PNNL

Abstract

One of the greatest challenges in today’s analysis of microarray gene expression data is to identify pathways across regulated genes that underlie structural and functional changes of living cells in specific pathologies. Most current approaches to pathway generation are based on a reverse engineering approach in which pathway plausibility is solely induced from observed pathway data. These approaches tend to lack in generality as they are too dependent on the pathway observables from which they are induced. By contrast, alternative approaches that rely on prior biological knowledge may err in the opposite direction as the prior knowledge is usually not sufficiently tuned to the pathology of focus. In this paper, we present a novel pathway generation approach which combines insights from the reverse engineering and knowledge-based approaches to increase the biological plausibility and specificity of induced regulatory networks.