https://github.com/MDverse/mdda
Tip revision: 4562c50d1b51a51fdf952ae6e9efaa407dd06e20 authored by Pierre Poulain on 16 May 2024, 15:18:06 UTC
Add previous results (2024-04-23) in HTML format
Add previous results (2024-04-23) in HTML format
Tip revision: 4562c50
search_MD_in_pubmed.ipynb
{
"cells": [
{
"cell_type": "markdown",
"id": "d6a404d9-ab10-4b76-83f8-0359e519db49",
"metadata": {},
"source": [
"# Search for Molecular Dynamics publications in PubMed\n",
"\n",
"Results can be verified [PubMed](https://pubmed.ncbi.nlm.nih.gov/?term=%22molecular+dynamics%22&filter=years.2000-2022&sort=date)"
]
},
{
"cell_type": "code",
"execution_count": 1,
"id": "eb166a0e-2239-40ac-aebd-42722ea2cb50",
"metadata": {},
"outputs": [],
"source": [
"from Bio import Entrez\n",
"import matplotlib.pyplot as plt\n",
"import pandas as pd"
]
},
{
"cell_type": "code",
"execution_count": 2,
"id": "231d7da4-6cda-4506-8fec-a55363282b4f",
"metadata": {
"tags": []
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Python implementation: CPython\n",
"Python version : 3.9.16\n",
"IPython version : 8.6.0\n",
"\n",
"Compiler : GCC 11.3.0\n",
"OS : Linux\n",
"Release : 5.15.0-67-generic\n",
"Machine : x86_64\n",
"Processor : x86_64\n",
"CPU cores : 8\n",
"Architecture: 64bit\n",
"\n",
"jupyterlab: 3.6.1\n",
"\n",
"Bio : 1.81\n",
"pandas : 1.5.3\n",
"matplotlib: 3.5.3\n",
"\n",
"Watermark: 2.3.1\n",
"\n",
"conda environment: mdda\n",
"\n"
]
}
],
"source": [
"%load_ext watermark\n",
"%watermark --python --machine\n",
"# Python packages versions\n",
"%watermark --packages jupyterlab --iversions --watermark\n",
"# conda environment name\n",
"%watermark --conda"
]
},
{
"cell_type": "markdown",
"id": "bb021859-98b5-4c89-8109-76040a3c874a",
"metadata": {},
"source": [
"Initiate Entrez module with your own e-mail address."
]
},
{
"cell_type": "code",
"execution_count": 3,
"id": "35ff6c48-e620-4990-81fa-ed8630969d25",
"metadata": {},
"outputs": [],
"source": [
"# Dummy e-mail address. Use your own.\n",
"Entrez.email = \"bob@email.com\""
]
},
{
"cell_type": "markdown",
"id": "6f09dd42-1279-4fd8-bb2d-4c258eaad44c",
"metadata": {},
"source": [
"Define function to query Pubmed per year"
]
},
{
"cell_type": "code",
"execution_count": 4,
"id": "3c3a9b1a-b036-4f26-b3f1-7b59ce5535e4",
"metadata": {},
"outputs": [],
"source": [
"def query_pubmed(term=\"\", year_start=2000, year_end=2022):\n",
" years = []\n",
" paper_count = []\n",
" for year in range(year_start, year_end+1):\n",
" query = (f'(\"{term}\") AND '\n",
" f'(\"{year}/01/01\"[Date - Publication] : \"{year}/12/31\"[Date - Publication])'\n",
" )\n",
" esearch_res = Entrez.read(Entrez.esearch(db=\"pubmed\", term=query))\n",
" years.append(year)\n",
" paper_count.append(int(esearch_res[\"Count\"]))\n",
" return years, paper_count"
]
},
{
"cell_type": "markdown",
"id": "1af0dfb6-dadd-4def-b0d0-50af04724ee4",
"metadata": {},
"source": [
"## Molecular dynamics"
]
},
{
"cell_type": "code",
"execution_count": 5,
"id": "59784436-9a67-410d-9fb5-779d82e27e1b",
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Querying MD...\n"
]
}
],
"source": [
"print(\"Querying MD...\")\n",
"years, md = query_pubmed(term=\"'molecular dynamics'[Title/Abstract]\", year_start=2000, year_end=2022)\n",
"df_md = pd.DataFrame({\"years\": years, \"MD\": md})\n",
"df_md.to_csv(\"number_of_MD_papers_per_year.tsv\", sep=\"\\t\", index=False)"
]
},
{
"cell_type": "code",
"execution_count": 6,
"id": "ee05c48c-5b31-4ca1-9c77-32ee43a67472",
"metadata": {
"tags": []
},
"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>years</th>\n",
" <th>MD</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>0</th>\n",
" <td>2000</td>\n",
" <td>570</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1</th>\n",
" <td>2001</td>\n",
" <td>666</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2</th>\n",
" <td>2002</td>\n",
" <td>827</td>\n",
" </tr>\n",
" <tr>\n",
" <th>3</th>\n",
" <td>2003</td>\n",
" <td>881</td>\n",
" </tr>\n",
" <tr>\n",
" <th>4</th>\n",
" <td>2004</td>\n",
" <td>1412</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" years MD\n",
"0 2000 570\n",
"1 2001 666\n",
"2 2002 827\n",
"3 2003 881\n",
"4 2004 1412"
]
},
"execution_count": 6,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"df_md.head()"
]
},
{
"cell_type": "code",
"execution_count": 7,
"id": "73a08751-08fe-430d-9612-d7b195f76e16",
"metadata": {},
"outputs": [
{
"data": {
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\n",
"text/plain": [
"<Figure size 1200x700 with 1 Axes>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"plt.rcParams.update({\"font.size\": 16})\n",
"fig, ax = plt.subplots(figsize=(12,7))\n",
"ax.bar(df_md[\"years\"], df_md[\"MD\"], color=\"C1\")\n",
"ax.set_title(\"Number of MD publications per year\")\n",
"ax.set_xlabel(\"Year\", weight=\"bold\")\n",
"ax.set_ylabel(\"Number of publications\", weight=\"bold\")\n",
"fig.savefig(\"fig/number_of_MD_papers_per_year.svg\")"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "ed48118e-4b38-45bf-96b0-397dc9676c7a",
"metadata": {},
"outputs": [],
"source": []
}
],
"metadata": {
"kernelspec": {
"display_name": "Python 3 (ipykernel)",
"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": "3.9.16"
}
},
"nbformat": 4,
"nbformat_minor": 5
}
