Datasets
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{
"cells": [
{
"cell_type": "markdown",
"metadata": {},
"source": [
"**Aims**:\n",
" - list high-impact works to aid navigation of the field\n",
" - check for unexpectedly common authors/affiliations/journals to screening for potential false-positive matches (see the Integromics and Panomics companies)"
]
},
{
"cell_type": "code",
"execution_count": 1,
"metadata": {},
"outputs": [],
"source": [
"%run notebook_setup.ipynb"
]
},
{
"cell_type": "code",
"execution_count": 2,
"metadata": {},
"outputs": [
{
"data": {
"text/markdown": [
"Imported:\n",
"\n",
" - `literature` (904B0F94)\n",
" - `affiliations` (E06399F2)\n",
" - `authors` (DC49BC74)\n",
" - `publication_types` (7DD4E741)\n",
"\n",
"at Wednesday, 05. Aug 2020 16:22"
],
"text/plain": [
"<IPython.core.display.Markdown object>"
]
},
"metadata": {
"text/markdown": {
"action": "import",
"command": "from pubmed_derived_data import literature, affiliations, authors, publication_types",
"finished": "2020-08-05T16:22:34.123010",
"finished_human_readable": "Wednesday, 05. Aug 2020 16:22",
"result": [
{
"new_file": {
"crc32": "904B0F94",
"sha256": "A2EFC068A287A3B724AE4B320EE5356E1E99474BD08A2E2A3EBA34CD0194F23B"
},
"subject": "literature"
},
{
"new_file": {
"crc32": "E06399F2",
"sha256": "8DD13D4B7CF3D2E314BBC4E051AEDBF21414371F42BB4D100D7721B5F4D24E60"
},
"subject": "affiliations"
},
{
"new_file": {
"crc32": "DC49BC74",
"sha256": "237BEFD0FDA68E2A155B9EC00519017B4C9BC92BD2AA3D10E058A013EC0DE1D9"
},
"subject": "authors"
},
{
"new_file": {
"crc32": "7DD4E741",
"sha256": "BD0EBF88B38BB9E0E44923E2CB473A532AEFBFFC6A7FCC02926290CAD2615150"
},
"subject": "publication_types"
}
],
"started": "2020-08-05T16:22:30.319280"
}
},
"output_type": "display_data"
}
],
"source": [
"%vault from pubmed_derived_data import literature, affiliations, authors, publication_types"
]
},
{
"cell_type": "code",
"execution_count": 3,
"metadata": {},
"outputs": [
{
"data": {
"text/markdown": [
"Imported:\n",
"\n",
" - `web_of_science_journals` (E95CE31E)\n",
" - `scimago_by_issn` (DDCBFB24)\n",
"\n",
"at Wednesday, 05. Aug 2020 16:22"
],
"text/plain": [
"<IPython.core.display.Markdown object>"
]
},
"metadata": {
"text/markdown": {
"action": "import",
"command": "from journals_data import web_of_science_journals, scimago_by_issn",
"finished": "2020-08-05T16:22:36.022687",
"finished_human_readable": "Wednesday, 05. Aug 2020 16:22",
"result": [
{
"new_file": {
"crc32": "E95CE31E",
"sha256": "55F51248C28FEEC07B4E5A98AD3660519AD3566DC9B61985279E6D4C9B374BF8"
},
"subject": "web_of_science_journals"
},
{
"new_file": {
"crc32": "DDCBFB24",
"sha256": "B16E18A78F3247A03950A39AB7B64E92EAFA747074BB6B2DBFEBDA7DCA5902D3"
},
"subject": "scimago_by_issn"
}
],
"started": "2020-08-05T16:22:34.145419"
}
},
"output_type": "display_data"
}
],
"source": [
"%vault from journals_data import web_of_science_journals, scimago_by_issn"
]
},
{
"cell_type": "code",
"execution_count": 4,
"metadata": {},
"outputs": [],
"source": [
"literature['journal_sjr_rank'] = (\n",
" literature['journal_issn']\n",
" .str.replace('-', '')\n",
" .fillna('-')\n",
" .apply(\n",
" lambda x: (\n",
" scimago_by_issn.loc[x].Rank\n",
" if x in scimago_by_issn.index else\n",
" None\n",
" )\n",
" )\n",
")"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## A quick overview/hot-takes"
]
},
{
"cell_type": "code",
"execution_count": 5,
"metadata": {},
"outputs": [],
"source": [
"columns_to_show = ['title', 'journal', 'doi', 'journal_sjr_rank']"
]
},
{
"cell_type": "code",
"execution_count": 6,
"metadata": {},
"outputs": [],
"source": [
"def display_sorted(data):\n",
" return data[columns_to_show].sort_values(['journal_sjr_rank', 'title'])"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### Benchmarks"
]
},
{
"cell_type": "code",
"execution_count": 7,
"metadata": {},
"outputs": [
{
"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>journal</th>\n",
" <th>doi</th>\n",
" <th>journal_sjr_rank</th>\n",
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" <tbody>\n",
" <tr>\n",
" <th>30295871</th>\n",
" <td>Multi-omic and multi-view clustering algorithm...</td>\n",
" <td>Nucleic acids research</td>\n",
" <td>10.1093/nar/gky889</td>\n",
" <td>90.0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>30496480</th>\n",
" <td>Multi-omic and multi-view clustering algorithm...</td>\n",
" <td>Nucleic acids research</td>\n",
" <td>10.1093/nar/gky1226</td>\n",
" <td>90.0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>22121217</th>\n",
" <td>The Stem Cell Discovery Engine: an integrated ...</td>\n",
" <td>Nucleic acids research</td>\n",
" <td>10.1093/nar/gkr1051</td>\n",
" <td>90.0</td>\n",
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" <tr>\n",
" <th>32234303</th>\n",
" <td>Multiomics Evaluation of Gastrointestinal and ...</td>\n",
" <td>Gastroenterology</td>\n",
" <td>10.1053/j.gastro.2020.03.045</td>\n",
" <td>169.0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>31154149</th>\n",
" <td>Quantitative CMR population imaging on 20,000 ...</td>\n",
" <td>Medical image analysis</td>\n",
" <td>10.1016/j.media.2019.05.006</td>\n",
" <td>409.0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>30068331</th>\n",
" <td>Species comparison of liver proteomes reveals ...</td>\n",
" <td>BMC biology</td>\n",
" <td>10.1186/s12915-018-0547-y</td>\n",
" <td>447.0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>32437529</th>\n",
" <td>Integrating multi-OMICS data through sparse Ca...</td>\n",
" <td>Bioinformatics (Oxford, England)</td>\n",
" <td>10.1093/bioinformatics/btaa530</td>\n",
" <td>484.0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>31792509</th>\n",
" <td>Clustering and variable selection evaluation o...</td>\n",
" <td>Briefings in bioinformatics</td>\n",
" <td>10.1093/bib/bbz138</td>\n",
" <td>625.0</td>\n",
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" <tr>\n",
" <th>29688321</th>\n",
" <td>Comparison and evaluation of integrative metho...</td>\n",
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" <td>625.0</td>\n",
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" <tr>\n",
" <th>31220206</th>\n",
" <td>Evaluation of integrative clustering methods f...</td>\n",
" <td>Briefings in bioinformatics</td>\n",
" <td>10.1093/bib/bbz015</td>\n",
" <td>625.0</td>\n",
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" <tr>\n",
" <th>29272335</th>\n",
" <td>Multi-omics integration-a comparison of unsupe...</td>\n",
" <td>Briefings in bioinformatics</td>\n",
" <td>10.1093/bib/bbx167</td>\n",
" <td>625.0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>30368064</th>\n",
" <td>Multi-omics at single-cell resolution: compari...</td>\n",
" <td>Current opinion in biotechnology</td>\n",
" <td>10.1016/j.copbio.2018.09.012</td>\n",
" <td>740.0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>32255618</th>\n",
" <td>Evaluation of Microbiome-Host Relationships in...</td>\n",
" <td>Environmental science & technology</td>\n",
" <td>10.1021/acs.est.0c00628</td>\n",
" <td>797.0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>25414848</th>\n",
" <td>Multi-omic landscape of rheumatoid arthritis: ...</td>\n",
" <td>Frontiers in cell and developmental biology</td>\n",
" <td>10.3389/fcell.2014.00059</td>\n",
" <td>862.0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>31292535</th>\n",
" <td>Multi-omic molecular comparison of primary ver...</td>\n",
" <td>British journal of cancer</td>\n",
