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Article: Identifying projected clusters from gene expression profiles

TitleIdentifying projected clusters from gene expression profiles
Authors
Issue Date2004
PublisherAcademic Press. The Journal's web site is located at http://www.elsevier.com/locate/yjbin
Citation
Journal Of Biomedical Informatics, 2004, v. 37 n. 5, p. 345-357 How to Cite?
AbstractIn microarray gene expression data, clusters may hide in certain subspaces. For example, a set of co-regulated genes may have similar expression patterns in only a subset of the samples in which certain regulating factors are present. Their expression patterns could be dissimilar when measuring in the full input space. Traditional clustering algorithms that make use of such similarity measurements may fail to identify 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. © 2004 Elsevier Inc. All rights reserved.
Persistent Identifierhttp://hdl.handle.net/10722/152405
ISSN
2015 Impact Factor: 2.447
2015 SCImago Journal Rankings: 1.283
ISI Accession Number ID
References

 

DC FieldValueLanguage
dc.contributor.authorYip, KYen_US
dc.contributor.authorCheung, DWen_US
dc.contributor.authorNg, MKen_US
dc.contributor.authorCheung, KHen_US
dc.date.accessioned2012-06-26T06:38:07Z-
dc.date.available2012-06-26T06:38:07Z-
dc.date.issued2004en_US
dc.identifier.citationJournal Of Biomedical Informatics, 2004, v. 37 n. 5, p. 345-357en_US
dc.identifier.issn1532-0464en_US
dc.identifier.urihttp://hdl.handle.net/10722/152405-
dc.description.abstractIn microarray gene expression data, clusters may hide in certain subspaces. For example, a set of co-regulated genes may have similar expression patterns in only a subset of the samples in which certain regulating factors are present. Their expression patterns could be dissimilar when measuring in the full input space. Traditional clustering algorithms that make use of such similarity measurements may fail to identify 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. © 2004 Elsevier Inc. All rights reserved.en_US
dc.languageengen_US
dc.publisherAcademic Press. The Journal's web site is located at http://www.elsevier.com/locate/yjbinen_US
dc.relation.ispartofJournal of Biomedical Informaticsen_US
dc.subject.meshAlgorithmsen_US
dc.subject.meshAnimalsen_US
dc.subject.meshArtificial Intelligenceen_US
dc.subject.meshCluster Analysisen_US
dc.subject.meshGene Expression Profiling - Methodsen_US
dc.subject.meshGene Expression Regulation - Physiologyen_US
dc.subject.meshHumansen_US
dc.subject.meshModels, Biologicalen_US
dc.subject.meshOligonucleotide Array Sequence Analysis - Methodsen_US
dc.subject.meshPattern Recognition, Automated - Methodsen_US
dc.subject.meshSignal Transduction - Physiologyen_US
dc.titleIdentifying projected clusters from gene expression profilesen_US
dc.typeArticleen_US
dc.identifier.emailCheung, DW:dcheung@cs.hku.hken_US
dc.identifier.authorityCheung, DW=rp00101en_US
dc.description.naturelink_to_subscribed_fulltexten_US
dc.identifier.doi10.1016/j.jbi.2004.05.002en_US
dc.identifier.pmid15488748-
dc.identifier.scopuseid_2-s2.0-5644304345en_US
dc.relation.referenceshttp://www.scopus.com/mlt/select.url?eid=2-s2.0-5644304345&selection=ref&src=s&origin=recordpageen_US
dc.identifier.volume37en_US
dc.identifier.issue5en_US
dc.identifier.spage345en_US
dc.identifier.epage357en_US
dc.identifier.isiWOS:000224901200004-
dc.publisher.placeUnited Statesen_US
dc.identifier.scopusauthoridYip, KY=7101909946en_US
dc.identifier.scopusauthoridCheung, DW=34567902600en_US
dc.identifier.scopusauthoridNg, MK=7202076432en_US
dc.identifier.scopusauthoridCheung, KH=7402406608en_US

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