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Online Journal of Bioinformatics©
Volume 5:102-128, 2004.
MCM-Based Clustering for Time-Course Gene Expression Data.
Wu FX, Zhang WJ, Kusalik AJ.
Division of Biomedical Engineering,
Department of Computer Science,
Wu FX, Zhang WJ, Kusalik AJ MCM-based
clustering for time-course gene expression data. Onl
J Bioinform., 5: 102-128, 2004. Time-course gene expression data
contains important information at the molecular level about underlying
biological processes. A huge body of such data has been and will continuously
be produced by microarray experiments. The challenge now is how to mine such
data and to obtain the useful information from them. Cluster analysis has
played an important role in analyzing time-course gene expression data and has
been proven useful. However, most clustering techniques have not considered the
inherent time dependence (dynamics) of time-course gene expression data.
Accounting for the inherent dynamics of such data in cluster analysis should
lead to high quality clustering. This paper proposes a model-based clustering
method, called MCM-based clustering method, for time-course gene expression
data. The proposed method uses Markov chain models (MCMs) to account for the
inherent dynamics. It is assumed that genes in the same cluster were generated
by the same MCM. For the given number of clusters, the proposed method finds
cluster models using EM algorithm and an assignment of genes to these models
that maximizes their posterior probabilities. Using Bayesian Information
Criterion (BIC) for model selection, the proposed method may automatically give
the number of clusters in a dataset. Further, this study employs the adjusted
KEYWORDS: MCM-Clustering, Time-Course, Gene expression