Bayesian Variable Selection in Multinomial Probit Models to Identify Molecular Signatures of Disease Stage
Article first published online: 27 AUG 2004
Volume 60, Issue 3, pages 812–819, September 2004
How to Cite
Sha, N., Vannucci, M., Tadesse, M. G., Brown, P. J., Dragoni, I., Davies, N., Roberts, T. C., Contestabile, A., Salmon, M., Buckley, C. and Falciani, F. (2004), Bayesian Variable Selection in Multinomial Probit Models to Identify Molecular Signatures of Disease Stage. Biometrics, 60: 812–819. doi: 10.1111/j.0006-341X.2004.00233.x
- Issue published online: 27 AUG 2004
- Article first published online: 27 AUG 2004
- Received May 2002. Revised October 2003. Accepted January 2004.
- Bayesian variable selection;
- DNA microarrays;
- Latent variables;
- Multinomial probit model;
- Truncated sampling
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.