" <td>10.1038/s41416-019-0507-5</td>\n",
" <td>941.0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>30866779</th>\n",
" <td>Multi-omics comparisons of p-aminosalicylic ac...</td>\n",
" <td>Emerging microbes & infections</td>\n",
" <td>10.1080/22221751.2019.1568179</td>\n",
" <td>1103.0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>22954204</th>\n",
" <td>Systematic comparison of reverse phase and hyd...</td>\n",
" <td>Analytical chemistry</td>\n",
" <td>10.1021/ac3012494</td>\n",
" <td>1178.0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>24216987</th>\n",
" <td>Metabolomic Dynamic Analysis of Hypoxia in MDA...</td>\n",
" <td>Cancers</td>\n",
" <td>10.3390/cancers5020491</td>\n",
" <td>1388.0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>29936248</th>\n",
" <td>U-BIOPRED: evaluation of the value of a public...</td>\n",
" <td>Drug discovery today</td>\n",
" <td>10.1016/j.drudis.2018.06.015</td>\n",
" <td>1444.0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>31312416</th>\n",
" <td>An Evaluation of Machine Learning Approaches f...</td>\n",
" <td>Computational and structural biotechnology jou...</td>\n",
" <td>10.1016/j.csbj.2019.05.008</td>\n",
" <td>1637.0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>29213276</th>\n",
" <td>An Integrated \"Multi-Omics\" Comparison of Embr...</td>\n",
" <td>Frontiers in plant science</td>\n",
" <td>10.3389/fpls.2017.01984</td>\n",
" <td>1794.0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>29740416</th>\n",
" <td>SplinectomeR Enables Group Comparisons in Long...</td>\n",
" <td>Frontiers in microbiology</td>\n",
" <td>10.3389/fmicb.2018.00785</td>\n",
" <td>1798.0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>29920461</th>\n",
" <td>Multi 'omics comparison reveals metabolome bio...</td>\n",
" <td>The Science of the total environment</td>\n",
" <td>10.1016/j.scitotenv.2018.05.256</td>\n",
" <td>1854.0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>29212468</th>\n",
" <td>A comparison of graph- and kernel-based -omics...</td>\n",
" <td>BMC bioinformatics</td>\n",
" <td>10.1186/s12859-017-1982-4</td>\n",
" <td>1930.0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>30234027</th>\n",
" <td>Comparison of Fecal Collection Methods for Mic...</td>\n",
" <td>Frontiers in cellular and infection microbiology</td>\n",
" <td>10.3389/fcimb.2018.00301</td>\n",
" <td>1931.0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>31792061</th>\n",
" <td>Evaluation of colorectal cancer subtypes and c...</td>\n",
" <td>Life science alliance</td>\n",
" <td>10.26508/lsa.201900517</td>\n",
" <td>2050.0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>31649733</th>\n",
" <td>CEPICS: A Comparison and Evaluation Platform f...</td>\n",
" <td>Frontiers in genetics</td>\n",
" <td>10.3389/fgene.2019.00966</td>\n",
" <td>2313.0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>27929400</th>\n",
" <td>A Systematic Evaluation of Blood Serum and Pla...</td>\n",
" <td>International journal of molecular sciences</td>\n",
" <td>NaN</td>\n",
" <td>2808.0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>32386347</th>\n",
" <td>Comparison of Proteomic Assessment Methods in ...</td>\n",
" <td>Proteomics</td>\n",
" <td>10.1002/pmic.201900278</td>\n",
" <td>2944.0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>22796353</th>\n",
" <td>Effects of pre-storage leukoreduction on store...</td>\n",
" <td>Journal of proteomics</td>\n",
" <td>10.1016/j.jprot.2012.06.032</td>\n",
" <td>3300.0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>32252356</th>\n",
" <td>Deep Functional Profiling Facilitates the Eval...</td>\n",
" <td>Antibiotics (Basel, Switzerland)</td>\n",
" <td>10.3390/antibiotics9040157</td>\n",
" <td>3371.0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>30596412</th>\n",
" <td>Growth Performance and Meat Quality Evaluation...</td>\n",
" <td>Journal of agricultural and food chemistry</td>\n",
" <td>10.1021/acs.jafc.8b05477</td>\n",
" <td>3803.0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>28927277</th>\n",
" <td>Benchmark Dose Modeling Estimates of the Conce...</td>\n",
" <td>Chemical research in toxicology</td>\n",
" <td>10.1021/acs.chemrestox.7b00221</td>\n",
" <td>4330.0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>31942259</th>\n",
" <td>A multiomics comparison between endometrial ca...</td>\n",
" <td>PeerJ</td>\n",
" <td>10.7717/peerj.8347</td>\n",
" <td>4381.0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>28804556</th>\n",
" <td>Anti-tumor efficacy evaluation of a novel mono...</td>\n",
" <td>American journal of translational research</td>\n",
" <td>NaN</td>\n",
" <td>4480.0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>21081137</th>\n",
" <td>Cross-study and cross-omics comparisons of thr...</td>\n",
" <td>Toxicology and applied pharmacology</td>\n",
" <td>10.1016/j.taap.2010.11.006</td>\n",
" <td>4714.0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>28546903</th>\n",
" <td>Comprehensive reconstruction and evaluation of...</td>\n",
" <td>Bioresources and bioprocessing</td>\n",
" <td>10.1186/s40643-017-0152-x</td>\n",
" <td>5398.0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>26495307</th>\n",
" <td>Cross-omics comparison of stress responses in ...</td>\n",
" <td>BioMed research international</td>\n",
" <td>10.1155/2015/628158</td>\n",
" <td>7157.0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>28521615</th>\n",
" <td>Comprehensive Evaluation of (+)-Usnic Acid-ind...</td>\n",
" <td>Toxicologic pathology</td>\n",
" <td>10.1177/0192623317707074</td>\n",
" <td>7826.0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>17785938</th>\n",
" <td>Evaluation of human hepatocyte chimeric mice a...</td>\n",
" <td>The Journal of toxicological sciences</td>\n",
" <td>NaN</td>\n",
" <td>8472.0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>31486672</th>\n",
" <td>: A Novel Bayesian Network Structural Learning...</td>\n",
" <td>Journal of computational biology : a journal o...</td>\n",
" <td>10.1089/cmb.2019.0210</td>\n",
" <td>8932.0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>28994524</th>\n",
" <td>[Clinical value evaluation of Chinese herbal f...</td>\n",
" <td>Zhongguo Zhong yao za zhi = Zhongguo zhongyao ...</td>\n",
" <td>10.19540/j.cnki.cjcmm.20170103.001</td>\n",
" <td>21244.0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>21765119</th>\n",
" <td>A comparison of the cyclic variation in serum ...</td>\n",
" <td>Biological research for nursing</td>\n",
" <td>10.1177/1099800411412766</td>\n",
" <td>NaN</td>\n",
" </tr>\n",
" <tr>\n",
" <th>24598031</th>\n",
" <td>A cross-omics toxicological evaluation of drin...</td>\n",
" <td>Journal of hazardous materials</td>\n",
" <td>10.1016/j.jhazmat.2014.02.007</td>\n",
" <td>NaN</td>\n",
" </tr>\n",
" <tr>\n",
" <th>31344359</th>\n",
" <td>Before and After: Comparison of Legacy and Har...</td>\n",
" <td>Cell systems</td>\n",
" <td>10.1016/j.cels.2019.06.006</td>\n",
" <td>NaN</td>\n",
" </tr>\n",
" <tr>\n",
" <th>32585153</th>\n",
" <td>Evaluation of Bunina et al.: Synthesizing Mult...</td>\n",
" <td>Cell systems</td>\n",
" <td>10.1016/j.cels.2020.06.002</td>\n",
" <td>NaN</td>\n",
" </tr>\n",
" <tr>\n",
" <th>32156385</th>\n",
" <td>Foodomics evaluation of the anti-proliferative...</td>\n",
" <td>Food research international (Ottawa, Ont.)</td>\n",
" <td>10.1016/j.foodres.2019.108938</td>\n",
" <td>NaN</td>\n",
" </tr>\n",
" <tr>\n",
" <th>32117598</th>\n",
" <td>Multiomics Evaluation of Human Fat-Derived Mes...</td>\n",
