Bayesian model-based tight clustering for time course data

被引：0
作者：
Yongsung Joo
George Casella
James Hobert
机构：
[1] Dongguk University,Department of Statistics
[2] University of Florida,Department of Statistics
来源：
Computational Statistics | 2010年 / 25卷
关键词：
Bayesian cluster analysis; Tight clustering; Time course gene expression; Microarray;
D O I：
暂无
中图分类号：
学科分类号：
摘要：
Cluster analysis has been widely used to explore thousands of gene expressions from microarray analysis and identify a small number of similar genes (objects) for further detailed biological investigation. However, most clustering algorithms tend to identify loose clusters with too many genes. In this paper, we propose a Bayesian tight clustering method for time course gene expression data, which selects a small number of closely-related genes and constructs tight clusters only with these closely-related genes.
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页码：17 / 38
页数：21
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