Abstract #301812

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JSM 2003 Abstract #301812
Activity Number: 346
Type: Invited
Date/Time: Wednesday, August 6, 2003 : 10:30 AM to 12:20 PM
Sponsor: Section on Bayesian Stat. Sciences
Abstract - #301812
Title: Bayesian Classification of Tumors Using Gene Expression Data
Author(s): Debashis Ghosh*+
Companies: University of Michigan
Address: 1420 Washington Heights, Ann Arbor, MI, 48109-2009,
Keywords: cross-species comparison ; microarray data ; time-course experiment
Abstract:

In recent years, there has been a move toward the use of Cdna microarrays for tumor classification. These high-throughput assays provide relative Mrna expression measurements simultaneously for thousands of genes. A key statistical task is to perform classification via different expression patterns. This paper considers several Bayesian classification methods for the analysis of microarray data based on reproducing kernel Hilbert spaces. We consider the logistic likelihood as well as likelihoods related to the Support Vector Machine (SVM) models. It is shown through three examples that SVM models with multiple shrinkage parameters produce the least amount of misclassification errors in comparison to several existing classical methods as well as Bayesian methods based on the logistic likelihood or those involving only one shrinkage parameter.

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