Editing Text mining (section)

=== Biomedical applications ===
{{Main|Biomedical text mining}}
[[File:Text mining protocol.png|alt=A flowchart of a text mining protocol.|thumb|An example of a text mining protocol used in a study of protein-protein complexes, or [[protein docking]].<ref>{{Cite journal|last1=Badal|first1=Varsha D.|last2=Kundrotas|first2=Petras J.|last3=Vakser|first3=Ilya A.|date=2015-12-09|title=Text Mining for Protein Docking|journal=PLOS Computational Biology|volume=11|issue=12|pages=e1004630|doi=10.1371/journal.pcbi.1004630|issn=1553-7358|pmc=4674139|pmid=26650466|bibcode=2015PLSCB..11E4630B |doi-access=free }}</ref>]]
A range of text mining applications in the biomedical literature has been described,<ref>{{cite journal |doi=10.1371/journal.pcbi.0040020 |title=Getting Started in Text Mining |year=2008 |last1=Cohen |first1=K. Bretonnel |last2=Hunter |first2=Lawrence |journal=PLOS Computational Biology |volume=4 |pages=e20 |pmid=18225946 |issue=1 |pmc=2217579|bibcode=2008PLSCB...4...20C |doi-access=free }}</ref> including computational approaches to assist with studies in [[protein docking]],<ref>{{cite journal |doi=10.1371/journal.pcbi.1004630 |title=Text mining for protein docking|journal=PLOS Computational Biology|volume=11|issue=12|pages=e1004630|pmid=26650466 |pmc=4674139|year=2015|last1=Badal|first1=V. D|last2=Kundrotas|first2=P. J|last3=Vakser|first3=I. A|bibcode=2015PLSCB..11E4630B |doi-access=free }}</ref> [[protein interactions]],<ref>{{Cite journal|last1=Papanikolaou|first1=Nikolas|last2=Pavlopoulos|first2=Georgios A.|last3=Theodosiou|first3=Theodosios|last4=Iliopoulos|first4=Ioannis|date=2015|title=Protein–protein interaction predictions using text mining methods|journal=Methods|volume=74|pages=47–53|doi=10.1016/j.ymeth.2014.10.026|pmid=25448298|issn=1046-2023}}</ref><ref>{{Cite journal|last1=Szklarczyk|first1=Damian|last2=Morris|first2=John H|last3=Cook|first3=Helen|last4=Kuhn|first4=Michael|last5=Wyder|first5=Stefan|last6=Simonovic|first6=Milan|last7=Santos|first7=Alberto|last8=Doncheva|first8=Nadezhda T|last9=Roth|first9=Alexander|date=2016-10-18|title=The STRING database in 2017: quality-controlled protein–protein association networks, made broadly accessible|journal=Nucleic Acids Research|volume=45|issue=D1|pages=D362–D368|doi=10.1093/nar/gkw937|issn=0305-1048|pmc=5210637|pmid=27924014}}</ref> and protein-disease associations.<ref>{{Cite journal|last1=Liem|first1=David A.|last2=Murali|first2=Sanjana|last3=Sigdel|first3=Dibakar|last4=Shi|first4=Yu|last5=Wang|first5=Xuan|last6=Shen|first6=Jiaming|last7=Choi|first7=Howard|last8=Caufield|first8=John H.|last9=Wang|first9=Wei|last10=Ping|first10=Peipei|last11=Han|first11=Jiawei|date=2018-10-01|title=Phrase mining of textual data to analyze extracellular matrix protein patterns across cardiovascular disease|journal=American Journal of Physiology. Heart and Circulatory Physiology|volume=315|issue=4|pages=H910–H924|doi=10.1152/ajpheart.00175.2018|issn=1522-1539|pmid=29775406|pmc=6230912}}</ref> In addition, with large patient textual datasets in the clinical field, datasets of demographic information in population studies and adverse event reports, text mining can facilitate clinical studies and precision medicine. Text mining algorithms can facilitate the stratification and indexing of specific clinical events in large patient textual datasets of symptoms, side effects, and comorbidities from electronic health records, event reports, and reports from specific diagnostic tests.<ref>{{cite journal |last1=Van Le |first1=D |last2=Montgomery |first2=J |last3=Kirkby |first3=KC |last4=Scanlan |first4=J |title=Risk Prediction using Natural Language Processing of Electronic Mental Health Records in an Inpatient Forensic Psychiatry Setting. |journal=Journal of Biomedical Informatics |volume=86 |pages=49–58 |date=10 August 2018 |doi=10.1016/j.jbi.2018.08.007 |pmid=30118855|doi-access=free }}</ref> One online text mining application in the biomedical literature is [[PubGene]], a publicly accessible [[search engine]] that combines biomedical text mining with network visualization.<ref>{{cite journal |doi=10.1038/ng0501-21 |title=A literature network of human genes for high-throughput analysis of gene expression |year=2001 |last1=Jenssen |first1=Tor-Kristian |last2=Lægreid |first2=Astrid |last3=Komorowski |first3=Jan |last4=Hovig |first4=Eivind |journal=Nature Genetics |volume=28 |pages=21–8 |pmid=11326270 |issue=1|s2cid=8889284 }}</ref><ref>{{cite journal |doi=10.1038/ng0501-9 |title=Linking microarray data to the literature |year=2001 |last1=Masys |first1=Daniel R. |journal=Nature Genetics |volume=28 |pages=9–10 |pmid=11326264 |issue=1|s2cid=52848745 }}</ref> [[GoPubMed]] is a knowledge-based search engine for biomedical texts. Text mining techniques also enable us to extract unknown knowledge from unstructured documents in the clinical domain<ref>{{Cite journal|last=Renganathan|first=Vinaitheerthan|date=2017|title=Text Mining in Biomedical Domain with Emphasis on Document Clustering|journal=Healthcare Informatics Research|volume=23|issue=3|pages=141–146|doi=10.4258/hir.2017.23.3.141|pmid=28875048|pmc=5572517|issn=2093-3681}}</ref>