Bioinformatics

Identifying significant differentially expressed genes of a disease can help understand the disease at the genomic level. A hierarchical statistical model named multi-class kernel-imbedded Gaussian process (mKIGP) is developed under a Bayesian framework for a multi-class classification problem using microarray gene expression data. Specifically, based on a multinomial probit regression setting, an empirically adaptive algorithm with a cascading structure is designed to find appropriate featuring kernels, to discover potentially significant genes, and to make optimal tumor/cancer class predictions. A Gibbs sampler is adopted as the core of the algorithm to perform Bayesian inferences. A prescreening procedure is implemented to alleviate the computational complexity. The simulated examples s…