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Article 110 of 244

Gene selection: a Bayesian variable selection approach
Lee KE, Sha NJ, Dougherty ER, Vannucci M, Mallick BK
BIOINFORMATICS

19 (1): 90-97 JAN 2003

Document type: Article

Language: English

Cited References: 27

Times Cited: 2

Abstract:
Selection of significant genes via expression patterns is an important problem in microarray experiments. Owing to small sample size and the large number of variables (genes), the selection process can be unstable. This paper proposes a hierarchical Bayesian model for gene (variable) selection. We employ latent variables to specialize the model to a regression setting and uses a Bayesian mixture prior to perform the variable selection. We control the size of the model by assigning a prior distribution over the dimension (number of significant genes) of the model. The posterior distributions of the parameters are not in explicit form and we need to use a combination of truncated sampling and Markov Chain Monte Carlo (MCMC) based computation techniques to simulate the parameters from the posteriors. The Bayesian model is flexible enough to identify significant genes as well as to perform future predictions. The method is applied to cancer classification via cDNA microarrays where the genes BRCA1 and BRCA2 are associated with a hereditary disposition to breast cancer, and the method is used to identify a set of significant genes. The method is also applied successfully to the leukemia data.

KeyWords Plus:
EXPRESSION PATTERNS, CDNA MICROARRAYS, BREAST-CANCER, CLASSIFICATION, PREDICTION

Addresses:
Mallick BK, Texas A&M Univ, Dept Stat, College Stn, TX 77843 USA
Texas A&M Univ, Dept Stat, College Stn, TX 77843 USA
Texas A&M Univ, Dept Elect Engn, College Stn, TX 77840 USA
Univ Texas, MD Anderson Canc Ctr, Dept Pathol, Houston, TX 77030 USA
Univ Texas, Dept Math Sci, El Paso, TX 79968 USA

Publisher:
OXFORD UNIV PRESS, OXFORD

IDS Number:
636PA

ISSN:
1367-4803

Article 110 of 244

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