" <td>BioResearch open access</td>\n",
" <td>10.1089/biores.2020.0005</td>\n",
" <td>NaN</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" title \\\n",
"uid \n",
"30295871 Multi-omic and multi-view clustering algorithm... \n",
"30496480 Multi-omic and multi-view clustering algorithm... \n",
"22121217 The Stem Cell Discovery Engine: an integrated ... \n",
"32234303 Multiomics Evaluation of Gastrointestinal and ... \n",
"31154149 Quantitative CMR population imaging on 20,000 ... \n",
"30068331 Species comparison of liver proteomes reveals ... \n",
"32437529 Integrating multi-OMICS data through sparse Ca... \n",
"31792509 Clustering and variable selection evaluation o... \n",
"29688321 Comparison and evaluation of integrative metho... \n",
"31220206 Evaluation of integrative clustering methods f... \n",
"29272335 Multi-omics integration-a comparison of unsupe... \n",
"30368064 Multi-omics at single-cell resolution: compari... \n",
"32255618 Evaluation of Microbiome-Host Relationships in... \n",
"25414848 Multi-omic landscape of rheumatoid arthritis: ... \n",
"31292535 Multi-omic molecular comparison of primary ver... \n",
"30866779 Multi-omics comparisons of p-aminosalicylic ac... \n",
"22954204 Systematic comparison of reverse phase and hyd... \n",
"24216987 Metabolomic Dynamic Analysis of Hypoxia in MDA... \n",
"29936248 U-BIOPRED: evaluation of the value of a public... \n",
"31312416 An Evaluation of Machine Learning Approaches f... \n",
"29213276 An Integrated \"Multi-Omics\" Comparison of Embr... \n",
"29740416 SplinectomeR Enables Group Comparisons in Long... \n",
"29920461 Multi 'omics comparison reveals metabolome bio... \n",
"29212468 A comparison of graph- and kernel-based -omics... \n",
"30234027 Comparison of Fecal Collection Methods for Mic... \n",
"31792061 Evaluation of colorectal cancer subtypes and c... \n",
"31649733 CEPICS: A Comparison and Evaluation Platform f... \n",
"27929400 A Systematic Evaluation of Blood Serum and Pla... \n",
"32386347 Comparison of Proteomic Assessment Methods in ... \n",
"22796353 Effects of pre-storage leukoreduction on store... \n",
"32252356 Deep Functional Profiling Facilitates the Eval... \n",
"30596412 Growth Performance and Meat Quality Evaluation... \n",
"28927277 Benchmark Dose Modeling Estimates of the Conce... \n",
"31942259 A multiomics comparison between endometrial ca... \n",
"28804556 Anti-tumor efficacy evaluation of a novel mono... \n",
"21081137 Cross-study and cross-omics comparisons of thr... \n",
"28546903 Comprehensive reconstruction and evaluation of... \n",
"26495307 Cross-omics comparison of stress responses in ... \n",
"28521615 Comprehensive Evaluation of (+)-Usnic Acid-ind... \n",
"17785938 Evaluation of human hepatocyte chimeric mice a... \n",
"31486672 : A Novel Bayesian Network Structural Learning... \n",
"28994524 [Clinical value evaluation of Chinese herbal f... \n",
"21765119 A comparison of the cyclic variation in serum ... \n",
"24598031 A cross-omics toxicological evaluation of drin... \n",
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"24216987 Cancers \n",
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"32252356 Antibiotics (Basel, Switzerland) \n",
"30596412 Journal of agricultural and food chemistry \n",
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"26495307 BioMed research international \n",
"28521615 Toxicologic pathology \n",
"17785938 The Journal of toxicological sciences \n",
"31486672 Journal of computational biology : a journal o... \n",
"28994524 Zhongguo Zhong yao za zhi = Zhongguo zhongyao ... \n",
"21765119 Biological research for nursing \n",
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},
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"metadata": {},
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}
],
"source": [
"literature[literature.title.str.lower().str.contains('|'.join(['benchmark', 'evaluation', 'comparison']))].pipe(display_sorted)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### Biomarkers"
]
},
{
"cell_type": "code",
"execution_count": 8,
"metadata": {},
"outputs": [
{
"data": {
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" <tr>\n",
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" <td>Multi-omics in IBD biomarker discovery: the mi...</td>\n",
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" <td>10.1038/s41575-019-0188-9</td>\n",
" <td>196.0</td>\n",
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" <tr>\n",
" <th>...</th>\n",
" <td>...</td>\n",
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" <tr>\n",
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" <tr>\n",
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],
"text/plain": [
" title \\\n",
"uid \n",
"31563876 Exploiting differential Wnt target gene expres... \n",
"28838933 Epigenome-Wide Association Study Identifies Ca... \n",
"24859455 Identification of prognostic biomarkers in hep... \n",
"31501510 Multi-omic biomarker identification and valida... \n",
"31312043 Multi-omics in IBD biomarker discovery: the mi... \n",
"... ... \n",
"29764059 Leveraging next-generation phenotyping and pan... \n",
"31882086 Meta-proteomics for the discovery of protein b... \n",
"29781548 Multi-omics in high-grade serous ovarian cance... \n",
"29686467 Multiomics biomarkers for the prediction of no... \n",
"26312246 Wading through the noise of \"multi-omics\" to i... \n",
"\n",
" journal \\\n",
"uid \n",
"31563876 Gut \n",
"28838933 Circulation \n",
"24859455 Journal of hepatology \n",
"31501510 Molecular psychiatry \n",
"31312043 Nature reviews. Gastroenterology & hepatology \n",
"... ... \n",
"29764059 Personalized medicine \n",
"31882086 Food research international (Ottawa, Ont.) \n",
"29781548 American journal of reproductive immunology (N... \n",
"29686467 World journal of gastroenterology \n",
"26312246 Hepatobiliary surgery and nutrition \n",
"\n",
" doi journal_sjr_rank \n",
"uid \n",
"31563876 10.1136/gutjnl-2019-319126 121.0 \n",
"28838933 10.1161/CIRCULATIONAHA.117.027355 142.0 \n",
"24859455 10.1016/j.jhep.2014.05.025 171.0 \n",
"31501510 10.1038/s41380-019-0496-z 187.0 \n",
"31312043 10.1038/s41575-019-0188-9 196.0 \n",
"... ... ... \n",
"29764059 10.2217/pme.14.6 NaN \n",
"31882086 10.1016/j.foodres.2019.108739 NaN \n",
"29781548 10.1111/aji.12975 NaN \n",
"29686467 10.3748/wjg.v24.i15.1601 NaN \n",
"26312246 10.3978/j.issn.2304-3881.2015.04.05 NaN \n",
"\n",
"[141 rows x 4 columns]"
]
},
"execution_count": 8,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"literature[literature.title.str.lower().str.contains('|'.join(['biomarker']))].pipe(display_sorted)"
]
},
{
"cell_type": "code",
"execution_count": 9,
"metadata": {},
"outputs": [
{
"data": {
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"32589957 Molecular Transducers of Physical Activity Con... \n",
"32579974 Multimodal Analysis of Composition and Spatial... \n",
"30193112 Personalized Gut Mucosal Colonization Resistan... \n",
"32649874 Proteogenomic Characterization Reveals Therape... \n",
"32059776 Proteogenomic Characterization of Endometrial ... \n",
"32649875 Proteogenomics of Non-smoking Lung Cancer in E... \n",
"29677503 Revolutionizing Precision Oncology through Col... \n",
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]
},
"execution_count": 9,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"literature.pipe(display_sorted).head(20)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### Discoveries?"
