Please use this identifier to cite or link to this item: http://hdl.handle.net/10397/8094
DC FieldValueLanguage
dc.contributorDepartment of Electronic and Information Engineering-
dc.creatorKung, SY-
dc.creatorMak, MW-
dc.creatorTagkopoulos, I-
dc.date.accessioned2015-06-23T09:14:58Z-
dc.date.available2015-06-23T09:14:58Z-
dc.identifier.issn0219-7200-
dc.identifier.urihttp://hdl.handle.net/10397/8094-
dc.language.isoenen_US
dc.subjectBiclusteringen_US
dc.subjectComputational bioinformaticsen_US
dc.subjectFinite mixture modelsen_US
dc.subjectGene expression patternsen_US
dc.subjectMachine learningen_US
dc.subjectMicroarrayen_US
dc.titleSymmetric and asymmetric multi-modality biclustering analysis for microarray data matrixen_US
dc.typeConference Paperen_US
dc.identifier.spage275-
dc.identifier.epage298-
dc.identifier.volume4-
dc.identifier.issue2-
dc.identifier.doi10.1142/S0219720006002065-
dcterms.abstractMachine learning techniques offer a viable approach to cluster discovery from microarray data, which involves identifying and classifying biologically relevant groups in genes and conditions. It has been recognized that genes (whether or not they belong to the same gene group) may be co-expressed via a variety of pathways. Therefore, they can be adequately described by a diversity of coherence models. In fact, it is known that a gene may participate in multiple pathways that may or may not be co-active under all conditions. It is therefore biologically meaningful to simultaneously divide genes into functional groups and conditions into co-active categories - leading to the so-called biclustering analysis. For this, we have proposed a comprehensive set of coherence models to cope with various plausible regulation processes. Furthermore, a multivariate biclustering analysis based on fusion of different coherence models appears to be promising because the expression level of genes from the same group may follow more than one coherence models. The simulation studies further confirm that the proposed framework enjoys the advantage of high prediction performance.-
dcterms.bibliographicCitationJournal of bioinformatics and computational biology, 2006, v. 4, no. 2, p. 275-298-
dcterms.isPartOfJournal of Bioinformatics and Computational Biology-
dcterms.issued2006-
dc.identifier.scopus2-s2.0-33646483117-
dc.identifier.pmid16819784-
dc.identifier.rosgroupidr28675-
dc.description.ros2005-2006 > Academic research: refereed > Publication in refereed journal-
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