PIMiner: A web tool for extraction of protein interactions from biomedical literature

Handle URI:
http://hdl.handle.net/10754/562510
Title:
PIMiner: A web tool for extraction of protein interactions from biomedical literature
Authors:
Chowdhary, Rajesh; Zhang, Jinfeng; Tan, Sinlam; Osborne, Daniel E.; Bajic, Vladimir B. ( 0000-0001-5435-4750 ) ; Liu, Jun
Abstract:
Information 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.
KAUST Department:
Computational Bioscience Research Center (CBRC); Computer, Electrical and Mathematical Sciences and Engineering (CEMSE) Division; Applied Mathematics and Computational Science Program
Publisher:
Inderscience Publishers
Journal:
International Journal of Data Mining and Bioinformatics
Issue Date:
2013
DOI:
10.1504/IJDMB.2013.054232
PubMed ID:
23798227
Type:
Article
ISSN:
17485673
Sponsors:
This 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.
Appears in Collections:
Articles; Applied Mathematics and Computational Science Program; Computational Bioscience Research Center (CBRC); Computer, Electrical and Mathematical Sciences and Engineering (CEMSE) Division

Full metadata record

DC FieldValue Language
dc.contributor.authorChowdhary, Rajeshen
dc.contributor.authorZhang, Jinfengen
dc.contributor.authorTan, Sinlamen
dc.contributor.authorOsborne, Daniel E.en
dc.contributor.authorBajic, Vladimir B.en
dc.contributor.authorLiu, Junen
dc.date.accessioned2015-08-03T10:40:47Zen
dc.date.available2015-08-03T10:40:47Zen
dc.date.issued2013en
dc.identifier.issn17485673en
dc.identifier.pmid23798227en
dc.identifier.doi10.1504/IJDMB.2013.054232en
dc.identifier.urihttp://hdl.handle.net/10754/562510en
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.en
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.en
dc.publisherInderscience Publishersen
dc.subjectBioinformaticsen
dc.subjectBiological text-miningen
dc.subjectComplex biological networksen
dc.subjectData miningen
dc.subjectGene-protein networksen
dc.subjectInteractome miningen
dc.subjectLiterature miningen
dc.subjectPIen
dc.subjectProtein interactionsen
dc.subjectProtein-protein interactionsen
dc.subjectSystems biologyen
dc.titlePIMiner: A web tool for extraction of protein interactions from biomedical literatureen
dc.typeArticleen
dc.contributor.departmentComputational Bioscience Research Center (CBRC)en
dc.contributor.departmentComputer, Electrical and Mathematical Sciences and Engineering (CEMSE) Divisionen
dc.contributor.departmentApplied Mathematics and Computational Science Programen
dc.identifier.journalInternational Journal of Data Mining and Bioinformaticsen
dc.contributor.institutionBiomedical Informatifcs Research Center, MCRF, Marshfield Clinic, 1000 North Oak Avenue, Marshfield, WI 54449, United Statesen
dc.contributor.institutionDepartment of Statistics, Florida State University, Tallahassee, FL 32306, United Statesen
dc.contributor.institutionDepartment of Statistics, Harvard University, Cambridge, MA 02138, United Statesen
kaust.authorBajic, Vladimir B.en

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