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dc.contributor.authorWu, Shuanhuen_US
dc.contributor.authorChung Liew, Alan Weeen_US
dc.contributor.authorYan, Hongen_US
dc.contributor.editorJun Wangen_US
dc.date.accessioned2017-04-24T14:53:22Z
dc.date.available2017-04-24T14:53:22Z
dc.date.issued2005en_US
dc.date.modified2010-08-30T07:01:59Z
dc.identifier.doi10.1007/11427469_113en_AU
dc.identifier.urihttp://hdl.handle.net/10072/24589
dc.description.abstractIn this paper, a new feature extracting method and clustering scheme in spectral space for gene expression data was proposed. We model each member of same cluster as the sum of cluster's representative term and experimental artifacts term. More compact clusters and hence better clustering results can be obtained through extracting essential features or reducing experimental artifacts. In term of the periodicity of gene expression profile data, features extracting is performed in DCT domain by soft-thresholding de-noising method. Clustering process is based on OPTOC competitive learning strategy. The results for clustering real gene expression profiles show that our method is better than directly clustering in the original space.en_US
dc.description.peerreviewedYesen_US
dc.description.publicationstatusYesen_AU
dc.languageEnglishen_US
dc.language.isoen_AU
dc.publisherSpringeren_US
dc.publisher.placeBerlinen_US
dc.relation.ispartofstudentpublicationNen_AU
dc.relation.ispartofconferencename2nd International Symposium on Neural Networksen_US
dc.relation.ispartofconferencetitleAdvances in Neural Networks - ISNN 2005en_US
dc.relation.ispartofdatefrom2005-05-30en_US
dc.relation.ispartofdateto2005-06-01en_US
dc.relation.ispartoflocationChongqing, Chinaen_US
dc.rights.retentionYen_AU
dc.subject.fieldofresearchcode270201en_US
dc.titleOPTOC-based clustering analysis of gene expression profiles in spectral space en_US
dc.typeConference outputen_US
dc.type.descriptionE1 - Conference Publications (HERDC)en_US
dc.type.codeE - Conference Publicationsen_US
gro.date.issued2005
gro.hasfulltextNo Full Text


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