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dc.contributor.authorChowdhary, Rajesh
dc.contributor.authorZhang, Jinfeng
dc.contributor.authorTan, Sin Lam
dc.contributor.authorOsborne, Daniel E.
dc.contributor.authorBajic, Vladimir B.
dc.contributor.authorLiu, Jun S.
dc.date.accessioned2015-08-03T10:40:47Z
dc.date.available2015-08-03T10:40:47Z
dc.date.issued2013
dc.identifier.citationChowdhary, R., Zhang, J., Tan, S. L., Osborne, D. E., Bajic, V. B., & Liu, J. S. (2013). PIMiner: a web tool for extraction of protein interactions from biomedical literature. International Journal of Data Mining and Bioinformatics, 7(4), 450. doi:10.1504/ijdmb.2013.054232
dc.identifier.issn1748-5673
dc.identifier.issn1748-5681
dc.identifier.pmid23798227
dc.identifier.doi10.1504/IJDMB.2013.054232
dc.identifier.urihttp://hdl.handle.net/10754/562510
dc.description.abstractInformation on Protein Interactions (PIs) is valuable for biomedical research, but often lies buried in the scientific literature and cannot be readily retrieved. While much progress has been made over the years in extracting PIs from the literature using computational methods, there is a lack of free, public, user-friendly tools for the discovery of PIs. We developed an online tool for the extraction of PI relationships from PubMed-abstracts, which we name PIMiner. Protein pairs and the words that describe their interactions are reported by PIMiner so that new interactions can be easily detected within text. The interaction likelihood levels are reported too. The option to extract only specific types of interactions is also provided. The PIMiner server can be accessed through a web browser or remotely through a client's command line. PIMiner can process 50,000 PubMed abstracts in approximately 7 min and thus appears suitable for large-scale processing of biological/biomedical literature. Copyright © 2013 Inderscience Enterprises Ltd.
dc.description.sponsorshipThis study was supported in part by grant 1UL1RR025011 from the Clinical and Translational Science Award (CTSA) program of the National Center for Research Resources, National Institutes of Health. We thank Ryan Frahm for providing server support. We also sincerely thank Rachel V. Stankowski for proof-reading the manuscript and for her comments.
dc.publisherInderscience Publishers
dc.relation.urlhttp://www.inderscience.com/link.php?id=54232
dc.relation.urlhttp://europepmc.org/articles/pmc4303605?pdf=render
dc.rightsArchived with thanks to Inderscience Publishers
dc.rightsThis file is an open access version redistributed from: http://europepmc.org/articles/pmc4303605?pdf=render
dc.subjectBioinformatics
dc.subjectBiological text-mining
dc.subjectComplex biological networks
dc.subjectData mining
dc.subjectGene-protein networks
dc.subjectInteractome mining
dc.subjectLiterature mining
dc.subjectPI
dc.subjectProtein interactions
dc.subjectProtein-protein interactions
dc.subjectSystems biology
dc.titlePIMiner: A web tool for extraction of protein interactions from biomedical literature
dc.typeArticle
dc.contributor.departmentComputational Bioscience Research Center (CBRC)
dc.contributor.departmentComputer, Electrical and Mathematical Sciences and Engineering (CEMSE) Division
dc.contributor.departmentApplied Mathematics and Computational Science Program
dc.identifier.journalInternational Journal of Data Mining and Bioinformatics
dc.identifier.pmcidPMC4303605
dc.identifier.wosutWOS:000319870800007
dc.eprint.versionPost-print
dc.contributor.institutionBiomedical Informatifcs Research Center, MCRF, Marshfield Clinic, 1000 North Oak Avenue, Marshfield, WI 54449, United States
dc.contributor.institutionDepartment of Statistics, Florida State University, Tallahassee, FL 32306, United States
dc.contributor.institutionDepartment of Statistics, Harvard University, Cambridge, MA 02138, United States
dc.identifier.volume7
dc.identifier.issue4
dc.identifier.pages450
kaust.personBajic, Vladimir B.
dc.identifier.eid2-s2.0-84878775644
refterms.dateFOA2021-04-28T06:18:33Z


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