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dc.contributor.authorWu, SH
dc.contributor.authorLiew, AWC
dc.contributor.authorYan, H
dc.contributor.authorYang, MS
dc.date.accessioned2017-05-03T15:20:22Z
dc.date.available2017-05-03T15:20:22Z
dc.date.issued2004
dc.date.modified2009-10-16T05:20:34Z
dc.identifier.issn1089-7771
dc.identifier.doi10.1109/TITB.2004.824724
dc.identifier.urihttp://hdl.handle.net/10072/21805
dc.description.abstractCluster analysis of gene expression data from a cDNA microarray is useful for identifying biologically relevant groups of genes. However, finding the natural clusters in the data and estimating the correct number of clusters are still two largely unsolved problems. In this paper, we propose a new clustering framework that is able to address both these problems. By using the one-prototype-take-one-cluster (OPTOC) competitive learning paradigm, the proposed algorithm can find natural clusters in the input data, and the clustering solution is not sensitive to initialization. In order to estimate the number of distinct clusters in the data, we propose a cluster splitting and merging strategy. We have applied the new algorithm to simulated gene expression data for which the correct distribution of genes over clusters is known a priori. The results show that the proposed algorithm can find natural clusters and give the correct number of clusters. The algorithm has also been tested on real gene expression changes during yeast cell cycle, for which the fundamental patterns of gene expression and assignment of genes to clusters are well understood from numerous previous studies. Comparative studies with several clustering algorithms illustrate the effectiveness of our method.
dc.description.peerreviewedYes
dc.description.publicationstatusYes
dc.format.extent922363 bytes
dc.format.mimetypeapplication/pdf
dc.languageEnglish
dc.language.isoeng
dc.publisherInstitute of Electrical and Electronics Engineers
dc.publisher.placeUnited States
dc.publisher.urihttp://ieeexplore.ieee.org/xpl/RecentIssue.jsp?punumber=4233
dc.relation.ispartofpagefrom5
dc.relation.ispartofpageto15
dc.relation.ispartofissue1
dc.relation.ispartofjournalIEEE Transactions on Information Technology in Biomedicine
dc.relation.ispartofvolume8
dc.subject.fieldofresearchInformation and Computing Sciences
dc.subject.fieldofresearchEngineering
dc.subject.fieldofresearchMedical and Health Sciences
dc.subject.fieldofresearchcode08
dc.subject.fieldofresearchcode09
dc.subject.fieldofresearchcode11
dc.titleCluster Analysis of Gene Expression Data Based on Self-Splitting and Merging Competitive Learning
dc.typeJournal article
dc.type.descriptionC1 - Articles
dc.type.codeC - Journal Articles
gro.rights.copyright© 2004 IEEE. Personal use of this material is permitted. However, permission to reprint/republish this material for advertising or promotional purposes or for creating new collective works for resale or redistribution to servers or lists, or to reuse any copyrighted component of this work in other works must be obtained from the IEEE.
gro.date.issued2004
gro.hasfulltextFull Text
gro.griffith.authorLiew, Alan Wee-Chung


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