sars_cov_2_pubmed_secondary_sources_.ipynb

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      "execution_count": 1,
      "metadata": {
        "id": "scAlWrQEENep"
      },
      "outputs": [],
      "source": [
        "import pandas as pd\n",
        "import seaborn as sns\n",
        "import networkx as nx\n",
        "import matplotlib.pyplot as plt\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 2,
      "metadata": {
        "id": "UMeRjc9gEdQL"
      },
      "outputs": [],
      "source": [
        "file_path =  \"/content/drive/MyDrive/04-Work/EDA-Comparison-21st-Epidemics-PubMed/all-covid-19-pubmed-metadata.csv\""
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 3,
      "metadata": {
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        },
        "id": "h9VmsU1aE0cT",
        "outputId": "eb8fb702-d52d-45e4-8ddb-4bd739ba1485"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "/usr/local/lib/python3.7/dist-packages/IPython/core/interactiveshell.py:2882: DtypeWarning: Columns (0) have mixed types.Specify dtype option on import or set low_memory=False.\n",
            "  exec(code_obj, self.user_global_ns, self.user_ns)\n"
          ]
        }
      ],
      "source": [
        "data = pd.read_csv(file_path)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 4,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "T7REVLsJE3k7",
        "outputId": "0c69e7ae-4f63-424f-c2fd-6bad2e1e2113"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "<class 'pandas.core.frame.DataFrame'>\n",
            "RangeIndex: 204668 entries, 0 to 204667\n",
            "Data columns (total 17 columns):\n",
            " #   Column  Non-Null Count   Dtype \n",
            "---  ------  --------------   ----- \n",
            " 0   PMID    204668 non-null  object\n",
            " 1   TI      204668 non-null  object\n",
            " 2   AB      204668 non-null  object\n",
            " 3   AD      204668 non-null  object\n",
            " 4   FAU     204668 non-null  object\n",
            " 5   DP      204668 non-null  object\n",
            " 6   TA      204668 non-null  object\n",
            " 7   JT      204668 non-null  object\n",
            " 8   LA      204668 non-null  object\n",
            " 9   MH      204668 non-null  object\n",
            " 10  OAB     204668 non-null  object\n",
            " 11  OT      204666 non-null  object\n",
            " 12  PL      204668 non-null  object\n",
            " 13  PST     204668 non-null  object\n",
            " 14  PT      204668 non-null  object\n",
            " 15  RN      204668 non-null  object\n",
            " 16  SI      204668 non-null  object\n",
            "dtypes: object(17)\n",
            "memory usage: 26.5+ MB\n"
          ]
        }
      ],
      "source": [
        "data.info()"
      ]
    },
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              "      <th></th>\n",
              "      <th>PMID</th>\n",
              "      <th>TI</th>\n",
              "      <th>AB</th>\n",
              "      <th>AD</th>\n",
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              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>35052023</td>\n",
              "      <td>Does a Mental Health Diagnosis Worsen Outcomes...</td>\n",
              "      <td>NONE</td>\n",
              "      <td>Department of Psychiatry, Icahn School of Medi...</td>\n",
              "      <td>Vadukapuram, Ramu;Mansuri, Zeeshan;Trivedi, Ch...</td>\n",
              "      <td>2022 Jan 20</td>\n",
              "      <td>Prim Care Companion CNS Disord</td>\n",
              "      <td>The primary care companion for CNS disorders</td>\n",
              "      <td>eng</td>\n",
              "      <td>NONE</td>\n",
              "      <td>NONE</td>\n",
              "      <td>NONE</td>\n",
              "      <td>United States</td>\n",
              "      <td>epublish</td>\n",
              "      <td>Journal Article</td>\n",
              "      <td>NONE</td>\n",
              "      <td>NONE</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>35052021</td>\n",
              "      <td>The Opioid Epidemic and the COVID-19 Pandemic:...</td>\n",
              "      <td>NONE</td>\n",
              "      <td>Department of Psychiatry, AdventHealth Orlando...</td>\n",
              "      <td>Das, Aparna;Padala, Akshay P;Wilson, Kerrie B</td>\n",
              "      <td>2022 Jan 20</td>\n",
              "      <td>Prim Care Companion CNS Disord</td>\n",
