Final_webscrapping_lit_review_publicversion.ipynb

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  "metadata": {
    "colab": {
      "name": "Final_webscrapping_lit_review_publicversion.ipynb",
      "provenance": []
    },
    "kernelspec": {
      "name": "python3",
      "display_name": "Python 3"
    }
  },
  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "4bMPh9WEiXt_"
      },
      "source": [
        "# 0. Install and import packages"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "jO81T6bcI33e",
        "outputId": "8cfd02e1-5350-4765-c0e5-8d6435991f78",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        }
      },
      "source": [
        "!pip install fake-useragent\n",
        "!pip install beautifulsoup4\n",
        "!apt-get update\n",
        "!apt install chromium-chromedriver\n",
        "!cp /usr/lib/chromium-browser/chromedriver /usr/bin\n",
        "!pip install selenium\n",
        "!pip install bs4\n",
        "!pip install biopython"
      ],
      "execution_count": 1,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Collecting fake-useragent\n",
            "  Downloading https://files.pythonhosted.org/packages/d1/79/af647635d6968e2deb57a208d309f6069d31cb138066d7e821e575112a80/fake-useragent-0.1.11.tar.gz\n",
            "Building wheels for collected packages: fake-useragent\n",
            "  Building wheel for fake-useragent (setup.py) ... \u001b[?25l\u001b[?25hdone\n",
            "  Created wheel for fake-useragent: filename=fake_useragent-0.1.11-cp36-none-any.whl size=13485 sha256=293054e8effdfe2d2ee788fb9a0716d838e72fdb96c9dfa8a03dbebaff1c3239\n",
            "  Stored in directory: /root/.cache/pip/wheels/5e/63/09/d1dc15179f175357d3f5c00cbffbac37f9e8690d80545143ff\n",
            "Successfully built fake-useragent\n",
            "Installing collected packages: fake-useragent\n",
            "Successfully installed fake-useragent-0.1.11\n",
            "Requirement already satisfied: beautifulsoup4 in /usr/local/lib/python3.6/dist-packages (4.6.3)\n",
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            "Get:16 https://cloud.r-project.org/bin/linux/ubuntu bionic-cran40/ Packages [39.3 kB]\n",
            "Get:17 http://archive.ubuntu.com/ubuntu bionic-updates/universe amd64 Packages [2,115 kB]\n",
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            "Get:21 http://security.ubuntu.com/ubuntu bionic-security/main amd64 Packages [1,745 kB]\n",
            "Ign:22 https://developer.download.nvidia.com/compute/cuda/repos/ubuntu1804/x86_64  Packages\n",
            "Get:23 https://developer.download.nvidia.com/compute/machine-learning/repos/ubuntu1804/x86_64  Packages [57.0 kB]\n",
            "Get:22 https://developer.download.nvidia.com/compute/cuda/repos/ubuntu1804/x86_64  Packages [370 kB]\n",
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            "Get:25 http://security.ubuntu.com/ubuntu bionic-security/restricted amd64 Packages [211 kB]\n",
            "Get:26 http://security.ubuntu.com/ubuntu bionic-security/universe amd64 Packages [1,352 kB]\n",
            "Fetched 11.2 MB in 2s (5,862 kB/s)\n",
            "Reading package lists... Done\n",
            "Reading package lists... Done\n",
            "Building dependency tree       \n",
            "Reading state information... Done\n",
            "The following additional packages will be installed:\n",
            "  chromium-browser chromium-browser-l10n chromium-codecs-ffmpeg-extra\n",
            "Suggested packages:\n",
            "  webaccounts-chromium-extension unity-chromium-extension adobe-flashplugin\n",
            "The following NEW packages will be installed:\n",
            "  chromium-browser chromium-browser-l10n chromium-chromedriver\n",
            "  chromium-codecs-ffmpeg-extra\n",
            "0 upgraded, 4 newly installed, 0 to remove and 51 not upgraded.\n",
            "Need to get 80.1 MB of archives.\n",
            "After this operation, 271 MB of additional disk space will be used.\n",
            "Get:1 http://archive.ubuntu.com/ubuntu bionic-updates/universe amd64 chromium-codecs-ffmpeg-extra amd64 86.0.4240.75-0ubuntu0.18.04.1 [1,127 kB]\n",
            "Get:2 http://archive.ubuntu.com/ubuntu bionic-updates/universe amd64 chromium-browser amd64 86.0.4240.75-0ubuntu0.18.04.1 [70.9 MB]\n",
            "Get:3 http://archive.ubuntu.com/ubuntu bionic-updates/universe amd64 chromium-browser-l10n all 86.0.4240.75-0ubuntu0.18.04.1 [3,579 kB]\n",
