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dc.contributor.authorMa, Ping
dc.contributor.authorZhong, Wenxuan
dc.contributor.authorFeng, Yang
dc.contributor.authorLiu, Jun
dc.date.accessioned2010-10-05T15:43:13Z
dc.date.issued2008
dc.identifier.citationMa, Ping, Wenxuan Zhong, Yang Feng, and Jun S. Liu. 2008. Bayesian Functional Data Clustering for Temporal Microarray Data. International Journal of Plant Genomics 2008: 231897.en_US
dc.identifier.issn1687-5370en_US
dc.identifier.urihttp://nrs.harvard.edu/urn-3:HUL.InstRepos:4457706
dc.description.abstractWe propose a Bayesian procedure to cluster temporal gene expression microarray profiles, based on a mixed-effect smoothing-spline model, and design a Gibbs sampler to sample from the desired posterior distribution. Our method can determine the cluster number automatically based on the Bayesian information criterion, and handle missing data easily. When applied to a microarray dataset on the budding yeast, our clustering algorithm provides biologically meaningful gene clusters according to a functional enrichment analysis.en_US
dc.description.sponsorshipStatisticsen_US
dc.language.isoen_USen_US
dc.publisherHindawi Publishing Corporationen_US
dc.relation.isversionofdoi:10.1155/2008/231897en_US
dc.relation.hasversionhttp://www.ncbi.nlm.nih.gov/pmc/articles/PMC2358942/pdf/en_US
dash.licenseLAA
dc.titleBayesian Functional Data Clustering for Temporal Microarray Dataen_US
dc.typeJournal Articleen_US
dc.description.versionVersion of Recorden_US
dc.relation.journalInternational Journal of Plant Genomicsen_US
dash.depositing.authorLiu, Jun
dc.date.available2010-10-05T15:43:13Z
dc.identifier.doi10.1155/2008/231897*
dash.contributor.affiliatedLiu, Jun


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