JSM 2005 Online Program

Abstract #303714

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Activity Number:	136
Type:	Contributed
Date/Time:	Monday, August 8, 2005 : 10:30 AM to 12:20 PM
Sponsor:	Section on Bayesian Statistical Science
Abstract - #303714
Title:	Bayesian Analysis of High-throughput Data with Ordinal Outcomes To Identify Prostate Cancer Biomarkers
Author(s):	Deukwoo Kwon*+ and Mahlet G. Tadesse and Naijun Sha and Marina Vannucci
Companies:	Texas A&M University and University of Pennsylvania and The University of Texas at El Paso and Texas A&M University
Address:	Dept of Statistics, College Station, TX, 77840, United States
Keywords:	Bayesian ordinal probit model ; classification ; Bayesian variable selection ; prostate cancer ; SELDI-TOF mass spectrometry ; wavelet thresholding
Abstract:	In this paper, we investigate the classification for prostate cancer with Bayesian ordinal probit model along with Bayesian variable selection. We also apply wavelets for the preprocessing of proteomic data such as Surface-enhanced laser desorbtion and ionization time-of-flight mass spectra (SELDI-TOF MS). We develop a novel preprocessing procedure using denoising of wavelet analysis. We employ Bayesian variable selection methods based on the smoothed spectra for the classification of cancer patients and the selection of the discriminating peaks of the spectra. We can find biomarkers for prostate cancer from the Bayesian variable selection.

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