dc.contributor.author | Erten, Cesim | en_US |
dc.contributor.author | Sözdinler, Melih | en_US |
dc.date.accessioned | 2019-08-31T12:10:23Z | |
dc.date.accessioned | 2019-08-05T16:05:01Z | |
dc.date.available | 2019-08-31T12:10:23Z | |
dc.date.available | 2019-08-05T16:05:01Z | |
dc.date.issued | 2009 | |
dc.identifier.citation | Erten, 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_22 | en_US |
dc.identifier.isbn | 3642007260 | |
dc.identifier.isbn | 9783642007262 | |
dc.identifier.issn | 0302-9743 | |
dc.identifier.issn | 1611-3349 | |
dc.identifier.uri | https://hdl.handle.net/11729/1985 | |
dc.identifier.uri | https://dx.doi.org/10.1007/978-3-642-00727-9_22 | |
dc.description.abstract | Biclustering 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.sponsorship | Univ Connecticut; Booth Engn Ctr Adv Technol | en_US |
dc.language.iso | eng | en_US |
dc.publisher | Springer-Verlag Berlin Heidelberg | en_US |
dc.relation.ispartofseries | Lecture Notes in Bioinformatics | en_US |
dc.relation.isversionof | 10.1007/978-3-642-00727-9_22 | |
dc.rights | info:eu-repo/semantics/closedAccess | en_US |
dc.subject | Adaptive noise | en_US |
dc.subject | Algorithms | en_US |
dc.subject | Biclustering | en_US |
dc.subject | Biclustering algorithm | en_US |
dc.subject | Biclusters | en_US |
dc.subject | Bioinformatics | en_US |
dc.subject | Biology | en_US |
dc.subject | Bipartite graph | en_US |
dc.subject | Data matrices | en_US |
dc.subject | Enrichment ratio | en_US |
dc.subject | Expression data | en_US |
dc.subject | Gene | en_US |
dc.subject | Gene expression | en_US |
dc.subject | Gene expression data | en_US |
dc.subject | Localization procedure | en_US |
dc.subject | Localize substructure | en_US |
dc.subject | Matrix | en_US |
dc.subject | Matrix algebra | en_US |
dc.subject | Microarray data | en_US |
dc.subject | Real data sets | en_US |
dc.subject | Search spaces | en_US |
dc.subject | Sub-matrices | en_US |
dc.subject | Yeast cell cycle | en_US |
dc.title | Biclustering expression data based on expanding localized substructures | en_US |
dc.type | conferenceObject | en_US |
dc.description.version | Publisher's Version | en_US |
dc.relation.journal | Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) | en_US |
dc.contributor.department | Işık Üniversitesi, Mühendislik Fakültesi, Bilgisayar Mühendisliği Bölümü | en_US |
dc.contributor.department | Işık University, Faculty of Engineering, Department of Computer Engineering | en_US |
dc.identifier.volume | 5462 LNBI | |
dc.identifier.startpage | 224 | |
dc.identifier.endpage | 235 | |
dc.peerreviewed | Yes | en_US |
dc.publicationstatus | Published | en_US |
dc.relation.publicationcategory | Konferans Öğesi - Uluslararası - Kurum Öğretim Elemanı | en_US |
dc.contributor.institutionauthor | Sözdinler, Melih | en_US |
dc.relation.index | WOS | en_US |
dc.relation.index | Scopus | en_US |
dc.relation.index | Conference Proceedings Citation Index – Science (CPCI-S) | en_US |
dc.description.wosid | WOS:000265785800022 | |
dc.description.wosid | Q4 | |