Graphs and networks are common ways of depicting information. In biology, many different processes are represented by graphs, such as regulatory networks, metabolic pathways and protein-protein interaction networks. This information provides useful supplement to the standard numerical genomic data such as microarray gene expression data. Effectively utilizing such an information can lead to a better identification of biologically relevant genomic features in the context of our prior biological knowledge. In this paper, we present a Bayesian variable selection procedure for network-structured covariates for both Gaussian linear and probit models. The key of our approach is the introduction of a Markov random field prior for the indicator variables that describe which covariates should be included in the model and the use of the Wolff algorithm for Markov Chain Monte Carlo inference. We illustrate the proposed procedure with simulations and with an analysis of genomic data. Finally, we present some other areas of genomics research where novel Bayesian approaches may play important roles.


Bioinformatics | Computational Biology