              "      <td>The primary care companion for CNS disorders</td>\n",
              "      <td>eng</td>\n",
              "      <td>NONE</td>\n",
              "      <td>NONE</td>\n",
              "      <td>NONE</td>\n",
              "      <td>United States</td>\n",
              "      <td>epublish</td>\n",
              "      <td>Journal Article</td>\n",
              "      <td>NONE</td>\n",
              "      <td>NONE</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>35052020</td>\n",
              "      <td>Has COVID-19 Complicated Eating Disorder Treat...</td>\n",
              "      <td>Objective: Recent evidence suggests psychosoci...</td>\n",
              "      <td>Medical Department, Alsana, Thousand Oaks, Cal...</td>\n",
              "      <td>Mascolo, Margherita;Duffy, Mary E;Zehring, Bra...</td>\n",
              "      <td>2022 Jan 20</td>\n",
              "      <td>Prim Care Companion CNS Disord</td>\n",
              "      <td>The primary care companion for CNS disorders</td>\n",
              "      <td>eng</td>\n",
              "      <td>Comorbidity;Pandemics;Adult;*COVID-19;Humans;S...</td>\n",
              "      <td>NONE</td>\n",
              "      <td>NONE</td>\n",
              "      <td>United States</td>\n",
              "      <td>epublish</td>\n",
              "      <td>Journal Article</td>\n",
              "      <td>NONE</td>\n",
              "      <td>NONE</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>35051990</td>\n",
              "      <td>Neutralizing antibodies against the SARS-CoV-2...</td>\n",
              "      <td>The recent emergence of the SARS-CoV-2 Omicron...</td>\n",
              "      <td>Howard Hughes Medical Institute, Chevy Chase, ...</td>\n",
              "      <td>Pearson, Claire;Grubaugh, Nathan D;Monteiro, V...</td>\n",
              "      <td>2022 Jan 20</td>\n",
              "      <td>Nat Med</td>\n",
              "      <td>Nature medicine</td>\n",
              "      <td>eng</td>\n",
              "      <td>NONE</td>\n",
              "      <td>NONE</td>\n",
              "      <td>NONE</td>\n",
              "      <td>United States</td>\n",
              "      <td>aheadofprint</td>\n",
              "      <td>Journal Article</td>\n",
              "      <td>NONE</td>\n",
              "      <td>NONE</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>35051989</td>\n",
              "      <td>Neutralizing antibodies against the SARS-CoV-2...</td>\n",
              "      <td>The Omicron variant is rapidly becoming the do...</td>\n",
              "      <td>School of Public Health, LKS Faculty of Medici...</td>\n",
              "      <td>Chen, Chunke;Chan, Karl C K;Lau, Eric H Y;Lam,...</td>\n",
              "      <td>2022 Jan 20</td>\n",
              "      <td>Nat Med</td>\n",
              "      <td>Nature medicine</td>\n",
              "      <td>eng</td>\n",
              "      <td>NONE</td>\n",
              "      <td>NONE</td>\n",
              "      <td>NONE</td>\n",
              "      <td>United States</td>\n",
              "      <td>aheadofprint</td>\n",
              "      <td>Journal Article</td>\n",
              "      <td>NONE</td>\n",
              "      <td>NONE</td>\n",
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            "text/plain": [
              "       PMID                                                 TI  ...    RN    SI\n",
              "0  35052023  Does a Mental Health Diagnosis Worsen Outcomes...  ...  NONE  NONE\n",
              "1  35052021  The Opioid Epidemic and the COVID-19 Pandemic:...  ...  NONE  NONE\n",
              "2  35052020  Has COVID-19 Complicated Eating Disorder Treat...  ...  NONE  NONE\n",
              "3  35051990  Neutralizing antibodies against the SARS-CoV-2...  ...  NONE  NONE\n",
              "4  35051989  Neutralizing antibodies against the SARS-CoV-2...  ...  NONE  NONE\n",
              "\n",
              "[5 rows x 17 columns]"
            ]
          },
          "metadata": {},
          "execution_count": 5
        }
      ],
      "source": [
        "data.head(5)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 6,
      "metadata": {
        "id": "V9HwDxPNE6eS"
      },
      "outputs": [],
      "source": [
        "def none_entries(column):\n",
        "  return dict(column.value_counts()).get('NONE')"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 7,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "AX43ETF5FLOP",
