Applications of Bayesian Gene Selection and Classification with Mixtures of Generalized Singular g-Priors

Abstract EN

Recent advancement in microarray technologies has led to a collection of an enormous number of genetic markers in disease association studies, and yet scientists are interested in selecting a smaller set of genes to explore the relation between genes and disease.

Current approaches either adopt a single marker test which ignores the possible interaction among genes or consider a multistage procedure that reduces the large size of genes before evaluation of the association.

Among the latter, Bayesian analysis can further accommodate the correlation between genes through the specification of a multivariate prior distribution and estimate the probabilities of association through latent variables.

The covariance matrix, however, depends on an unknown parameter.

In this research, we suggested a reference hyperprior distribution for such uncertainty, outlined the implementation of its computation, and illustrated this fully Bayesian approach with a colon and leukemia cancer study.

Comparison with other existing methods was also conducted.

The classification accuracy of our proposed model is higher with a smaller set of selected genes.

The results not only replicated findings in several earlier studies, but also provided the strength of association with posterior probabilities.