JSM 2004 - Toronto

Abstract #300011

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Activity Number: 97
Type: Invited
Date/Time: Monday, August 9, 2004 : 10:30 AM to 12:20 PM
Sponsor: WNAR
Abstract - #300011
Title: Bayesian Variable Selection for Gene Expression Analysis
Author(s): Marina Vannucci*+
Companies: Texas A&M University
Address: , College Station, TX, 77843-3143,
Keywords: Bayesian methods ; microarrays ; variable selection
Abstract:

The analysis of the high-dimensional data generated by DNA microarrays poses challenge to standard statistical methods. This has revived a strong interest in classification and clustering algorithms. Currently, investigators first resort to data-filtering procedures or dimension reduction techniques before applying a statistical model. We propose the use of Bayesian variable selection techniques built directly into the statistical modeling. The flexibility of the priors allows us to incorporate biological information.

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Revised March 2004