  It is very challenging to select informative features from tens of thousands
of measured features in high-throughput data analysis. Recently, several
parametric/regression models have been developed utilizing the gene network
information to select genes or pathways strongly associated with a
clinical/biological outcome. Alternatively, in this paper, we propose a
nonparametric Bayesian model for gene selection incorporating network
information. In addition to identifying genes that have a strong association
with a clinical outcome, our model can select genes with particular
expressional behavior, in which case the regression models are not directly
applicable. We show that our proposed model is equivalent to an infinity
mixture model for which we develop a posterior computation algorithm based on
Markov chain Monte Carlo (MCMC) methods. We also propose two fast computing
algorithms that approximate the posterior simulation with good accuracy but
relatively low computational cost. We illustrate our methods on simulation
studies and the analysis of Spellman yeast cell cycle microarray data.