            "Get:4 http://archive.ubuntu.com/ubuntu bionic-updates/universe amd64 chromium-chromedriver amd64 86.0.4240.75-0ubuntu0.18.04.1 [4,486 kB]\n",
            "Fetched 80.1 MB in 1s (74.6 MB/s)\n",
            "Selecting previously unselected package chromium-codecs-ffmpeg-extra.\n",
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            "Setting up chromium-codecs-ffmpeg-extra (86.0.4240.75-0ubuntu0.18.04.1) ...\n",
            "Setting up chromium-browser (86.0.4240.75-0ubuntu0.18.04.1) ...\n",
            "update-alternatives: using /usr/bin/chromium-browser to provide /usr/bin/x-www-browser (x-www-browser) in auto mode\n",
            "update-alternatives: using /usr/bin/chromium-browser to provide /usr/bin/gnome-www-browser (gnome-www-browser) in auto mode\n",
            "Setting up chromium-chromedriver (86.0.4240.75-0ubuntu0.18.04.1) ...\n",
            "Setting up chromium-browser-l10n (86.0.4240.75-0ubuntu0.18.04.1) ...\n",
            "Processing triggers for hicolor-icon-theme (0.17-2) ...\n",
            "Processing triggers for mime-support (3.60ubuntu1) ...\n",
            "Processing triggers for man-db (2.8.3-2ubuntu0.1) ...\n",
            "cp: '/usr/lib/chromium-browser/chromedriver' and '/usr/bin/chromedriver' are the same file\n",
            "Collecting selenium\n",
            "\u001b[?25l  Downloading https://files.pythonhosted.org/packages/80/d6/4294f0b4bce4de0abf13e17190289f9d0613b0a44e5dd6a7f5ca98459853/selenium-3.141.0-py2.py3-none-any.whl (904kB)\n",
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            "Installing collected packages: selenium\n",
            "Successfully installed selenium-3.141.0\n",
            "Requirement already satisfied: bs4 in /usr/local/lib/python3.6/dist-packages (0.0.1)\n",
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            "Collecting biopython\n",
            "\u001b[?25l  Downloading https://files.pythonhosted.org/packages/76/02/8b606c4aa92ff61b5eda71d23b499ab1de57d5e818be33f77b01a6f435a8/biopython-1.78-cp36-cp36m-manylinux1_x86_64.whl (2.3MB)\n",
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            "Installing collected packages: biopython\n",
            "Successfully installed biopython-1.78\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "ZoWIqzTQJUP_"
      },
      "source": [
        "import openpyxl\n",
        "import requests\n",
        "from fake_useragent import UserAgent\n",
        "from bs4 import BeautifulSoup\n",
        "import pandas as pd\n",
        "from selenium import webdriver\n",
        "from selenium import webdriver\n",
        "import re\n",
        "import requests\n",
        "from lxml import html\n",
        "from lxml import etree\n",
        "import urllib\n",
        "import re   \n",
        "import unicodedata\n",
        "import time\n",
        "from Bio import Entrez\n",
        "import networkx as nx\n",
        "import pandas as pd"
      ],
      "execution_count": 2,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "bQjRqUeEihgf"
      },
      "source": [
        "What do you want to search?"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "faPPfJzKLBhd"
      },
      "source": [
        "myquery = '\"radiomics\"AND\"CT\"AND\"Ovarian Cancer\"'"
      ],
      "execution_count": 3,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "G-LJv8ndgYhg"
      },
      "source": [
        "# 1. Get excel with abstracts"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "3gGkSqApf_Xh"
      },
      "source": [
        "Add your personal key and email here to be able to access the PubMed database\n",
        "\n",
        "Instructions to get your personal key can be found here: https://ncbiinsights.ncbi.nlm.nih.gov/2017/11/02/new-api-keys-for-the-e-utilities/\n"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "ZMa-FxaqPgeJ"
      },
      "source": [
        "Entrez.email = 'name@domain'\n"
      ],
      "execution_count": 4,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "G0i36lTpPeN2"
      },
      "source": [
        "The api key is optional, but you can get more articles without error"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "DBstjRhyPifL"
      },
      "source": [
        "Entrez.api_key = \"*********\""
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "htf6QIgXjYNl"
      },
      "source": [
        "Some helper functions..."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "s1KABabfPltO"
