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Bioinformatics Advance Access originally published online on July 15, 2004
Bioinformatics 2004 20(18):3423-3430; doi:10.1093/bioinformatics/bth419
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Bioinformatics vol. 20 issue 18 © Oxford University Press 2004; all rights reserved.

Gene selection using a two-level hierarchical Bayesian model

Kyounghwa Bae and Bani K. Mallick 1,*

1 Department of Statistics, Texas A&M University, College Station, TX 77843-3143, USA

Received on March 7, 2004; revised on July 9, 2004; accepted on July 10, 2004
Advance Access Publication July 15, 2004

Summary: The fundamental problem of gene selection via cDNA data is to identify which genes are differentially expressed across different kinds of tissue samples (e.g. normal and cancer). cDNA data contain large number of variables (genes) and usually the sample size is relatively small so the selection process can be unstable. Therefore, models which incorporate sparsity in terms of variables (genes) are desirable for this kind of problem. This paper proposes a two-level hierarchical Bayesian model for variable selection which assumes a prior that favors sparseness. We adopt a Markov chain Monte Carlo (MCMC) based computation technique to simulate the parameters from the posteriors. The method is applied to leukemia data from a previous study and a published dataset on breast cancer.

Supplementary information: http://stat.tamu.edu/people/faculty/bmallick.html

Contact: bmallick{at}stat.tamu.edu

* To whom correspondence should be addressed.


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