]
},
{
"cell_type": "code",
"execution_count": 10,
"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>title</th>\n",
" <th>journal</th>\n",
" <th>doi</th>\n",
" <th>journal_sjr_rank</th>\n",
" </tr>\n",
" <tr>\n",
" <th>uid</th>\n",
" <th></th>\n",
" <th></th>\n",
" <th></th>\n",
" <th></th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>32649874</th>\n",
" <td>Proteogenomic Characterization Reveals Therape...</td>\n",
" <td>Cell</td>\n",
" <td>10.1016/j.cell.2020.06.013</td>\n",
" <td>8.0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>29328921</th>\n",
" <td>Tomato Multiomics Reveals Consequences of Crop...</td>\n",
" <td>Cell</td>\n",
" <td>10.1016/j.cell.2017.12.036</td>\n",
" <td>8.0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>28285833</th>\n",
" <td>Integrative Proteomics and Phosphoproteomics P...</td>\n",
" <td>Immunity</td>\n",
" <td>10.1016/j.immuni.2017.02.010</td>\n",
" <td>50.0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>29898900</th>\n",
" <td>Principled multi-omic analysis reveals gene re...</td>\n",
" <td>Genome research</td>\n",
" <td>10.1101/gr.227066.117</td>\n",
" <td>84.0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>32182340</th>\n",
" <td>Multi-omic analysis of gametogenesis reveals a...</td>\n",
" <td>Nucleic acids research</td>\n",
" <td>10.1093/nar/gkaa163</td>\n",
" <td>90.0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>31227589</th>\n",
" <td>Integrated multiomic analysis reveals comprehe...</td>\n",
" <td>Gut</td>\n",
" <td>10.1136/gutjnl-2019-318912</td>\n",
" <td>121.0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>30268436</th>\n",
" <td>A Pan-Cancer Analysis Reveals High-Frequency G...</td>\n",
" <td>Cell systems</td>\n",
" <td>10.1016/j.cels.2018.08.010</td>\n",
" <td>131.0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>28544881</th>\n",
" <td>Mammalian Systems Biotechnology Reveals Global...</td>\n",
" <td>Cell systems</td>\n",
" <td>10.1016/j.cels.2017.04.009</td>\n",
" <td>131.0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>26446169</th>\n",
" <td>A Cross-Species Analysis in Pancreatic Neuroen...</td>\n",
" <td>Cancer discovery</td>\n",
" <td>10.1158/2159-8290.CD-15-0068</td>\n",
" <td>137.0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>29588317</th>\n",
" <td>Spatiotemporal Multi-Omics Mapping Generates a...</td>\n",
" <td>Circulation</td>\n",
" <td>10.1161/CIRCULATIONAHA.117.032291</td>\n",
" <td>142.0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>27132591</th>\n",
" <td>Integrative Multi-omic Analysis of Human Plate...</td>\n",
" <td>American journal of human genetics</td>\n",
" <td>10.1016/j.ajhg.2016.03.007</td>\n",
" <td>143.0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>32023468</th>\n",
" <td>Inverse Data-Driven Modeling and Multiomics An...</td>\n",
" <td>Cell reports</td>\n",
" <td>10.1016/j.celrep.2020.01.011</td>\n",
" <td>203.0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>32522993</th>\n",
" <td>A multi-omics analysis reveals the unfolded pr...</td>\n",
" <td>Nature communications</td>\n",
" <td>10.1038/s41467-020-16747-y</td>\n",
" <td>238.0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>30692544</th>\n",
" <td>Deconvolution of single-cell multi-omics layer...</td>\n",
" <td>Nature communications</td>\n",
" <td>10.1038/s41467-018-08205-7</td>\n",
" <td>238.0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>31604924</th>\n",
" <td>Integrated molecular characterization of chond...</td>\n",
" <td>Nature communications</td>\n",
" <td>10.1038/s41467-019-12525-7</td>\n",
" <td>238.0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>29500431</th>\n",
" <td>Integrative analysis of omics summary data rev...</td>\n",
" <td>Nature communications</td>\n",
" <td>10.1038/s41467-018-03371-0</td>\n",
" <td>238.0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>29615613</th>\n",
" <td>Multi-omics analysis reveals neoantigen-indepe...</td>\n",
" <td>Nature communications</td>\n",
" <td>10.1038/s41467-018-03730-x</td>\n",
" <td>238.0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>29713003</th>\n",
" <td>Multi-omics profiling of younger Asian breast ...</td>\n",
" <td>Nature communications</td>\n",
" <td>10.1038/s41467-018-04129-4</td>\n",
" <td>238.0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>32312967</th>\n",
" <td>Nitrogen limitation reveals large reserves in ...</td>\n",
" <td>Nature communications</td>\n",
" <td>10.1038/s41467-020-15749-0</td>\n",
" <td>238.0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>28408661</th>\n",
" <td>Multi-Omics of Tomato Glandular Trichomes Reve...</td>\n",
" <td>The Plant cell</td>\n",
" <td>10.1105/tpc.17.00060</td>\n",
" <td>254.0</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" title \\\n",
"uid \n",
"32649874 Proteogenomic Characterization Reveals Therape... \n",
"29328921 Tomato Multiomics Reveals Consequences of Crop... \n",
"28285833 Integrative Proteomics and Phosphoproteomics P... \n",
"29898900 Principled multi-omic analysis reveals gene re... \n",
"32182340 Multi-omic analysis of gametogenesis reveals a... \n",
"31227589 Integrated multiomic analysis reveals comprehe... \n",
"30268436 A Pan-Cancer Analysis Reveals High-Frequency G... \n",
"28544881 Mammalian Systems Biotechnology Reveals Global... \n",
"26446169 A Cross-Species Analysis in Pancreatic Neuroen... \n",
"29588317 Spatiotemporal Multi-Omics Mapping Generates a... \n",
"27132591 Integrative Multi-omic Analysis of Human Plate... \n",
"32023468 Inverse Data-Driven Modeling and Multiomics An... \n",
"32522993 A multi-omics analysis reveals the unfolded pr... \n",
"30692544 Deconvolution of single-cell multi-omics layer... \n",
"31604924 Integrated molecular characterization of chond... \n",
"29500431 Integrative analysis of omics summary data rev... \n",
"29615613 Multi-omics analysis reveals neoantigen-indepe... \n",
"29713003 Multi-omics profiling of younger Asian breast ... \n",
"32312967 Nitrogen limitation reveals large reserves in ... \n",
"28408661 Multi-Omics of Tomato Glandular Trichomes Reve... \n",
"\n",
" journal \\\n",
"uid \n",
"32649874 Cell \n",
"29328921 Cell \n",
"28285833 Immunity \n",
"29898900 Genome research \n",
