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dc.contributor.authorZhao, Hongyaen_US
dc.contributor.authorLiew, Alan Wee-Chungen_US
dc.contributor.authorXie, Xudongen_US
dc.contributor.authorYan, Hongen_US
dc.date.accessioned2017-04-24T12:52:47Z
dc.date.available2017-04-24T12:52:47Z
dc.date.issued2008en_US
dc.date.modified2011-11-18T06:03:27Z
dc.identifier.issn00225193en_US
dc.identifier.doi10.1016/j.jtbi.2007.11.030en_AU
dc.identifier.urihttp://hdl.handle.net/10072/22590
dc.description.abstractBiclustering is an important tool in microarray analysis when only a subset of genes co-regulates in a subset of conditions. Different from standard clustering analyses, biclustering performs simultaneous classification in both gene and condition directions in a microarray data matrix. However, the biclustering problem is inherently intractable and computationally complex. In this paper, we present a new biclustering algorithm based on the geometrical viewpoint of coherent gene expression profiles. In this method, we perform pattern identification based on the Hough transform in a column-pair space. The algorithm is especially suitable for the biclustering analysis of large-scale microarray data. Our studies show that the approach can discover significant biclusters with respect to the increased noise level and regulatory complexity. Furthermore, we also test the ability of our method to locate biologically verifiable biclusters within an annotated set of genes.en_US
dc.description.peerreviewedYesen_US
dc.description.publicationstatusYesen_AU
dc.format.extent601061 bytes
dc.format.mimetypeapplication/pdf
dc.languageEnglishen_US
dc.language.isoen_AU
dc.publisherAcademic Pressen_US
dc.publisher.placeUnited Kingdomen_US
dc.relation.ispartofstudentpublicationNen_AU
dc.relation.ispartofpagefrom264en_US
dc.relation.ispartofpageto274en_US
dc.relation.ispartofissue2en_US
dc.relation.ispartofjournalJournal of Theoretical Biologyen_US
dc.relation.ispartofvolume251en_US
dc.rights.retentionYen_AU
dc.subject.fieldofresearchcode270201en_US
dc.subject.fieldofresearchcode280207en_US
dc.titleA new geometric biclustering algorithm based on the Hough transform for analysis of large-scale microarray dataen_US
dc.typeJournal articleen_US
dc.type.descriptionC1 - Peer Reviewed (HERDC)en_US
dc.type.codeC - Journal Articlesen_US
gro.rights.copyrightCopyright 2008 Elsevier Ltd. This is the author-manuscript version of this paper. Reproduced in accordance with the copyright policy of the publisher. Please refer to the journal's website for access to the definitive, published version.en_AU
gro.date.issued2008
gro.hasfulltextFull Text


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