      },
      "source": [
        "def search(query,mymax):\n",
        "    handle = Entrez.esearch(db='pubmed', \n",
        "                            sort='relevance', \n",
        "                            retmax=str(mymax),\n",
        "                            retmode='xml', \n",
        "                            term=query)\n",
        "    results = Entrez.read(handle)\n",
        "    return results\n",
        "\n",
        "def fetch_details(id_list):\n",
        "    ids = ','.join(id_list)\n",
        "    handle = Entrez.efetch(db='pubmed',\n",
        "                           retmode='xml',\n",
        "                           id=ids)\n",
        "    results = Entrez.read(handle)\n",
        "    return results\n",
        "\n",
        "def fetch_details_2(id):\n",
        "    handle = Entrez.efetch(db='pubmed',\n",
        "                           retmode='xml',\n",
        "                           id=id)\n",
        "    results = Entrez.read(handle)\n",
        "    return results"
      ],
      "execution_count": 7,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "OHa4bZOPjapN"
      },
      "source": [
        "Get the results"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "oxNDx86TPzUY"
      },
      "source": [
        "titles=[]\n",
        "aims=[]\n",
        "mms=[]\n",
        "ress=[]\n",
        "concs=[] \n",
        "refs=[]\n",
        "abs=[]\n",
        "pmids = []\n",
        "cited_by = []\n",
        "\n",
        "mymax=100\n",
        "results = search(myquery,mymax)\n",
        "id_list = results['IdList'] # list of UIDs\n",
        "chunk_size = 50 # whatever you like   \n",
        "for chunk_i in range(0, len(id_list), chunk_size):\n",
        "    chunk = id_list[chunk_i:chunk_i + chunk_size]\n",
        " \n",
        "    papers = fetch_details(chunk)\n",
        "    for i, paper in enumerate(papers['PubmedArticle']):\n",
        "      try:\n",
        "          data = paper.get('PubmedData')\n",
        "          data2 = paper.get('MedlineCitation')\n",
        "          article = data2.get('Article')\n",
        "          title = article.get('ArticleTitle')\n",
        "          abstr = article.get('Abstract')\n",
        "          abstr = abstr.get('AbstractText')\n",
        "          ref = data.get('ReferenceList')\n",
        "          refs.append(ref)\n",
        "          pmids.append(data2.get('PMID'))\n",
        "          cited_by.append('')\n",
        "          try:\n",
        "            aim = abstr[0]\n",
        "            MM = abstr[1]\n",
        "            res = abstr[2]\n",
        "            conc = abstr[3]\n",
        "            titles.append(title)\n",
        "            aims.append(aim)\n",
        "            mms.append(MM)\n",
        "            ress.append(res)\n",
        "            concs.append(conc)\n",
        "            abs.append('')\n",
        "          except:\n",
        "            abs.append(abstr[0])\n",
        "            titles.append(title)\n",
        "            aims.append('')\n",
        "            mms.append('')\n",
        "            ress.append('')\n",
        "            concs.append('')\n",
        "      except:\n",
        "          pass\n"
      ],
      "execution_count": 8,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "190BWIK5jdcp"
      },
      "source": [
        "add them to a dataframe and download!"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "KcSzQdh-ffP4"
      },
      "source": [
        "data = {'title': titles, 'id':pmids,'abs':abs,'aims':aims,'MM':mms,'res':ress,'concl':concs} \n",
        "df2 = pd.DataFrame(data) "
      ],
      "execution_count": 9,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "JzhC_8WwSwQR",
        "outputId": "706d22ff-0245-466b-d40b-b393ef50839c",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 467
        }
      },
      "source": [
        "df2"
      ],
      "execution_count": 10,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<div>\n",
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              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>title</th>\n",
              "      <th>id</th>\n",
              "      <th>abs</th>\n",
              "      <th>aims</th>\n",
              "      <th>MM</th>\n",
              "      <th>res</th>\n",
              "      <th>concl</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>Radiomics: a Novel CT-Based Method of Predicti...</td>\n",