"32182340 Nucleic acids research \n",
"31227589 Gut \n",
"30268436 Cell systems \n",
"28544881 Cell systems \n",
"26446169 Cancer discovery \n",
"29588317 Circulation \n",
"27132591 American journal of human genetics \n",
"32023468 Cell reports \n",
"32522993 Nature communications \n",
"30692544 Nature communications \n",
"31604924 Nature communications \n",
"29500431 Nature communications \n",
"29615613 Nature communications \n",
"29713003 Nature communications \n",
"32312967 Nature communications \n",
"28408661 The Plant cell \n",
"\n",
" doi journal_sjr_rank \n",
"uid \n",
"32649874 10.1016/j.cell.2020.06.013 8.0 \n",
"29328921 10.1016/j.cell.2017.12.036 8.0 \n",
"28285833 10.1016/j.immuni.2017.02.010 50.0 \n",
"29898900 10.1101/gr.227066.117 84.0 \n",
"32182340 10.1093/nar/gkaa163 90.0 \n",
"31227589 10.1136/gutjnl-2019-318912 121.0 \n",
"30268436 10.1016/j.cels.2018.08.010 131.0 \n",
"28544881 10.1016/j.cels.2017.04.009 131.0 \n",
"26446169 10.1158/2159-8290.CD-15-0068 137.0 \n",
"29588317 10.1161/CIRCULATIONAHA.117.032291 142.0 \n",
"27132591 10.1016/j.ajhg.2016.03.007 143.0 \n",
"32023468 10.1016/j.celrep.2020.01.011 203.0 \n",
"32522993 10.1038/s41467-020-16747-y 238.0 \n",
"30692544 10.1038/s41467-018-08205-7 238.0 \n",
"31604924 10.1038/s41467-019-12525-7 238.0 \n",
"29500431 10.1038/s41467-018-03371-0 238.0 \n",
"29615613 10.1038/s41467-018-03730-x 238.0 \n",
"29713003 10.1038/s41467-018-04129-4 238.0 \n",
"32312967 10.1038/s41467-020-15749-0 238.0 \n",
"28408661 10.1105/tpc.17.00060 254.0 "
]
},
"execution_count": 10,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"literature[literature.title.str.lower().str.contains('|'.join(['reveals']))].pipe(display_sorted).head(20)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### Affiliations"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Most authors with given affiliation on papers:"
]
},
{
"cell_type": "code",
"execution_count": 11,
"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>Affiliation</th>\n",
" </tr>\n",
" <tr>\n",
" <th>index</th>\n",
" <th></th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>Department of Genetics, Stanford University School of Medicine, Stanford, CA, USA.</th>\n",
" <td>58</td>\n",
" </tr>\n",
" <tr>\n",
" <th>Tohoku Medical Megabank Organization, Tohoku University, Sendai, Japan.</th>\n",
" <td>48</td>\n",
" </tr>\n",
" <tr>\n",
" <th>College of Bioinformatics Science and Technology, Harbin Medical University, Harbin, China.</th>\n",
" <td>46</td>\n",
" </tr>\n",
" <tr>\n",
" <th>The European Molecular Biology Laboratory, The European Bioinformatics Institute, The Wellcome Trust Genome Campus, Hinxton, Cambridgeshire CB10 1SD, UK.</th>\n",
" <td>39</td>\n",
" </tr>\n",
" <tr>\n",
" <th>College of Bioinformatics Science and Technology, Harbin Medical University, Harbin, Heilongjiang 150086, China.</th>\n",
" <td>32</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" Affiliation\n",
"index \n",
"Department of Genetics, Stanford University Sch... 58\n",
"Tohoku Medical Megabank Organization, Tohoku Un... 48\n",
"College of Bioinformatics Science and Technolog... 46\n",
"The European Molecular Biology Laboratory, The ... 39\n",
"College of Bioinformatics Science and Technolog... 32"
]
},
"execution_count": 11,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"affiliations.Affiliation.sorted_value_counts().head(5).to_frame()"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Most papers with given affiliation:"
]
},
{
"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>Affiliation</th>\n",
" </tr>\n",
" <tr>\n",
" <th>index</th>\n",
" <th></th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>M&M Medical BioInformatics, Hongo 113-0033, Japan.</th>\n",
" <td>16</td>\n",
" </tr>\n",
" <tr>\n",
" <th>College of Bioinformatics Science and Technology, Harbin Medical University, Harbin, China.</th>\n",
" <td>7</td>\n",
" </tr>\n",
" <tr>\n",
" <th>Department of Genetics, Stanford University School of Medicine, Stanford, CA, USA.</th>\n",
" <td>7</td>\n",
" </tr>\n",
" <tr>\n",
" <th>University of Chinese Academy of Sciences, Beijing 100049, China.</th>\n",
" <td>7</td>\n",
" </tr>\n",
" <tr>\n",
" <th>Institute for Systems Biology, Seattle, WA, USA.</th>\n",
" <td>5</td>\n",
" </tr>\n",
" <tr>\n",
" <th>Luxembourg Centre for Systems Biomedicine, University of Luxembourg, Esch-sur-Alzette, Luxembourg.</th>\n",
" <td>5</td>\n",
" </tr>\n",
" <tr>\n",
" <th>Mosaiques Diagnostics GmbH, Hannover, Germany.</th>\n",
" <td>5</td>\n",
" </tr>\n",
" <tr>\n",
" <th>Biological Sciences Division, Pacific Northwest National Laboratory, Richland, WA, USA.</th>\n",
" <td>4</td>\n",
" </tr>\n",
" <tr>\n",
" <th>Blavatnik School of Computer Science, Tel Aviv University, Tel Aviv, Israel.</th>\n",
" <td>4</td>\n",
" </tr>\n",
" <tr>\n",
" <th>College of Bioinformatics Science and Technology, Harbin Medical University, Harbin, Heilongjiang 150086, China.</th>\n",
" <td>4</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" Affiliation\n",
"index \n",
"M&M Medical BioInformatics, Hongo 113-0033, Japan. 16\n",
"College of Bioinformatics Science and Technolog... 7\n",
"Department of Genetics, Stanford University Sch... 7\n",
"University of Chinese Academy of Sciences, Beij... 7\n",
"Institute for Systems Biology, Seattle, WA, USA. 5\n",
"Luxembourg Centre for Systems Biomedicine, Univ... 5\n",
"Mosaiques Diagnostics GmbH, Hannover, Germany. 5\n",
"Biological Sciences Division, Pacific Northwest... 4\n",
"Blavatnik School of Computer Science, Tel Aviv ... 4\n",
"College of Bioinformatics Science and Technolog... 4"
]
},
"execution_count": 12,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"affiliations[['Affiliation', 'PMID']].drop_duplicates().Affiliation.sorted_value_counts().head(10).to_frame()"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"We were previously getting false hits because we were matching by affiliations:\n",
" - \"Multi-Omics Based Creative Drug Research Team, Kyungpook National University, Daegu 41566, Republic of Korea\"\n",
" - \"Panomics, Inc\"\n",
" \n",
"so it is important to check if no affiliations overrepresented. Would need a cleanup to be more reliable (not a priority)."
