_numpy())      # cria o tensot do tipo torch.float32 \n",
    "print('\\nInput format: ', input.shape, input.dtype)  \n",
    "output = torch.tensor(output.to_numpy())        # cria o tensor do tipo torch.int64  \n",
    "print('Output format: ', output.shape, output.dtype)\n",
    "data = TensorDataset(input, output) "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "metadata": {},
   "outputs": [],
   "source": [
    "#separa em treino, validação e test usando random_split\n",
    "train_batch_size = 5 #quantidade de passagens pelo conjunto\n",
    "number_rows = len(input)    \n",
    "test_split = int(number_rows*0.3)  \n",
    "validate_split = int(number_rows*0.2) \n",
    "train_split = number_rows - test_split - validate_split     \n",
    "train_set, validate_set, test_set = random_split( data, [train_split, validate_split, test_split])  "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "metadata": {},
   "outputs": [],
   "source": [
    "#Cria o Dataloader para ler o dado com tamanho batch e põe na memória:\n",
    "train_loader = DataLoader(train_set, batch_size = train_batch_size, shuffle = True) \n",
    "validate_loader = DataLoader(validate_set, batch_size = 1) \n",
    "test_loader = DataLoader(test_set, batch_size = 1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Define os parâmetros do modelo\n",
    "input_size = list(input.shape)[1]  \n",
    "learning_rate = 0.01 \n",
    "output_size = len(labels)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "metadata": {},
   "outputs": [],
   "source": [
    "# definindo a rede neural \n",
    "class Network(nn.Module): \n",
    "   def __init__(self, input_size, output_size): \n",
    "       super(Network, self).__init__() \n",
    "        \n",
    "       self.layer1 = nn.Linear(input_size, 24) \n",
    "       self.layer2 = nn.Linear(24, 24) \n",
    "       self.layer3 = nn.Linear(24, output_size) \n",
    "\n",
    "\n",
    "   def forward(self, x): \n",
    "       x1 = F.relu(self.layer1(x)) \n",
    "       x2 = F.relu(self.layer2(x1)) \n",
    "       x3 = self.layer3(x2) \n",
    "       return x3 "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Instanciando o modelo\n",
    "model = Network(input_size, output_size)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Função para salvar o modelo\n",
    "def saveModel(): \n",
    "    path = \"./Resultado-do-Civic-Modelo.pth\" \n",
    "    torch.save(model.state_dict(), path) "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Define the loss function with Classification Cross-Entropy loss and an optimizer with Adam optimizer\n",
    "loss_fn = nn.CrossEntropyLoss()\n",
    "optimizer = Adam(model.parameters(), lr=0.001, weight_decay=0.0001)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Função de treino:\n",
    "def train(num_epochs): \n",
    "    best_accuracy = 0.0 \n",
    "     \n",
    "    print(\"Começando o treino das epochs...\") \n",
    "    for epoch in range(1, num_epochs+1): \n",
    "        running_train_loss = 0.0 \n",
    "        running_accuracy = 0.0 \n",
    "        running_vall_loss = 0.0 \n",
    "        total = 0 \n",
    " \n",
    "        # Looping de treino \n",
    "        for data in train_loader: \n",
    "            inputs, outputs = data  \n",
    "            optimizer.zero_grad()   \n",
    "            predicted_outputs = model(inputs) #predict\n",
    "            train_loss = loss_fn(predicted_outputs, outputs)   \n",
    "            train_loss.backward()   \n",
    "            optimizer.step()        \n",
    "            running_train_loss +=train_loss.item()  \n",
    " \n",
    "        # Calculando o training loss value:\n",
    "        train_loss_value = running_train_loss/len(train_loader) \n",
    " \n",
    "        # Looping de validação: \n",
    "        with torch.no_grad(): \n",
    "            model.eval() \n",
    "            for data in validate_loader: \n",
    "               inputs, outputs = data \n",
    "               predicted_outputs = model(inputs) \n",
    "               val_loss = loss_fn(predicted_outputs, outputs) \n",
    "             \n",