              "      <td>30441264</td>\n",
              "      <td>In order to predict the 3-year recurrence of a...</td>\n",
              "      <td></td>\n",
              "      <td></td>\n",
              "      <td></td>\n",
              "      <td></td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>Radiomics of high-grade serous ovarian cancer:...</td>\n",
              "      <td>29737390</td>\n",
              "      <td></td>\n",
              "      <td>To determine if radiomic features, alone or co...</td>\n",
              "      <td>This retrospective study enrolled 101 patients...</td>\n",
              "      <td>Patients with values of F2-Shape/Compactness1 ...</td>\n",
              "      <td>This study demonstrated significant associatio...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>Integration of proteomics with CT-based qualit...</td>\n",
              "      <td>32253542</td>\n",
              "      <td></td>\n",
              "      <td>To investigate the association between CT imag...</td>\n",
              "      <td>This retrospective, hypothesis-generating stud...</td>\n",
              "      <td>Four proteins were associated with CT-based im...</td>\n",
              "      <td>This study provides the first insights into th...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>Association between CT-texture-derived tumor h...</td>\n",
              "      <td>30474722</td>\n",
              "      <td></td>\n",
              "      <td>To assess the associations between inter-site ...</td>\n",
              "      <td>Retrospective study of 88 HGSOC patients under...</td>\n",
              "      <td>Higher inter-site cluster variance (SCV) was a...</td>\n",
              "      <td>The assessment of tumoral heterogeneity in the...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>Applying Quantitative CT Image Feature Analysi...</td>\n",
              "      <td>28554551</td>\n",
              "      <td></td>\n",
              "      <td>The study aimed to investigate the role of app...</td>\n",
              "      <td>A dataset involving 91 patients was retrospect...</td>\n",
              "      <td>The highest areas under the receiver operating...</td>\n",
              "      <td>This study demonstrated the feasibility of pre...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>5</th>\n",
              "      <td>A Computed Tomography-Based Radiomic Prognosti...</td>\n",
              "      <td>31024855</td>\n",
              "      <td>&lt;b&gt;Objectives:&lt;/b&gt; We used radiomic analysis t...</td>\n",
              "      <td></td>\n",
              "      <td></td>\n",
              "      <td></td>\n",
              "      <td></td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>6</th>\n",
              "      <td>Tissue-specific and interpretable sub-segmenta...</td>\n",
              "      <td>32421652</td>\n",
              "      <td></td>\n",
              "      <td>Cancer typically exhibits genotypic and phenot...</td>\n",
              "      <td>With the goal of achieving the automated sub-s...</td>\n",
              "      <td>On all tested sub-segmentation tasks, our two-...</td>\n",
              "      <td>The proposed intelligent framework could be re...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>7</th>\n",
              "      <td>A novel representation of inter-site tumour he...</td>\n",
              "      <td>28289945</td>\n",
              "      <td></td>\n",
              "      <td>To evaluate the associations between clinical ...</td>\n",
              "      <td>IRB-approved retrospective study of 38 HGSOC p...</td>\n",
              "      <td>Of the 12 inter-site texture heterogeneity met...</td>\n",
              "      <td>Quantitative metrics non-invasively capturing ...</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "                                               title  ...                                              concl\n",
              "0  Radiomics: a Novel CT-Based Method of Predicti...  ...                                                   \n",
              "1  Radiomics of high-grade serous ovarian cancer:...  ...  This study demonstrated significant associatio...\n",
              "2  Integration of proteomics with CT-based qualit...  ...  This study provides the first insights into th...\n",