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### Authors"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Note: not neccessarily unique persons, adoption of ORCID still low:"
]
},
{
"cell_type": "code",
"execution_count": 13,
"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>JointName</th>\n",
" </tr>\n",
" <tr>\n",
" <th>index</th>\n",
" <th></th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>Masaru Katoh</th>\n",
" <td>26</td>\n",
" </tr>\n",
" <tr>\n",
" <th>Richard D Smith</th>\n",
" <td>19</td>\n",
" </tr>\n",
" <tr>\n",
" <th>Paul Wilmes</th>\n",
" <td>18</td>\n",
" </tr>\n",
" <tr>\n",
" <th>Jens Nielsen</th>\n",
" <td>17</td>\n",
" </tr>\n",
" <tr>\n",
" <th>Young-Mo Kim</th>\n",
" <td>17</td>\n",
" </tr>\n",
" <tr>\n",
" <th>Thomas O Metz</th>\n",
" <td>16</td>\n",
" </tr>\n",
" <tr>\n",
" <th>Xia Li</th>\n",
" <td>16</td>\n",
" </tr>\n",
" <tr>\n",
" <th>Bernhard O Palsson</th>\n",
" <td>14</td>\n",
" </tr>\n",
" <tr>\n",
" <th>Yuriko Katoh</th>\n",
" <td>14</td>\n",
" </tr>\n",
" <tr>\n",
" <th>Bing Zhang</th>\n",
" <td>13</td>\n",
" </tr>\n",
" <tr>\n",
" <th>James G Wilson</th>\n",
" <td>13</td>\n",
" </tr>\n",
" <tr>\n",
" <th>Li Wang</th>\n",
" <td>13</td>\n",
" </tr>\n",
" <tr>\n",
" <th>Carrie D Nicora</th>\n",
" <td>12</td>\n",
" </tr>\n",
" <tr>\n",
" <th>Claudio Angione</th>\n",
" <td>12</td>\n",
" </tr>\n",
" <tr>\n",
" <th>Jerome I Rotter</th>\n",
" <td>12</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" JointName\n",
"index \n",
"Masaru Katoh 26\n",
"Richard D Smith 19\n",
"Paul Wilmes 18\n",
"Jens Nielsen 17\n",
"Young-Mo Kim 17\n",
"Thomas O Metz 16\n",
"Xia Li 16\n",
"Bernhard O Palsson 14\n",
"Yuriko Katoh 14\n",
"Bing Zhang 13\n",
"James G Wilson 13\n",
"Li Wang 13\n",
"Carrie D Nicora 12\n",
"Claudio Angione 12\n",
"Jerome I Rotter 12"
]
},
"execution_count": 13,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"authors['JointName'].sorted_value_counts().head(15).to_frame()"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### Publication kind and type"
]
},
{
"cell_type": "code",
"execution_count": 14,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"index\n",
"article 3453\n",
"article in book 3\n",
"Name: kind, dtype: int64"
]
},
"execution_count": 14,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"literature.kind.sorted_value_counts()"
]
},
{
"cell_type": "code",
"execution_count": 15,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"744"
]
},
"execution_count": 15,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"sum(literature['Is Review'] == True)"
]
},
{
"cell_type": "code",
"execution_count": 16,
"metadata": {},
"outputs": [
{
"data": {
"text/html": [
"<div>\n",
"<style scoped>\n",
" .dataframe tbody tr th:only-of-type {\n",
" vertical-align: middle;\n",
" }\n",
"\n",
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" }\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>count</th>\n",
" </tr>\n",
" <tr>\n",
" <th>index</th>\n",
" <th></th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>Journal Article</th>\n",
" <td>3370</td>\n",
" </tr>\n",
" <tr>\n",
" <th>Research Support, Non-U.S. Gov't</th>\n",
" <td>1371</td>\n",
" </tr>\n",
" <tr>\n",
" <th>Review</th>\n",
" <td>744</td>\n",
" </tr>\n",
" <tr>\n",
" <th>Research Support, N.I.H., Extramural</th>\n",
" <td>460</td>\n",
" </tr>\n",
" <tr>\n",
" <th>Research Support, U.S. Gov't, Non-P.H.S.</th>\n",
" <td>161</td>\n",
" </tr>\n",
" <tr>\n",
" <th>Comparative Study</th>\n",
" <td>61</td>\n",
" </tr>\n",
" <tr>\n",
" <th>Editorial</th>\n",
" <td>44</td>\n",
" </tr>\n",
" <tr>\n",
" <th>Comment</th>\n",
" <td>37</td>\n",
" </tr>\n",
" <tr>\n",
" <th>Clinical Trial</th>\n",
" <td>26</td>\n",
" </tr>\n",
" <tr>\n",
" <th>Published Erratum</th>\n",
" <td>23</td>\n",
" </tr>\n",
" <tr>\n",
" <th>Multicenter Study</th>\n",
" <td>21</td>\n",
" </tr>\n",
" <tr>\n",
" <th>Research Support, N.I.H., Intramural</th>\n",
" <td>16</td>\n",
" </tr>\n",
" <tr>\n",
" <th>Evaluation Study</th>\n",
" <td>13</td>\n",
" </tr>\n",
" <tr>\n",
" <th>Letter</th>\n",
" <td>13</td>\n",
" </tr>\n",
" <tr>\n",
" <th>Case Reports</th>\n",
" <td>9</td>\n",
" </tr>\n",
" <tr>\n",
" <th>Dataset</th>\n",
" <td>9</td>\n",
" </tr>\n",
" <tr>\n",
" <th>Introductory Journal Article</th>\n",
" <td>7</td>\n",
" </tr>\n",
" <tr>\n",
" <th>Observational Study</th>\n",
" <td>7</td>\n",
" </tr>\n",
" <tr>\n",
" <th>Twin Study</th>\n",
" <td>7</td>\n",
" </tr>\n",
" <tr>\n",
" <th>Validation Study</th>\n",
" <td>7</td>\n",
" </tr>\n",
" <tr>\n",
" <th>English Abstract</th>\n",
" <td>6</td>\n",
" </tr>\n",
" <tr>\n",
" <th>Randomized Controlled Trial</th>\n",
" <td>6</td>\n",
" </tr>\n",
" <tr>\n",
" <th>Systematic Review</th>\n",
" <td>6</td>\n",
" </tr>\n",
" <tr>\n",
" <th>Video-Audio Media</th>\n",
" <td>6</td>\n",
" </tr>\n",
" <tr>\n",
" <th>Meta-Analysis</th>\n",
" <td>5</td>\n",
" </tr>\n",
" <tr>\n",
" <th>Research Support, U.S. Gov't, P.H.S.</th>\n",
" <td>5</td>\n",
" </tr>\n",
" <tr>\n",
" <th>Congress</th>\n",
" <td>4</td>\n",
" </tr>\n",
" <tr>\n",
" <th>Interview</th>\n",
" <td>3</td>\n",
" </tr>\n",
" <tr>\n",
" <th>News</th>\n",
" <td>2</td>\n",
" </tr>\n",
" <tr>\n",
" <th>Clinical Trial, Phase II</th>\n",
" <td>1</td>\n",
" </tr>\n",
" <tr>\n",
" <th>Consensus Development Conference, NIH</th>\n",
" <td>1</td>\n",
" </tr>\n",
" <tr>\n",
" <th>Controlled Clinical Trial</th>\n",
" <td>1</td>\n",
" </tr>\n",
" <tr>\n",
" <th>Historical Article</th>\n",
" <td>1</td>\n",
" </tr>\n",
" <tr>\n",
" <th>Practice Guideline</th>\n",
" <td>1</td>\n",
" </tr>\n",
" <tr>\n",
" <th>Preprint</th>\n",
" <td>1</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" count\n",
"index \n",
"Journal Article 3370\n",
"Research Support, Non-U.S. Gov't 1371\n",
"Review 744\n",
"Research Support, N.I.H., Extramural 460\n",
"Research Support, U.S. Gov't, Non-P.H.S. 161\n",
"Comparative Study 61\n",
"Editorial 44\n",
"Comment 37\n",
"Clinical Trial 26\n",
"Published Erratum 23\n",
"Multicenter Study 21\n",
"Research Support, N.I.H., Intramural 16\n",
"Evaluation Study 13\n",
"Letter 13\n",
"Case Reports 9\n",
"Dataset 9\n",
"Introductory Journal Article 7\n",
"Observational Study 7\n",
"Twin Study 7\n",
"Validation Study 7\n",
"English Abstract 6\n",
"Randomized Controlled Trial 6\n",
"Systematic Review 6\n",
"Video-Audio Media 6\n",
"Meta-Analysis 5\n",
"Research Support, U.S. Gov't, P.H.S. 5\n",
"Congress 4\n",
"Interview 3\n",
"News 2\n",
"Clinical Trial, Phase II 1\n",
"Consensus Development Conference, NIH 1\n",
"Controlled Clinical Trial 1\n",
"Historical Article 1\n",
"Practice Guideline 1\n",
"Preprint 1"
]
},
"execution_count": 16,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"publication_types['0'].sorted_value_counts().to_frame('count')"
]
},
{
"cell_type": "code",
"execution_count": 17,
"metadata": {},
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{
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" <th>uid</th>\n",
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" <tr>\n",
" <th>21889780</th>\n",
" <td>[OMICS and biomarkers of glial tumors].</td>\n",
" <td>10.1016/j.neurol.2011.07.007</td>\n",
" </tr>\n",
" <tr>\n",
" <th>22490743</th>\n",
" <td>[Application of an integrated omics analysis f...</td>\n",
" <td>NaN</td>\n",
" </tr>\n",
