title: Bayesian model-based tight clustering for time course data.
abstract: 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.
authors.affiliation: Department of Statistics, Dongguk University, Seoul 100-715, Korea, yongsungjoo@dongguk.edu.
authors.forename: Yongsung
authors.initials: Y
authors.lastname: Joo
authors.forename: G
authors.initials: G
authors.lastname: Casella
authors.forename: J
authors.initials: J
authors.lastname: Hobert