Bayesian Functional Data Clustering for Temporal Microarray Data

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Bayesian Functional Data Clustering for Temporal Microarray Data

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Title: Bayesian Functional Data Clustering for Temporal Microarray Data
Author: Ma, Ping; Zhong, Wenxuan; Feng, Yang; Liu, Jun

Note: Order does not necessarily reflect citation order of authors.

Citation: Ma, 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.
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Abstract: We 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.
Published Version: doi:10.1155/2008/231897
Other Sources: http://www.ncbi.nlm.nih.gov/pmc/articles/PMC2358942/pdf/
Terms of Use: This article is made available under the terms and conditions applicable to Other Posted Material, as set forth at http://nrs.harvard.edu/urn-3:HUL.InstRepos:dash.current.terms-of-use#LAA
Citable link to this page: http://nrs.harvard.edu/urn-3:HUL.InstRepos:4457706

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  • FAS Scholarly Articles [7106]
    Peer reviewed scholarly articles from the Faculty of Arts and Sciences of Harvard University
 
 

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