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Bayesian Variable Selection in Multinomial Probit Models to Identify Molecular Signatures of Disease Stage
Author(s) -
Sha Naijun,
Vannucci Marina,
Tadesse Mahlet G.,
Brown Philip J.,
Dragoni Ilaria,
Davies Nick,
Roberts Tracy C.,
Contestabile Andrea,
Salmon Mike,
Buckley Chris,
Falciani Francesco
Publication year - 2004
Publication title -
biometrics
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 2.298
H-Index - 130
eISSN - 1541-0420
pISSN - 0006-341X
DOI - 10.1111/j.0006-341x.2004.00233.x
Subject(s) - multinomial probit , markov chain monte carlo , feature selection , multinomial distribution , bayesian probability , prior probability , computer science , econometrics , statistics , probit model , machine learning , artificial intelligence , mathematics
Summary 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.