Bayesian Models for Variable Selection that Incorporate Biological Information

Variable selection has been the focus of much research in recent years. Bayesian methods have found many successful applications, particularly in situations where the amount of measured variables can be much greater than the number of observations. One such example is the analysis of genomics data. In this paper we first review Bayesian variable selection methods for linear settings, including regression and classification models. We focus in particular on recent prior constructions that have been used for the analysis of genomic data and briefly describe two novel applications that integrate different sources of biological information into the analysis of experimental data. Next, we address variable selection for a different modeling context, i.e., mixture models. We address both clustering and discriminant analysis settings and conclude with an application to gene expression data for patients affected by leukemia.