              "3  Association between CT-texture-derived tumor h...  ...  The assessment of tumoral heterogeneity in the...\n",
              "4  Applying Quantitative CT Image Feature Analysi...  ...  This study demonstrated the feasibility of pre...\n",
              "5  A Computed Tomography-Based Radiomic Prognosti...  ...                                                   \n",
              "6  Tissue-specific and interpretable sub-segmenta...  ...  The proposed intelligent framework could be re...\n",
              "7  A novel representation of inter-site tumour he...  ...  Quantitative metrics non-invasively capturing ...\n",
              "\n",
              "[8 rows x 7 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 10
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "9JAImpmef-nD"
      },
      "source": [
        "df2.to_excel('output.xlsx') "
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "fyeilwqOgfWj"
      },
      "source": [
        "# 2. Get the graph for the citations"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "6QEtv_a4jkhh"
      },
      "source": [
        "Get the nodes and edges"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "NsMgPm-ybTYL"
      },
      "source": [
        "nodes = []\n",
        "edges = []\n",
        "new_ids=[]\n",
        "for i in range(len(refs)):\n",
        "  temp = refs[i]\n",
        "  name=str(pmids[i])\n",
        "  if temp:\n",
        "    if name not in nodes:\n",
        "      nodes.append(name)\n",
        "    temp2 = temp[0]\n",
        "    A = temp2.get('Reference')\n",
        "    for j in range(len(A)):\n",
        "      temp3 = A[j].get('ArticleIdList')\n",
        "      try:\n",
        "        name2 = str(temp3[0])\n",
        "        if name2 not in nodes:\n",
        "          nodes.append(name2)\n",
        "          new_ids.append(name2)\n",
        "\n",
        "        edges.append((name,name2))\n",
        "      except:\n",
        "        pass"
      ],
      "execution_count": 11,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "Y5R2qu-jjm-d"
      },
      "source": [
        "Build the graph"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "l3iGGZrDaPrq"
      },
      "source": [
        "g = nx.Graph()\n",
        "g.add_nodes_from(nodes)\n",
        "for i in range(len(edges)):\n",
        "  B = edges[i]\n",
        "  g.add_edge(B[0], B[1])\n"
      ],
      "execution_count": 12,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "QW-7Gv2mgvGU"
      },
      "source": [
        "color code the original papers in blue, the rest in red"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "ROrYZyFOgxqc"
      },
      "source": [
        "color_map = []\n",
        "for node in nodes:\n",
        "    if node in pmids:\n",
        "        color_map.append('blue')\n",
        "    else: \n",
        "        color_map.append('red')      "
      ],
      "execution_count": 13,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "TZ1XCqXmgzBi"
      },
      "source": [
        "get the graph"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "b6ckdJ7ma4iU",
        "outputId": "ed348169-5fa9-4256-a3b1-a85465197d45",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 319
        }
      },
      "source": [
        "nx.draw(g,node_size=100, node_color=color_map,alpha=0.5)"
      ],
      "execution_count": 14,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "BqjHryqzhCt1"
      },
      "source": [
        "Voilá!! "
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "mPODDlBLNinD"
      },
      "source": [
        "now we can also get the abstracts for the references"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "3FXqB-HQNWwD"
      },
      "source": [
        "mymax=100\n",
        "id_list = new_ids # list of UIDs\n",
        "chunk_size = 50 # whatever you like   \n",
        "for chunk_i in range(0, len(id_list), chunk_size):\n",
        "    chunk = id_list[chunk_i:chunk_i + chunk_size]\n",
        " \n",
        "    papers = fetch_details(chunk)\n",
        "    for i, paper in enumerate(papers['PubmedArticle']):\n",
        "      try:\n",
        "          data = paper.get('PubmedData')\n",
        "          data2 = paper.get('MedlineCitation')\n",
        "          article = data2.get('Article')\n",
        "          title = article.get('ArticleTitle')\n",
        "          abstr = article.get('Abstract')\n",