    "               # A label com o mais alto valor será o prediction \n",
    "               _, predicted = torch.max(predicted_outputs, 1) \n",
    "               running_vall_loss += val_loss.item()  \n",
    "               total += outputs.size(0) \n",
    "               running_accuracy += (predicted == outputs).sum().item() \n",
    " \n",
    "        # calculando o validation loss value \n",
    "        val_loss_value = running_vall_loss/len(validate_loader) \n",
    "        accuracy = (100 * running_accuracy / total)     \n",
    " \n",
    "        # Salva o modelo, se tiver boa acurácia:\n",
    "        if accuracy > best_accuracy: \n",
    "            saveModel() \n",
    "            best_accuracy = accuracy \n",
    "            \n",
    "         \n",
    "        # Print as estatisticas das epoch's\n",
    "        print('Completed training batch', epoch, 'Training Loss is: %.4f' %train_loss_value, 'Validation Loss is: %.4f' %val_loss_value, 'Accuracy is %d %%' % (accuracy))\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Função de teste do Modelo\n",
    "def test(): \n",
    "    # Load the model that we saved at the end of the training loop \n",
    "    model = Network(input_size, output_size) \n",
    "    path = \"Resultado-do-Civic-Modelo.pth\" \n",
    "    model.load_state_dict(torch.load(path)) \n",
    "     \n",
    "    running_accuracy = 0 \n",
    "    total = 0 \n",
    " \n",
    "    with torch.no_grad(): \n",
    "        for data in test_loader: \n",
    "            inputs, outputs = data \n",
    "            outputs = outputs.to(torch.float32) \n",
    "            predicted_outputs = model(inputs) \n",
    "            _, predicted = torch.max(predicted_outputs, 1) \n",
    "            total += outputs.size(0) \n",
    "            running_accuracy += (predicted == outputs).sum().item() \n",
    " \n",
    "        print('Precisão do modelo com base no conjunto de teste de', test_split ,'acurácia de: %d %%' % (100 * running_accuracy / total))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Função de teste para cada categoria\n",
    "def test_dna(): \n",
    "    # carrega o modelo salvo\n",
    "    model = Network(input_size, output_size) \n",
    "    path = \"Resultado-do-Civic-Modelo.pth\" \n",
    "    model.load_state_dict(torch.load(path)) \n",
    "     \n",
    "    labels_length = len(labels) \n",
    "    labels_correct = list(0. for i in range(labels_length)) \n",
    "    labels_total = list(0. for i in range(labels_length))   \n",
    "  \n",
    "    with torch.no_grad(): \n",
    "        for data in test_loader: \n",
    "            inputs, outputs = data \n",
    "            predicted_outputs = model(inputs) \n",
    "            _, predicted = torch.max(predicted_outputs, 1) \n",
    "             \n",
    "            label_correct_running = (predicted == outputs).squeeze() \n",
    "            label = outputs[0] \n",
    "            if label_correct_running.item():  \n",
    "                labels_correct[label] += 1 \n",
    "            labels_total[label] += 1  \n",
    "  \n",
    "    label_list = list(labels.keys()) \n",
    "    for i in range(output_size): \n",
    "        print('Acurácia para prever %5s : %2d %%' % (label_list[i], 100 * labels_correct[i] / labels_total[i]))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Começando o treino das epochs...\n",
      "Completed training batch 1 Training Loss is: 3.5216 Validation Loss is: 1.6348 Accuracy is 69 %\n",
      "Completed training batch 2 Training Loss is: 0.9096 Validation Loss is: 0.7068 Accuracy is 80 %\n",
      "Completed training batch 3 Training Loss is: 0.7343 Validation Loss is: 0.5789 Accuracy is 84 %\n",
      "Completed training batch 4 Training Loss is: 0.5848 Validation Loss is: 0.6041 Accuracy is 85 %\n",
      "Completed training batch 5 Training Loss is: 0.5297 Validation Loss is: 0.5653 Accuracy is 84 %\n",
      "Completed training batch 6 Training Loss is: 0.4894 Validation Loss is: 0.5048 Accuracy is 87 %\n",
      "Completed training batch 7 Training Loss is: 0.4859 Validation Loss is: 0.6527 Accuracy is 85 %\n",