        "outputId": "a9dd2d7b-ccb1-4cb5-ac4f-490cb8c9d5e8"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Missing entries from PMID-\t2\n",
            "Missing entries from TI-\t617\n",
            "Missing entries from AB-\t56128\n",
            "Missing entries from AD-\t10258\n",
            "Missing entries from FAU-\t1838\n",
            "Missing entries from DP-\t2\n",
            "Missing entries from TA-\t165\n",
            "Missing entries from JT-\t158\n",
            "Missing entries from LA-\t2\n",
            "Missing entries from MH-\t72505\n",
            "Missing entries from OAB-\t204498\n",
            "Missing entries from OT-\t81220\n",
            "Missing entries from PL-\t23\n",
            "Missing entries from PST-\t158\n",
            "Missing entries from PT-\t2\n",
            "Missing entries from RN-\t158321\n",
            "Missing entries from SI-\t202877\n"
          ]
        }
      ],
      "source": [
        "for column in data.columns:\n",
        "  print(f\"Missing entries from {column}-\\t{none_entries(data[column])}\")"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "data.SI.value_counts()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "-W0bhb8Cv8Uy",
        "outputId": "cd456fb4-5249-4d47-fa4e-4b02d4249a95"
      },
      "execution_count": 8,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "NONE                                                                                                                           202877\n",
              "ClinicalTrials.gov/NCT04318366                                                                                                     10\n",
              "ClinicalTrials.gov/NCT04334291                                                                                                      8\n",
              "ClinicalTrials.gov/NCT04381936                                                                                                      7\n",
              "ClinicalTrials.gov/NCT02735707                                                                                                      6\n",
              "                                                                                                                                ...  \n",
              "ClinicalTrials.gov/NCT04388670                                                                                                      1\n",
              "ClinicalTrials.gov/NCT04634409;ClinicalTrials.gov/NCT04411628;ClinicalTrials.gov/NCT04497987;ClinicalTrials.gov/NCT04427501         1\n",
              "ClinicalTrials.gov/NCT01826591                                                                                                      1\n",
              "ClinicalTrials.gov/NCT04542941                                                                                                      1\n",
              "ClinicalTrials.gov/NCT03052777                                                                                                      1\n",
              "Name: SI, Length: 1558, dtype: int64"
            ]
          },
          "metadata": {},
          "execution_count": 8
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "sec_source_present = data[data['SI']!='NONE']"
      ],
      "metadata": {
        "id": "vrCRUvkBv_HR"
      },
      "execution_count": 9,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "secondary_sources_list = list()\n",
        "\n",
        "for item in sec_source_present['SI']:\n",
        "  secondary_sources_list.append(item.split('/')[0])"
      ],
      "metadata": {
        "id": "E-FACJC1wJ1A"
      },
      "execution_count": 10,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "import numpy as np"
      ],
      "metadata": {
        "id": "hMOYOsjS06by"
      },
      "execution_count": 11,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "np.unique(secondary_sources_list)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "t5tOG7b_0-pQ",
        "outputId": "c7cbe646-761d-4735-ab93-502130ed98ce"
      },
      "execution_count": 12,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array(['ANZCTR', 'BioProject', 'CTRI', 'ChiCTR', 'ClinicalTrials.gov',\n",
              "       'DRKS', 'Dryad', 'EudraCT', 'GENBANK', 'GEO', 'IRCT', 'ISRCTN',\n",
              "       'NTR', 'PDB', 'RefSeq', 'SRA', 'TCTR', 'UMIN-CTR', 'dbGaP',\n",
              "       'figshare'], dtype='<U18')"
            ]
