JSM 2004 - Toronto

Abstract #300567

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Activity Number: 198
Type: Contributed
Date/Time: Tuesday, August 10, 2004 : 8:30 AM to 10:20 AM
Sponsor: Section on Bayesian Statistical Science
Abstract - #300567
Title: Multiclass Cancer Diagnosis Using Bayesian Kernel Machine Models
Author(s): Sounak Chakraborty*+ and Bani K. Mallick and Debashis Ghosh and Malay Ghosh
Companies: University of Florida and Texas A&M University and University of Michigan and University of Florida
Address: 103 Griffin/Floyd Hall - PO Box 118545, Gainesville, FL, 32611,
Keywords: Gibbs sampling ; Markov chain Monte Carlo ; Metropolis-Hastings ; microarrays ; reproducing kernel Hilbert space ; support vector machine
Abstract:

Precise classification of tumors is critical for cancer diagnosis and treatment. Using gene expression microarray data to classify tumor types is a very promising tool in cancer diagnosis. In recent years, several works showed successful classification of pairs of tumor types using gene expression patterns. Usually the multicategory classification problems are solved by using a bunch of binary classifiers which may fail in variety of circumstances. We have proposed the Bayesian multicategory kernel machine for multicategory classification. We have compared our method with several other available methods. The methods are illustrated with two real-life microarray datasets.

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