Show simple item record

dc.contributor.authorZhao, Hongya
dc.contributor.authorLiew, Alan Wee-Chung
dc.contributor.authorWang, Doris Z
dc.contributor.authorYan, Hong
dc.date.accessioned2017-05-03T15:20:08Z
dc.date.available2017-05-03T15:20:08Z
dc.date.issued2012
dc.date.modified2013-06-26T03:01:19Z
dc.identifier.issn1574-8936
dc.identifier.doi10.2174/157489312799304413
dc.identifier.urihttp://hdl.handle.net/10072/47234
dc.description.abstractBiclustering analysis is a useful methodology to discover the local coherent patterns hidden in a data matrix. Unlike the traditional clustering procedure, which searches for groups of coherent patterns using the entire feature set, biclustering performs simultaneous pattern classification in both row and column directions in a data matrix. The technique has found useful applications in many fields but notably in bioinformatics. In this paper, we give an overview of the biclustering problem and review some existing biclustering algorithms in terms of their underlying methodology, search strategy, detected bicluster patterns, and validation strategies. Moreover, we show that geometry of biclustering patterns can be used to solve biclustering problems effectively. Well-known methods in signal and image analysis, such as the Hough transform and relaxation labeling, can be employed to detect the geometrical biclustering patterns. We present performance evaluation results for several of the well known biclustering algorithms, on both artificial and real gene expression datasets. Finally, several interesting applications of biclustering are discussed.
dc.description.peerreviewedYes
dc.description.publicationstatusYes
dc.format.extent883314 bytes
dc.format.mimetypeapplication/pdf
dc.languageEnglish
dc.language.isoeng
dc.publisherBentham Science
dc.publisher.placeNetherlands
dc.relation.ispartofstudentpublicationN
dc.relation.ispartofpagefrom43
dc.relation.ispartofpageto55
dc.relation.ispartofissue1
dc.relation.ispartofjournalCurrent Bioinformatics
dc.relation.ispartofvolume7
dc.rights.retentionY
dc.subject.fieldofresearchMathematical sciences
dc.subject.fieldofresearchBiological sciences
dc.subject.fieldofresearchInformation and computing sciences
dc.subject.fieldofresearchcode49
dc.subject.fieldofresearchcode31
dc.subject.fieldofresearchcode46
dc.titleBiclustering Analysis for Pattern Discovery: Current Techniques, Comparative Studies and Applications
dc.typeJournal article
dc.type.descriptionC1 - Articles
dc.type.codeC - Journal Articles
gro.rights.copyright© 2012 Bentham Science Publishers. This is the author-manuscript version of this paper. Reproduced in accordance with the copyright policy of the publisher. Please refer to the journal website for access to the definitive, published version.
gro.date.issued2012
gro.hasfulltextFull Text
gro.griffith.authorLiew, Alan Wee-Chung


Files in this item

This item appears in the following Collection(s)

  • Journal articles
    Contains articles published by Griffith authors in scholarly journals.

Show simple item record