        "          abstr = abstr.get('AbstractText')\n",
        "          ref = data.get('ReferenceList')\n",
        "          refs.append(ref)\n",
        "          pmids.append(data2.get('PMID'))\n",
        "          cited_by.append(str(g.degree(str(data2.get('PMID')))))\n",
        "          try:\n",
        "            aim = abstr[0]\n",
        "            MM = abstr[1]\n",
        "            res = abstr[2]\n",
        "            conc = abstr[3]\n",
        "            titles.append(title)\n",
        "            aims.append(aim)\n",
        "            mms.append(MM)\n",
        "            ress.append(res)\n",
        "            concs.append(conc)\n",
        "            abs.append('')\n",
        "          except:\n",
        "            abs.append(abstr[0])\n",
        "            titles.append(title)\n",
        "            aims.append('')\n",
        "            mms.append('')\n",
        "            ress.append('')\n",
        "            concs.append('')\n",
        "      except:\n",
        "          pass\n"
      ],
      "execution_count": 15,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "0YMm5NuSNmeZ"
      },
      "source": [
        "add them to a dataframe"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "yRJeXfoYNcyU",
        "outputId": "6e82242b-8a19-40b1-e900-aaf11e3a4b16",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 657
        }
      },
      "source": [
        "data = {'title': titles, 'id':pmids,'abs':abs,'aims':aims,'MM':mms,'res':ress,'concl':concs,'degree':cited_by} \n",
        "df2 = pd.DataFrame(data) \n",
        "df2"
      ],
      "execution_count": 16,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>title</th>\n",
              "      <th>id</th>\n",
              "      <th>abs</th>\n",
              "      <th>aims</th>\n",
              "      <th>MM</th>\n",
              "      <th>res</th>\n",
              "      <th>concl</th>\n",
              "      <th>degree</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>Radiomics: a Novel CT-Based Method of Predicti...</td>\n",
              "      <td>30441264</td>\n",
              "      <td>In order to predict the 3-year recurrence of a...</td>\n",
              "      <td></td>\n",
              "      <td></td>\n",
              "      <td></td>\n",
              "      <td></td>\n",
              "      <td></td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>Radiomics of high-grade serous ovarian cancer:...</td>\n",
              "      <td>29737390</td>\n",
              "      <td></td>\n",
              "      <td>To determine if radiomic features, alone or co...</td>\n",
              "      <td>This retrospective study enrolled 101 patients...</td>\n",
              "      <td>Patients with values of F2-Shape/Compactness1 ...</td>\n",
              "      <td>This study demonstrated significant associatio...</td>\n",
              "      <td></td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>Integration of proteomics with CT-based qualit...</td>\n",
              "      <td>32253542</td>\n",
              "      <td></td>\n",
              "      <td>To investigate the association between CT imag...</td>\n",
              "      <td>This retrospective, hypothesis-generating stud...</td>\n",
              "      <td>Four proteins were associated with CT-based im...</td>\n",
              "      <td>This study provides the first insights into th...</td>\n",
              "      <td></td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>Association between CT-texture-derived tumor h...</td>\n",
              "      <td>30474722</td>\n",
              "      <td></td>\n",
              "      <td>To assess the associations between inter-site ...</td>\n",
              "      <td>Retrospective study of 88 HGSOC patients under...</td>\n",
              "      <td>Higher inter-site cluster variance (SCV) was a...</td>\n",
              "      <td>The assessment of tumoral heterogeneity in the...</td>\n",
              "      <td></td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>Applying Quantitative CT Image Feature Analysi...</td>\n",
              "      <td>28554551</td>\n",
              "      <td></td>\n",
              "      <td>The study aimed to investigate the role of app...</td>\n",
              "      <td>A dataset involving 91 patients was retrospect...</td>\n",
              "      <td>The highest areas under the receiver operating...</td>\n",