" <tr>\n",
" <th>24292186</th>\n",
" <td>[Biomarker exploration and its clinical use].</td>\n",
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" </tr>\n",
" <tr>\n",
" <th>25744640</th>\n",
" <td>[Gut microbiota, host defense and immunity: an...</td>\n",
" <td>10.2177/jsci.37.403</td>\n",
" </tr>\n",
" <tr>\n",
" <th>26351165</th>\n",
" <td>[Identification of disease targets for precisi...</td>\n",
" <td>10.16288/j.yczz.15-061</td>\n",
" </tr>\n",
" <tr>\n",
" <th>32694108</th>\n",
" <td>[Comprehensive re-annotation of protein-coding...</td>\n",
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" title \\\n",
"uid \n",
"21889780 [OMICS and biomarkers of glial tumors]. \n",
"22490743 [Application of an integrated omics analysis f... \n",
"24292186 [Biomarker exploration and its clinical use]. \n",
"25744640 [Gut microbiota, host defense and immunity: an... \n",
"26351165 [Identification of disease targets for precisi... \n",
"32694108 [Comprehensive re-annotation of protein-coding... \n",
"\n",
" doi \n",
"uid \n",
"21889780 10.1016/j.neurol.2011.07.007 \n",
"22490743 NaN \n",
"24292186 NaN \n",
"25744640 10.2177/jsci.37.403 \n",
"26351165 10.16288/j.yczz.15-061 \n",
"32694108 10.16288/j.yczz.20-022 "
]
},
"execution_count": 17,
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"output_type": "execute_result"
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],
"source": [
"literature[literature['Is English Abstract'] == True][['title', 'doi']]"
]
},
{
"cell_type": "code",
"execution_count": 18,
"metadata": {},
"outputs": [
{
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" <thead>\n",
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" <th></th>\n",
" <th>title</th>\n",
" <th>doi</th>\n",
" </tr>\n",
" <tr>\n",
" <th>uid</th>\n",
" <th></th>\n",
" <th></th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>22424393</th>\n",
" <td>Q & A: the Snyderome.</td>\n",
" <td>10.1186/gb-2012-13-3-147</td>\n",
" </tr>\n",
" <tr>\n",
" <th>31429661</th>\n",
" <td>Interview with Prof. K. Yalçın Arga: A Pioneer...</td>\n",
" <td>10.1089/omi.2019.0131</td>\n",
" </tr>\n",
" <tr>\n",
" <th>31755849</th>\n",
" <td>Interview with Prof. Collet Dandara: A Pioneer...</td>\n",
" <td>10.1089/omi.2019.0174</td>\n",
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"31755849 10.1089/omi.2019.0174 "
]
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}
],
"source": [
"literature[literature['Is Interview'] == True][['title', 'doi']]"
]
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{
"cell_type": "code",
"execution_count": 19,
"metadata": {},
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{
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" <thead>\n",
" <tr style=\"text-align: right;\">\n",
" <th></th>\n",
" <th>title</th>\n",
" <th>doi</th>\n",
" </tr>\n",
" <tr>\n",
" <th>uid</th>\n",
" <th></th>\n",
" <th></th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>12186644</th>\n",
" <td>Integromics: challenges in data integration.</td>\n",
" <td>NaN</td>\n",
" </tr>\n",
" <tr>\n",
" <th>27164162</th>\n",
" <td>Emergence of Biomolecular Pathways to Define N...</td>\n",
" <td>10.1165/rcmb.2016-0141PS</td>\n",
" </tr>\n",
" <tr>\n",
" <th>29339647</th>\n",
" <td>Proceedings of the 11th Congress of the Intern...</td>\n",
" <td>10.1159/000485799</td>\n",
" </tr>\n",
" <tr>\n",
" <th>31394729</th>\n",
" <td>Novel Strategies for Cancer Treatment: Highlig...</td>\n",
" <td>10.3390/cancers11081125</td>\n",
" </tr>\n",
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"</table>\n",
"</div>"
],
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" title \\\n",
"uid \n",
"12186644 Integromics: challenges in data integration. \n",
"27164162 Emergence of Biomolecular Pathways to Define N... \n",
"29339647 Proceedings of the 11th Congress of the Intern... \n",
"31394729 Novel Strategies for Cancer Treatment: Highlig... \n",
"\n",
" doi \n",
"uid \n",
"12186644 NaN \n",
"27164162 10.1165/rcmb.2016-0141PS \n",
"29339647 10.1159/000485799 \n",
"31394729 10.3390/cancers11081125 "
]
},
"execution_count": 19,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"literature[literature['Is Congress'] == True][['title', 'doi']]"
]
},
{
"cell_type": "code",
"execution_count": 20,
"metadata": {},
"outputs": [
{
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" <tr style=\"text-align: right;\">\n",
" <th></th>\n",
" <th>title</th>\n",
" <th>doi</th>\n",
" </tr>\n",
" <tr>\n",
" <th>uid</th>\n",
" <th></th>\n",
" <th></th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>29301609</th>\n",
" <td>Integration of metabolomics and transcriptomic...</td>\n",
" <td>NaN</td>\n",
" </tr>\n",
" <tr>\n",
" <th>32475383</th>\n",
" <td>From genome sequencing to the discovery of pot...</td>\n",
" <td>NaN</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
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" title doi\n",
"uid \n",
"29301609 Integration of metabolomics and transcriptomic... NaN\n",
"32475383 From genome sequencing to the discovery of pot... NaN"
]
},
"execution_count": 20,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"literature[literature['Is News'] == True][['title', 'doi']]"
]
},
{
"cell_type": "code",
"execution_count": 21,
"metadata": {},
"outputs": [
{
"data": {
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"<div>\n",
"<style scoped>\n",
" .dataframe tbody tr th:only-of-type {\n",
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" }\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>doi</th>\n",
" </tr>\n",
" <tr>\n",
" <th>uid</th>\n",
" <th></th>\n",
" <th></th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>25632108</th>\n",
" <td>CyanOmics: an integrated database of omics for...</td>\n",
" <td>10.1093/database/bau127</td>\n",
" </tr>\n",
" <tr>\n",
" <th>26130662</th>\n",
" <td>toxoMine: an integrated omics data warehouse f...</td>\n",
" <td>10.1093/database/bav066</td>\n",
" </tr>\n",
" <tr>\n",
" <th>26646939</th>\n",
" <td>Multi-omic profiles of human non-alcoholic fat...</td>\n",
" <td>10.1038/sdata.2015.68</td>\n",
" </tr>\n",
" <tr>\n",
" <th>27504011</th>\n",
" <td>MODEM: multi-omics data envelopment and mining...</td>\n",
" <td>10.1093/database/baw117</td>\n",
" </tr>\n",
" <tr>\n",
" <th>29087370</th>\n",
" <td>Monitoring microbial responses to ocean deoxyg...</td>\n",
" <td>10.1038/sdata.2017.158</td>\n",
" </tr>\n",
" <tr>\n",
" <th>30084846</th>\n",
" <td>A multi-omic atlas of the human frontal cortex...</td>\n",
" <td>10.1038/sdata.2018.142</td>\n",
" </tr>\n",
" <tr>\n",
" <th>30204156</th>\n",
" <td>The Mount Sinai cohort of large-scale genomic,...</td>\n",
" <td>10.1038/sdata.2018.185</td>\n",
" </tr>\n",
" <tr>\n",
" <th>30621600</th>\n",
" <td>The 1000IBD project: multi-omics data of 1000 ...</td>\n",
" <td>10.1186/s12876-018-0917-5</td>\n",
" </tr>\n",
" <tr>\n",
" <th>31201317</th>\n",
" <td>Multi omics analysis of fibrotic kidneys in tw...</td>\n",
" <td>10.1038/s41597-019-0095-5</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" title \\\n",
"uid \n",
"25632108 CyanOmics: an integrated database of omics for... \n",
"26130662 toxoMine: an integrated omics data warehouse f... \n",
"26646939 Multi-omic profiles of human non-alcoholic fat... \n",
"27504011 MODEM: multi-omics data envelopment and mining... \n",
