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Article: Annotating gene functions with integrative spectral clustering on microarray expressions and sequences.

TitleAnnotating gene functions with integrative spectral clustering on microarray expressions and sequences.
Authors
Issue Date2010
PublisherUniversal Academy Press, Inc. The Journal's web site is located at http://www.uap.co.jp/uap/Publication/SERIES/GIS/
Citation
Genome Informatics. International Conference On Genome Informatics, 2010, v. 22, p. 95-120 How to Cite?
AbstractAnnotating genes is a fundamental issue in the post-genomic era. A typical procedure for this issue is first clustering genes by their features and then assigning functions of unknown genes by using known genes in the same cluster. A lot of genomic information are available for this issue, but two major types of data which can be measured for any gene are microarray expressions and sequences, both of which however have their own flaws. Thus a natural and promising approach for gene annotation is to integrate these two data sources, especially in terms of their costs to be optimized in clustering. We develop an efficient gene annotation method with three steps containing spectral clustering over the integrated cost, based on the idea of network modularity. We rigorously examined the performance of our proposed method from three different viewpoints. All experimental results indicate the performance advantage of our method over possible clustering/classification-based approaches of gene function annotation, using expressions and/or sequences.
Persistent Identifierhttp://hdl.handle.net/10722/156257
ISSN
2014 SCImago Journal Rankings: 0.184

 

DC FieldValueLanguage
dc.contributor.authorLi, Len_US
dc.contributor.authorShiga, Men_US
dc.contributor.authorChing, WKen_US
dc.contributor.authorMamitsuka, Hen_US
dc.date.accessioned2012-08-08T08:41:03Z-
dc.date.available2012-08-08T08:41:03Z-
dc.date.issued2010en_US
dc.identifier.citationGenome Informatics. International Conference On Genome Informatics, 2010, v. 22, p. 95-120en_US
dc.identifier.issn0919-9454en_US
dc.identifier.urihttp://hdl.handle.net/10722/156257-
dc.description.abstractAnnotating genes is a fundamental issue in the post-genomic era. A typical procedure for this issue is first clustering genes by their features and then assigning functions of unknown genes by using known genes in the same cluster. A lot of genomic information are available for this issue, but two major types of data which can be measured for any gene are microarray expressions and sequences, both of which however have their own flaws. Thus a natural and promising approach for gene annotation is to integrate these two data sources, especially in terms of their costs to be optimized in clustering. We develop an efficient gene annotation method with three steps containing spectral clustering over the integrated cost, based on the idea of network modularity. We rigorously examined the performance of our proposed method from three different viewpoints. All experimental results indicate the performance advantage of our method over possible clustering/classification-based approaches of gene function annotation, using expressions and/or sequences.en_US
dc.languageengen_US
dc.publisherUniversal Academy Press, Inc. The Journal's web site is located at http://www.uap.co.jp/uap/Publication/SERIES/GIS/en_US
dc.relation.ispartofGenome informatics. International Conference on Genome Informaticsen_US
dc.subject.meshAlgorithmsen_US
dc.subject.meshGene Expression - Physiologyen_US
dc.subject.meshGene Expression Profiling - Methodsen_US
dc.subject.meshGenes - Physiologyen_US
dc.subject.meshHumansen_US
dc.subject.meshPattern Recognition, Automateden_US
dc.subject.meshSignal Transduction - Physiologyen_US
dc.subject.meshSystems Integrationen_US
dc.titleAnnotating gene functions with integrative spectral clustering on microarray expressions and sequences.en_US
dc.typeArticleen_US
dc.identifier.emailChing, WK:wching@hku.hken_US
dc.identifier.authorityChing, WK=rp00679en_US
dc.description.naturelink_to_OA_fulltexten_US
dc.identifier.pmid20238422-
dc.identifier.scopuseid_2-s2.0-77954626855en_US
dc.identifier.hkuros168204-
dc.identifier.volume22en_US
dc.identifier.spage95en_US
dc.identifier.epage120en_US
dc.publisher.placeJapanen_US
dc.identifier.scopusauthoridLi, L=37090149800en_US
dc.identifier.scopusauthoridShiga, M=18538640800en_US
dc.identifier.scopusauthoridChing, WK=13310265500en_US
dc.identifier.scopusauthoridMamitsuka, H=6602748450en_US

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