              "      <td>This study demonstrated the feasibility of pre...</td>\n",
              "      <td></td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>...</th>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>166</th>\n",
              "      <td>Analysis of tumor specimens at the time of acq...</td>\n",
              "      <td>23470965</td>\n",
              "      <td></td>\n",
              "      <td>All patients with EGF receptor (EGFR)-mutant l...</td>\n",
              "      <td>Patients with lung adenocarcinomas and acquire...</td>\n",
              "      <td>Adequate tumor samples for molecular analysis ...</td>\n",
              "      <td>This is the largest series reporting mechanism...</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>167</th>\n",
              "      <td>Histologic subtypes of ovarian carcinoma: an o...</td>\n",
              "      <td>18317227</td>\n",
              "      <td>Reproducible subclassification of ovarian carc...</td>\n",
              "      <td></td>\n",
              "      <td></td>\n",
              "      <td></td>\n",
              "      <td></td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>168</th>\n",
              "      <td>Radiomics: the process and the challenges.</td>\n",
              "      <td>22898692</td>\n",
              "      <td>\"Radiomics\" refers to the extraction and analy...</td>\n",
              "      <td></td>\n",
              "      <td></td>\n",
              "      <td></td>\n",
              "      <td></td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>169</th>\n",
              "      <td>Novel molecular subtypes of serous and endomet...</td>\n",
              "      <td>18698038</td>\n",
              "      <td></td>\n",
              "      <td>The study aim to identify novel molecular subt...</td>\n",
              "      <td>Microarray gene expression profiling was done ...</td>\n",
              "      <td>Optimal clustering of array data identified si...</td>\n",
              "      <td>Gene expression profiling identified molecular...</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>170</th>\n",
              "      <td>Genetic intra-tumour heterogeneity in epitheli...</td>\n",
              "      <td>17154249</td>\n",
              "      <td>Genetic analysis of solid tumours using DNA or...</td>\n",
              "      <td></td>\n",
              "      <td></td>\n",
              "      <td></td>\n",
              "      <td></td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>171 rows × 8 columns</p>\n",
              "</div>"
            ],
            "text/plain": [
              "                                                 title  ... degree\n",
              "0    Radiomics: a Novel CT-Based Method of Predicti...  ...       \n",
              "1    Radiomics of high-grade serous ovarian cancer:...  ...       \n",
              "2    Integration of proteomics with CT-based qualit...  ...       \n",
              "3    Association between CT-texture-derived tumor h...  ...       \n",
              "4    Applying Quantitative CT Image Feature Analysi...  ...       \n",
              "..                                                 ...  ...    ...\n",
              "166  Analysis of tumor specimens at the time of acq...  ...      1\n",
              "167  Histologic subtypes of ovarian carcinoma: an o...  ...      1\n",
              "168         Radiomics: the process and the challenges.  ...      1\n",
              "169  Novel molecular subtypes of serous and endomet...  ...      1\n",
              "170  Genetic intra-tumour heterogeneity in epitheli...  ...      1\n",
              "\n",
              "[171 rows x 8 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 16
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "XvJ72Za8No7Y"
      },
      "source": [
        "and we can save/download the document "
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "xuoOJmZpNhle"
      },
      "source": [
        "df2.to_excel('output.xlsx') "
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "_NSKAwV5g0cL"
      },
      "source": [
        "Some of the posts I've used as references:\n",
        "\n",
        "https://medium.com/@kliang933/scraping-big-data-from-public-research-repositories-e-g-pubmed-arxiv-2-488666f6f29b\n",
        "\n",
        "https://github.com/CrossRef/rest-api-doc#queries\n"
      ]
    }
  ]
}