"29087370 Monitoring microbial responses to ocean deoxyg... \n",
"30084846 A multi-omic atlas of the human frontal cortex... \n",
"30204156 The Mount Sinai cohort of large-scale genomic,... \n",
"30621600 The 1000IBD project: multi-omics data of 1000 ... \n",
"31201317 Multi omics analysis of fibrotic kidneys in tw... \n",
"\n",
" doi \n",
"uid \n",
"25632108 10.1093/database/bau127 \n",
"26130662 10.1093/database/bav066 \n",
"26646939 10.1038/sdata.2015.68 \n",
"27504011 10.1093/database/baw117 \n",
"29087370 10.1038/sdata.2017.158 \n",
"30084846 10.1038/sdata.2018.142 \n",
"30204156 10.1038/sdata.2018.185 \n",
"30621600 10.1186/s12876-018-0917-5 \n",
"31201317 10.1038/s41597-019-0095-5 "
]
},
"execution_count": 21,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"literature[literature['Is Dataset'] == True][['title', 'doi']]"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### Journals"
]
},
{
"cell_type": "code",
"execution_count": 22,
"metadata": {},
"outputs": [],
"source": [
"journal_freq = literature.journal.sorted_value_counts()"
]
},
{
"cell_type": "code",
"execution_count": 23,
"metadata": {},
"outputs": [
{
"data": {
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"<table border=\"1\" class=\"dataframe\">\n",
" <thead>\n",
" <tr style=\"text-align: right;\">\n",
" <th></th>\n",
" <th>journal</th>\n",
" </tr>\n",
" <tr>\n",
" <th>index</th>\n",
" <th></th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>Scientific reports</th>\n",
" <td>126</td>\n",
" </tr>\n",
" <tr>\n",
" <th>Omics : a journal of integrative biology</th>\n",
" <td>78</td>\n",
" </tr>\n",
" <tr>\n",
" <th>PloS one</th>\n",
" <td>69</td>\n",
" </tr>\n",
" <tr>\n",
" <th>Bioinformatics (Oxford, England)</th>\n",
" <td>68</td>\n",
" </tr>\n",
" <tr>\n",
" <th>Nature communications</th>\n",
" <td>58</td>\n",
" </tr>\n",
" <tr>\n",
" <th>Frontiers in genetics</th>\n",
" <td>55</td>\n",
" </tr>\n",
" <tr>\n",
" <th>Journal of proteomics</th>\n",
" <td>53</td>\n",
" </tr>\n",
" <tr>\n",
" <th>BMC bioinformatics</th>\n",
" <td>52</td>\n",
" </tr>\n",
" <tr>\n",
" <th>Nucleic acids research</th>\n",
" <td>45</td>\n",
" </tr>\n",
" <tr>\n",
" <th>Methods in molecular biology (Clifton, N.J.)</th>\n",
" <td>43</td>\n",
" </tr>\n",
" <tr>\n",
" <th>Journal of proteome research</th>\n",
" <td>42</td>\n",
" </tr>\n",
" <tr>\n",
" <th>BMC genomics</th>\n",
" <td>41</td>\n",
" </tr>\n",
" <tr>\n",
" <th>Oncotarget</th>\n",
" <td>39</td>\n",
" </tr>\n",
" <tr>\n",
" <th>International journal of molecular sciences</th>\n",
" <td>37</td>\n",
" </tr>\n",
" <tr>\n",
" <th>Briefings in bioinformatics</th>\n",
" <td>36</td>\n",
" </tr>\n",
" <tr>\n",
" <th>Frontiers in microbiology</th>\n",
" <td>34</td>\n",
" </tr>\n",
" <tr>\n",
" <th>Molecular & cellular proteomics : MCP</th>\n",
" <td>29</td>\n",
" </tr>\n",
" <tr>\n",
" <th>mSystems</th>\n",
" <td>28</td>\n",
" </tr>\n",
" <tr>\n",
" <th>Cell reports</th>\n",
" <td>26</td>\n",
" </tr>\n",
" <tr>\n",
" <th>Metabolites</th>\n",
" <td>26</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" journal\n",
"index \n",
"Scientific reports 126\n",
"Omics : a journal of integrative biology 78\n",
"PloS one 69\n",
"Bioinformatics (Oxford, England) 68\n",
"Nature communications 58\n",
"Frontiers in genetics 55\n",
"Journal of proteomics 53\n",
"BMC bioinformatics 52\n",
"Nucleic acids research 45\n",
"Methods in molecular biology (Clifton, N.J.) 43\n",
"Journal of proteome research 42\n",
"BMC genomics 41\n",
"Oncotarget 39\n",
"International journal of molecular sciences 37\n",
"Briefings in bioinformatics 36\n",
"Frontiers in microbiology 34\n",
"Molecular & cellular proteomics : MCP 29\n",
"mSystems 28\n",
"Cell reports 26\n",
"Metabolites 26"
]
},
"execution_count": 23,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"journal_freq.head(20).to_frame()"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Sanity check (is any of the top names not unique?) - the numbers should be same if counting by ISSN:"
]
},
{
"cell_type": "code",
"execution_count": 24,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"index\n",
"2045-2322 126\n",
"1557-8100 77\n",
"1932-6203 69\n",
"1367-4811 68\n",
"2041-1723 58\n",
"1664-8021 55\n",
"1876-7737 53\n",
"1471-2105 52\n",
"1362-4962 45\n",
"1940-6029 42\n",
"Name: journal_issn, dtype: int64"
]
},
"execution_count": 24,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"literature.journal_issn.sorted_value_counts().head(10)"
]
},
{
"cell_type": "code",
"execution_count": 25,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"index\n",
"Nucleic acids research 45\n",
"Methods in molecular biology (Clifton, N.J.) 43\n",
"Journal of proteome research 42\n",
"BMC genomics 41\n",
"Oncotarget 39\n",
" ..\n",
"Zhongguo yi xue ke xue yuan xue bao. Acta Academiae Medicinae Sinicae 1\n",
"Zhonghua nan ke xue = National journal of andrology 1\n",
"Zhonghua yu fang yi xue za zhi [Chinese journal of preventive medicine] 1\n",
"Zoology (Jena, Germany) 1\n",
"mSphere 1\n",
"Name: journal, Length: 967, dtype: int64"
]
},
"execution_count": 25,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"journal_freq[journal_freq < 50]"
]
},
{
"cell_type": "code",
"execution_count": 26,
"metadata": {},
"outputs": [],
"source": [
"literature = literature.replace({float('nan'): None}).infer_objects()\n",
"%R -i literature"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Publication types"
]
},
{
"cell_type": "code",
"execution_count": 27,
"metadata": {},
"outputs": [],
"source": [
"%%R\n",
"library(ComplexUpset)\n",
"source('helpers/plots.R')\n",
"source('helpers/colors.R')"
]
},
{
"cell_type": "code",
"execution_count": 28,
"metadata": {},
"outputs": [],
"source": [
"publication_types_list = ['Is ' + t for t in publication_types['0'].sorted_value_counts().where(lambda x: x > 10).dropna().index]"
]
},
{
"cell_type": "code",
"execution_count": 29,
"metadata": {},
"outputs": [
{
"name": "stderr",
"output_type": "stream",
"text": [
"R[write to console]: Scale for 'y' is already present. Adding another scale for 'y', which will\n",
"replace the existing scale.\n",
"\n"
]
},
{
"data": {
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\n"
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"%%R -i publication_types_list -w 2000 -r 100 -h 800\n",
"\n",
"upset(\n",
" literature,\n",
" publication_types_list,\n",
" base_annotations=list(\n",
" 'Intersection size'=intersection_size(\n",
" text=list(angle=90, vjust=0.5, hjust=0)\n",
" )\n",
" ),\n",
" width_ratio=0.1,\n",
" set_sizes=(\n",
" upset_set_size(\n",
" geom=geom_bar(width=0.5)\n",
" )\n",
" + scale_y_continuous(trans=reverse_log_trans())\n",
" + theme(axis.text.x=element_text(angle=90))\n",
" )\n",
")"
]
},
{
"cell_type": "code",
"execution_count": 30,
"metadata": {},
"outputs": [
{
"data": {
"image/png": 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\n"
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"%%R -w 700 -h 400 -r 100\n",
"(\n",
" ggplot(literature, aes(x=year, fill=has_doi))\n",
" + geom_bar()\n",
" + theme_bw()\n",
") + (\n",
" ggplot(literature, aes(x=year, fill=has_pmc))\n",
" + geom_bar()\n",
" + theme_bw()\n",
") & plot_layout(ncol=1)"
]
}
],
"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": "3.8.3"
}
},
"nbformat": 4,
"nbformat_minor": 4
}