Context-specific protein network miner - an online system for exploring context-specific protein interaction networks from the literature

Handle URI:
http://hdl.handle.net/10754/325303
Title:
Context-specific protein network miner - an online system for exploring context-specific protein interaction networks from the literature
Authors:
Chowdhary, Rajesh; Tan, Sin Lam; Zhang, Jinfeng; Karnik, Shreyas; Bajic, Vladimir B. ( 0000-0001-5435-4750 ) ; Liu, Jun S.
Abstract:
Background: 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.
KAUST Department:
Computational Bioscience Research Center (CBRC)
Citation:
Chowdhary 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.
Publisher:
Public Library of Science (PLoS)
Journal:
PLoS ONE
Issue Date:
6-Apr-2012
DOI:
10.1371/journal.pone.0034480
PubMed ID:
22493694
PubMed Central ID:
PMC3321019
Type:
Article
ISSN:
19326203
Appears in Collections:
Articles; Computational Bioscience Research Center (CBRC)

Full metadata record

DC FieldValue Language
dc.contributor.authorChowdhary, Rajeshen
dc.contributor.authorTan, Sin Lamen
dc.contributor.authorZhang, Jinfengen
dc.contributor.authorKarnik, Shreyasen
dc.contributor.authorBajic, Vladimir B.en
dc.contributor.authorLiu, Jun S.en
dc.date.accessioned2014-08-27T09:45:58Z-
dc.date.available2014-08-27T09:45:58Z-
dc.date.issued2012-04-06en
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.en
dc.identifier.issn19326203en
dc.identifier.pmid22493694en
dc.identifier.doi10.1371/journal.pone.0034480en
dc.identifier.urihttp://hdl.handle.net/10754/325303en
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.en
dc.language.isoenen
dc.publisherPublic Library of Science (PLoS)en
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.en
dc.rightsArchived with thanks to PLoS ONEen
dc.subjectcomputer interfaceen
dc.subjectcomputer networken
dc.subjectcontrolled studyen
dc.subjectdown regulationen
dc.subjectgene expression regulationen
dc.subjectonline systemen
dc.subjectpredictionen
dc.subjectprotein databaseen
dc.subjectprotein interactionen
dc.subjectupregulationen
dc.subjectvalidation processen
dc.subjectalgorithmen
dc.subjectcomputer programen
dc.subjectdata miningen
dc.subjectgenetic databaseen
dc.subjectgeneticsen
dc.subjectInterneten
dc.subjectMedlineen
dc.subjectmetabolismen
dc.subjectmethodologyen
dc.subjectprotein analysisen
dc.subjectprotein protein interactionen
dc.subjectproteinen
dc.subjectAlgorithmsen
dc.subjectData Miningen
dc.subjectDatabases, Geneticen
dc.subjectInterneten
dc.subjectProtein Interaction Mappingen
dc.subjectProtein Interaction Mapsen
dc.subjectProteinsen
dc.subjectPubMeden
dc.subjectSoftwareen
dc.subjectUser-Computer Interfaceen
dc.titleContext-specific protein network miner - an online system for exploring context-specific protein interaction networks from the literatureen
dc.typeArticleen
dc.contributor.departmentComputational Bioscience Research Center (CBRC)en
dc.identifier.journalPLoS ONEen
dc.identifier.pmcidPMC3321019en
dc.eprint.versionPublisher's Version/PDFen
dc.contributor.institutionMarshfield Clinic-Marshfield Center, Marshfield Clinic Research Foundation -Biomedical Informatics Research Center, Marshfield, WI, United Statesen
dc.contributor.institutionDepartment of Statistics, Florida State University, Tallahassee, FL, United Statesen
dc.contributor.institutionDepartment of Statistics, Harvard University, Cambridge, MA, United Statesen
dc.contributor.affiliationKing Abdullah University of Science and Technology (KAUST)en
kaust.authorBajic, Vladimir B.en

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