<p>This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.</p> <p>The use of large-scale microarray expression profiling to identify predictors of disease class has become of major interest. Beyond their impact in the clinical setting (i.e.
improving diagnosis and treatment), these markers are also likely to provide clues on
the molecular mechanisms underlining the diseases. In this paper we describe a new
method for the identification of multiple gene predictors of disease class. The method
is applied to the classification of two forms of arthritis that have a similar clinical
endpoint but different underlying molecular mechanisms: rheumatoid arthritis (RA)
and osteoarthritis (OA). We aim at both the classification of samples and the location
of genes characterizing the different classes. We achieve both goals simultaneously by
combining a binary probit model for classification with Bayesian variable selection
methods to identify important genes.We find very small sets of genes that lead to good
classification results. Some of the selected genes are clearly correlated with known
aspects of the biology of arthritis and, in some cases, reflect already known differences
between RA and OA.</p>
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Comp Funct Genomics. 2003 Apr; 4(2):171-181