          },
          "metadata": {},
          "execution_count": 12
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "from collections import Counter\n",
        "\n",
        "counter = Counter(secondary_sources_list)"
      ],
      "metadata": {
        "id": "7qOm5nmE0-ru"
      },
      "execution_count": 13,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "counter"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "-TFph4fn0-t_",
        "outputId": "af80e586-6654-47fd-c301-322088ce9715"
      },
      "execution_count": 14,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Counter({'ANZCTR': 6,\n",
              "         'BioProject': 1,\n",
              "         'CTRI': 3,\n",
              "         'ChiCTR': 13,\n",
              "         'ClinicalTrials.gov': 1359,\n",
              "         'DRKS': 11,\n",
              "         'Dryad': 39,\n",
              "         'EudraCT': 11,\n",
              "         'GENBANK': 14,\n",
              "         'GEO': 15,\n",
              "         'IRCT': 3,\n",
              "         'ISRCTN': 24,\n",
              "         'NTR': 1,\n",
              "         'PDB': 10,\n",
              "         'RefSeq': 7,\n",
              "         'SRA': 3,\n",
              "         'TCTR': 1,\n",
              "         'UMIN-CTR': 3,\n",
              "         'dbGaP': 1,\n",
              "         'figshare': 266})"
            ]
          },
          "metadata": {},
          "execution_count": 14
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "ext_source_text = data.iloc[201871]['SI']\n",
        "ext_source_text"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 35
        },
        "id": "jk9kFg87wO5Y",
        "outputId": "092e7357-e9df-4efe-9476-271d3aaff45d"
      },
      "execution_count": 15,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "string"
            },
            "text/plain": [
              "'GENBANK/MT007544.1;GENBANK/NC_045512.3;GENBANK/EPI_ISL_406844'"
            ]
          },
          "metadata": {},
          "execution_count": 15
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "ct_ids = list()\n",
        "for ext_source_text in sec_source_present['SI']:\n",
        "  if 'ClinicalTrials' in ext_source_text:\n",
        "    source_list = ext_source_text.split(';')\n",
        "    \n",
        "    for item in source_list:\n",
        "      if item.startswith(\"C\"):\n",
        "        items = item.split('/')\n",
        "        ct_ids.extend(items[1].split(','))\n",
        "    \n"
      ],
      "metadata": {
        "id": "ux5c-KP7x9Gq"
      },
      "execution_count": 16,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "ct_ids"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "w8RqyB7H0Zzx",
        "outputId": "08ad47c3-0c07-4ce2-d1e5-0cea9abf4a0c"
      },
      "execution_count": 17,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
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              " 'NCT04318431',\n",
              " ...]"
            ]
          },
          "metadata": {},
          "execution_count": 17
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "len(ct_ids)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "1rBP4c9xwcK3",
        "outputId": "b70a9354-1da1-4ef5-8e5b-8a89bc428643"
      },
      "execution_count": 18,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "1658"
            ]
          },
          "metadata": {},
          "execution_count": 18
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "genbank_ids = list()\n",
        "for ext_source_text in sec_source_present['SI']:\n",
        "  if 'GENBANK' in ext_source_text:\n",
        "    source_list = ext_source_text.split(';')\n",
        "    \n",
        "    for item in source_list:\n",
        "      if item.startswith(\"G\"):\n",
        "        items = item.split('/')\n",
        "        genbank_ids.extend(items[1].split(','))"
      ],
      "metadata": {
        "id": "NXrAmX0lxvB4"
      },
      "execution_count": 19,
      "outputs": []
    }
  ],
  "metadata": {
    "colab": {
      "collapsed_sections": [],
      "name": "Sars-CoV-2-EDA.ipynb",
      "provenance": [],
      "mount_file_id": "1S7gWhwTTu7-m6K1q9rrKpcq1XSwQWQRa",
      "authorship_tag": "ABX9TyOgnweYWJGQ0XoeJSLz8ZRW"
    },
    "kernelspec": {
      "display_name": "Python 3",
      "name": "python3"
    },
    "language_info": {
      "name": "python"
    }
  },
  "nbformat": 4,
  "nbformat_minor": 0
}