Bayesian variable selection in multinomial probit models to identify molecular signatures of disease stage.
Here we focus on discrimination problems where the number of predictors substantially exceeds the sample size and we propose a Bayesian variable selection approach to multinomial probit models. Our method makes use of mixture priors and Markov chain Monte Carlo techniques to select sets of variables that differ among the classes. We apply our methodology to a problem in functional genomics using gene expression profiling data. The aim of the analysis is to identify molecular signatures that characterize two different stages of rheumatoid arthritis.
Sha
Naijun
N
Department of Mathematical Sciences, University of Texas at El Paso, Texas 79968-0514, USA.
Vannucci
Marina
M
Tadesse
Mahlet G
MG
Brown
Philip J
PJ
Dragoni
Ilaria
I
Davies
Nick
N
Roberts
Tracy C
TC
Contestabile
Andrea
A
Salmon
Mike
M
Buckley
Chris
C
Falciani
Francesco
F
D001172:Arthritis, Rheumatoid / Q000145:classification / Q000235:genetics / Q000503:physiopathology; D001499:Bayes Theorem; D001699:Biometry; D006801:Humans; D008390:Markov Chains; D008954:Models, Biological; D015233:Models, Statistical; D009010:Monte Carlo Method; D020411:Oligonucleotide Array Sequence Analysis / Q000706:statistics & numerical data*