      "Completed training batch 8 Training Loss is: 0.4674 Validation Loss is: 0.4337 Accuracy is 88 %\n",
      "Completed training batch 9 Training Loss is: 0.3801 Validation Loss is: 0.4836 Accuracy is 85 %\n",
      "Completed training batch 10 Training Loss is: 0.3646 Validation Loss is: 0.4510 Accuracy is 88 %\n",
      "Fim do Treino para 10 epochs definidas! \n",
      "\n",
      "Precisão do modelo com base no conjunto de teste de 1169 acurácia de: 87 %\n",
      "Acurácia para prever Predictive : 95 %\n",
      "Acurácia para prever Predisposing : 92 %\n",
      "Acurácia para prever Prognostic : 78 %\n",
      "Acurácia para prever Diagnostic : 30 %\n",
      "Acurácia para prever Oncogenic : 35 %\n",
      "Acurácia para prever Functional : 40 %\n"
     ]
    }
   ],
   "source": [
    "# Executando o modelo\n",
    "num_epochs = 10\n",
    "train(num_epochs) \n",
    "print('Fim do Treino para', num_epochs, 'epochs definidas! \\n') \n",
    "test() \n",
    "test_dna()"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Temos uma taxa de acerto relativamente alta de 87% \n",
    "\n",
    "Com base nos resultados apresentados, podemos concluir que o modelo apresentou uma precisão geral de 87% no conjunto de teste. Além disso, ele obteve altas acurácias ao prever os tipos de evidência Predictive (95%) e Predisposing (92%). No entanto, a acurácia foi menor ao prever os tipos de evidência Diagnostic (30%) e Oncogenic (35%)."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 57,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "from sklearn.metrics import confusion_matrix\n",
    "# colocando o modelo no modo de avaliação\n",
    "model.eval()\n",
    "# lista para armazenar as previsões do modelo\n",
    "predicted_labels = []\n",
    "# lista para armazenar as verdadeiras labels\n",
    "true_labels = []\n",
    "# percorre o conjunto de testes e faz as previsões\n",
    "for data in test_loader:\n",
    "    inputs, outputs = data\n",
    "    predicted_outputs = model(inputs)\n",
    "    _, predicted = torch.max(predicted_outputs.data, 1)\n",
    "    predicted_labels.append(predicted.item())\n",
    "    true_labels.append(outputs.item())\n",
    "\n",
    "# cria a matriz de confusão\n",
    "cm = confusion_matrix(true_labels, predicted_labels)\n",
    "# labels para o eixo x e y\n",
    "labels = list(y.loc[:, 'evidence_type'].value_counts().keys())\n",
    "# plotando a matriz de confusão\n",
    "sns.heatmap(cm, annot=True, cmap='Blues', fmt='g', xticklabels=labels, yticklabels=labels, cbar=False)\n",
    "# labels do gráfico\n",
    "plt.xlabel('Predicted labels')\n",
    "plt.ylabel('True labels')\n",
    "plt.title('Matriz de Confusão')\n",
    "plt.show()"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Com a Matriz de Confusão é possível visualizar quantos previsões foram corretas através dos valores diagonais e todos os valores fora foram as classificações preditas erroneamente, nossos falsos positivos."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 56,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "evidence_type  evidence_type_num\n",
       "Predictive     0                    2451\n",
       "Predisposing   1                     643\n",
       "Prognostic     2                     511\n",
       "Diagnostic     3                     194\n",
       "Oncogenic      4                      67\n",
       "Functional     5                      31\n",
       "Name: count, dtype: int64"
      ]
     },
     "execution_count": 56,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#quantidade de ocorrências para cada categoria\n",
    "y.value_counts()"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Observando a distribuição dos tipos de evidência na coluna \"evidence_type\", podemos notar que os tipos Predictive e Predisposing são os mais frequentes, seguidos pelo tipo Prognostic. Portanto, é natural que o modelo tenha apresentado maior acurácia ao prever esses tipos de evidência."
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "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": "3.11.0"
  },
  "orig_nbformat": 4
 },
 "nbformat": 4,
 "nbformat_minor": 2
}