Biclustering expression data based on expanding localized substructures

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dc.authorid0000-0002-8149-7113
dc.contributor.authorErten, Cesimen_US
dc.contributor.authorSözdinler, Melihen_US
dc.date.accessioned2019-08-31T12:10:23Z
dc.date.accessioned2019-08-05T16:05:01Z
dc.date.available2019-08-31T12:10:23Z
dc.date.available2019-08-05T16:05:01Z
dc.date.issued2009
dc.departmentIşık Üniversitesi, Mühendislik Fakültesi, Bilgisayar Mühendisliği Bölümüen_US
dc.departmentIşık University, Faculty of Engineering, Department of Computer Engineeringen_US
dc.description.abstractBiclustering gene expression data is the problem of extracting submatrices of genes and conditions exhibiting significant correlation across both the rows and the columns of a data matrix of expression values. We provide a method, LEB (Localize-and-Extract Biclusters) which reduces the search space into local neighborhoods within the matrix by first localizing correlated structures. The localization procedure takes its roots from effective use of graph-theoretical methods applied to problems exhibiting a similar structure to that of biclustering. Once interesting structures are localized the search space reduces to small neighborhoods and the biclusters are extracted from these localities. We evaluate the effectiveness of our method with extensive experiments both using artificial and real datasets.en_US
dc.description.sponsorshipUniv Connecticut; Booth Engn Ctr Adv Technolen_US
dc.description.versionPublisher's Versionen_US
dc.identifier.citationErten, C. & Sözdinler, M. (2009). Biclustering expression data based on expanding localized substructures. Paper presented at the Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), 5462 224-235. doi:10.1007/978-3-642-00727-9_22en_US
dc.identifier.doi10.1007/978-3-642-00727-9_22
dc.identifier.endpage235
dc.identifier.isbn3642007260
dc.identifier.isbn9783642007262
dc.identifier.issn0302-9743
dc.identifier.issn1611-3349
dc.identifier.scopus2-s2.0-68249117368
dc.identifier.scopusqualityQ3
dc.identifier.startpage224
dc.identifier.urihttps://hdl.handle.net/11729/1985
dc.identifier.urihttps://dx.doi.org/10.1007/978-3-642-00727-9_22
dc.identifier.volume5462 LNBI
dc.identifier.wosWOS:000265785800022
dc.identifier.wosqualityQ4
dc.indekslendigikaynakWeb of Scienceen_US
dc.indekslendigikaynakScopusen_US
dc.indekslendigikaynakConference Proceedings Citation Index – Science (CPCI-S)en_US
dc.institutionauthorSözdinler, Melihen_US
dc.language.isoenen_US
dc.peerreviewedYesen_US
dc.publicationstatusPublisheden_US
dc.publisherSpringer-Verlag Berlin Heidelbergen_US
dc.relation.ispartofLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)en_US
dc.relation.ispartofseriesLecture Notes in Bioinformaticsen_US
dc.relation.publicationcategoryKonferans Öğesi - Uluslararası - Kurum Öğretim Elemanıen_US
dc.rightsinfo:eu-repo/semantics/closedAccessen_US
dc.subjectAdaptive noise en_US
dc.subjectAlgorithmsen_US
dc.subjectBiclusteringen_US
dc.subjectBiclustering algorithmen_US
dc.subjectBiclustersen_US
dc.subjectBioinformaticsen_US
dc.subjectBiologyen_US
dc.subjectBipartite graphen_US
dc.subjectData matricesen_US
dc.subjectEnrichment ratioen_US
dc.subjectExpression dataen_US
dc.subjectGeneen_US
dc.subjectGene expressionen_US
dc.subjectGene expression dataen_US
dc.subjectLocalization procedureen_US
dc.subjectLocalize substructureen_US
dc.subjectMatrixen_US
dc.subjectMatrix algebraen_US
dc.subjectMicroarray dataen_US
dc.subjectReal data setsen_US
dc.subjectSearch spacesen_US
dc.subjectSub-matricesen_US
dc.subjectYeast cell cycleen_US
dc.titleBiclustering expression data based on expanding localized substructuresen_US
dc.typeConference Objecten_US
dspace.entity.typePublication

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