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Bioinformatics Advance Access published online on July 15, 2004
Bioinformatics, doi:10.1093/bioinformatics/bth419
Bioinformatics © Oxford University Press 2004; all rights reserved
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Received March 3, 2004
Revised July 9, 2004
Accepted July 10, 2004

Article

Gene selection using a two-level hierarchical Bayesian model

Kyounghwa Bae 1 Bani K. Mallick 1*

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

* To whom correspondence should be addressed. E-mail: bmallick{at}stat.tamu.edu.


  Abstract

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 contains 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 Golub et al. (1999) and a published data set of Hendenfalk et al. (2001) on breast cancer.

Supplimentary website: http://stat.tamu.edu/people/faculty/bmallick.html.


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