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Conference Paper: Identifying projected clusters from gene expression profiles

TitleIdentifying projected clusters from gene expression profiles
Authors
Issue Date2004
PublisherIEEE, Computer Society.
Citation
Proceedings - Fourth Ieee Symposium On Bioinformatics And Bioengineering, Bibe 2004, 2004, p. 259-266 How to Cite?
AbstractIn microarray gene expression data, clusters may hide in subspaces. Traditional clustering algorithms that make use of similarity measurements in the full input space may fail to detect the clusters. In recent years a number of algorithms have been proposed to identify this kind of projected clusters, but many of them rely on some critical parameters whose proper values are hard for users to determine. In this paper a new algorithm that dynamically adjusts its internal thresholds is proposed. It has a low dependency on user parameters while allowing users to input some domain knowledge should they be available. Experimental results show that the algorithm is capable of identifying some interesting projected clusters from real microarray data.
Persistent Identifierhttp://hdl.handle.net/10722/46597
References

 

DC FieldValueLanguage
dc.contributor.authorYip, KYen_HK
dc.contributor.authorCheung, DWen_HK
dc.contributor.authorNg, MKen_HK
dc.contributor.authorCheung, KHen_HK
dc.date.accessioned2007-10-30T06:53:50Z-
dc.date.available2007-10-30T06:53:50Z-
dc.date.issued2004en_HK
dc.identifier.citationProceedings - Fourth Ieee Symposium On Bioinformatics And Bioengineering, Bibe 2004, 2004, p. 259-266en_HK
dc.identifier.urihttp://hdl.handle.net/10722/46597-
dc.description.abstractIn microarray gene expression data, clusters may hide in subspaces. Traditional clustering algorithms that make use of similarity measurements in the full input space may fail to detect the clusters. In recent years a number of algorithms have been proposed to identify this kind of projected clusters, but many of them rely on some critical parameters whose proper values are hard for users to determine. In this paper a new algorithm that dynamically adjusts its internal thresholds is proposed. It has a low dependency on user parameters while allowing users to input some domain knowledge should they be available. Experimental results show that the algorithm is capable of identifying some interesting projected clusters from real microarray data.en_HK
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dc.format.extent2254 bytes-
dc.format.extent6619 bytes-
dc.format.mimetypeapplication/pdf-
dc.format.mimetypetext/plain-
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dc.languageengen_HK
dc.publisherIEEE, Computer Society.en_HK
dc.relation.ispartofProceedings - Fourth IEEE Symposium on Bioinformatics and Bioengineering, BIBE 2004en_HK
dc.rightsCreative Commons: Attribution 3.0 Hong Kong License-
dc.rights©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.en_HK
dc.titleIdentifying projected clusters from gene expression profilesen_HK
dc.typeConference_Paperen_HK
dc.identifier.emailCheung, DW:dcheung@cs.hku.hken_HK
dc.identifier.authorityCheung, DW=rp00101en_HK
dc.description.naturepublished_or_final_versionen_HK
dc.identifier.doi10.1109/BIBE.2004.1317352en_HK
dc.identifier.scopuseid_2-s2.0-4544383734en_HK
dc.identifier.hkuros103275-
dc.relation.referenceshttp://www.scopus.com/mlt/select.url?eid=2-s2.0-4544383734&selection=ref&src=s&origin=recordpageen_HK
dc.identifier.spage259en_HK
dc.identifier.epage266en_HK
dc.identifier.scopusauthoridYip, KY=7101909946en_HK
dc.identifier.scopusauthoridCheung, DW=34567902600en_HK
dc.identifier.scopusauthoridNg, MK=7202076432en_HK
dc.identifier.scopusauthoridCheung, KH=7402406608en_HK

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