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The vast amount of biological knowledge accumulated over the years hasallowed researchers to identify various biochemical interactions and definedifferent families of pathways. There is an increased interest in identifyingpathways and pathway elements involved in particular biological processes. Drugdiscovery efforts, for example, are focused on identifying biomarkers as wellas pathways related to a disease. We propose a Bayesian model that addressesthis question by incorporating information on pathways and gene networks in theanalysis of DNA microarray data. Such information is used to define pathwaysummaries, specify prior distributions, and structure the MCMC moves to fit themodel. We illustrate the method with an application to gene expression datawith censored survival outcomes. In addition to identifying markers that wouldhave been missed otherwise and improving prediction accuracy, the integrationof existing biological knowledge into the analysis provides a betterunderstanding of underlying molecular processes.

Incorporating biological information into linear models: A Bayesian approach to the selection of pathways and genes