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dc.contributor.authorChowdhary, Rajesh
dc.contributor.authorTan, Sin Lam
dc.contributor.authorZhang, Jinfeng
dc.contributor.authorKarnik, Shreyas
dc.contributor.authorBajic, Vladimir B.
dc.contributor.authorLiu, Jun S.
dc.date.accessioned2014-08-27T09:45:58Z
dc.date.available2014-08-27T09:45:58Z
dc.date.issued2012-04-06
dc.identifier.citationChowdhary R, Tan SL, Zhang J, Karnik S, Bajic VB, et al. (2012) Context-Specific Protein Network Miner - An Online System for Exploring Context-Specific Protein Interaction Networks from the Literature. PLoS ONE 7: e34480. doi:10.1371/journal.pone.0034480.
dc.identifier.issn19326203
dc.identifier.pmid22493694
dc.identifier.doi10.1371/journal.pone.0034480
dc.identifier.urihttp://hdl.handle.net/10754/325303
dc.description.abstractBackground: Protein interaction networks (PINs) specific within a particular context contain crucial information regarding many cellular biological processes. For example, PINs may include information on the type and directionality of interaction (e.g. phosphorylation), location of interaction (i.e. tissues, cells), and related diseases. Currently, very few tools are capable of deriving context-specific PINs for conducting exploratory analysis. Results: We developed a literature-based online system, Context-specific Protein Network Miner (CPNM), which derives context-specific PINs in real-time from the PubMed database based on a set of user-input keywords and enhanced PubMed query system. CPNM reports enriched information on protein interactions (with type and directionality), their network topology with summary statistics (e.g. most densely connected proteins in the network; most densely connected protein-pairs; and proteins connected by most inbound/outbound links) that can be explored via a user-friendly interface. Some of the novel features of the CPNM system include PIN generation, ontology-based PubMed query enhancement, real-time, user-queried, up-to-date PubMed document processing, and prediction of PIN directionality. Conclusions: CPNM provides a tool for biologists to explore PINs. It is freely accessible at http://www.biotextminer.com/CPNM/. © 2012 Chowdhary et al.
dc.language.isoen
dc.publisherPublic Library of Science (PLoS)
dc.rightsChowdhary et al. This is an open-access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.
dc.rightsArchived with thanks to PLoS ONE
dc.subjectcomputer interface
dc.subjectcomputer network
dc.subjectcontrolled study
dc.subjectdown regulation
dc.subjectgene expression regulation
dc.subjectonline system
dc.subjectprediction
dc.subjectprotein database
dc.subjectprotein interaction
dc.subjectupregulation
dc.subjectvalidation process
dc.subjectalgorithm
dc.subjectcomputer program
dc.subjectdata mining
dc.subjectgenetic database
dc.subjectgenetics
dc.subjectInternet
dc.subjectMedline
dc.subjectmetabolism
dc.subjectmethodology
dc.subjectprotein analysis
dc.subjectprotein protein interaction
dc.subjectprotein
dc.subjectAlgorithms
dc.subjectData Mining
dc.subjectDatabases, Genetic
dc.subjectInternet
dc.subjectProtein Interaction Mapping
dc.subjectProtein Interaction Maps
dc.subjectProteins
dc.subjectPubMed
dc.subjectSoftware
dc.subjectUser-Computer Interface
dc.titleContext-specific protein network miner - an online system for exploring context-specific protein interaction networks from the literature
dc.typeArticle
dc.contributor.departmentComputational Bioscience Research Center (CBRC)
dc.contributor.departmentApplied Mathematics and Computational Science Program
dc.contributor.departmentComputer, Electrical and Mathematical Sciences and Engineering (CEMSE) Division
dc.identifier.journalPLoS ONE
dc.identifier.pmcidPMC3321019
dc.eprint.versionPublisher's Version/PDF
dc.contributor.institutionMarshfield Clinic-Marshfield Center, Marshfield Clinic Research Foundation -Biomedical Informatics Research Center, Marshfield, WI, United States
dc.contributor.institutionDepartment of Statistics, Florida State University, Tallahassee, FL, United States
dc.contributor.institutionDepartment of Statistics, Harvard University, Cambridge, MA, United States
dc.contributor.affiliationKing Abdullah University of Science and Technology (KAUST)
kaust.personBajic, Vladimir B.
refterms.dateFOA2018-06-13T14:51:48Z


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