PNNL

Abstract

Most current approaches to automatic pathway generation are based on a reverse engineering approach in which pathway plausibility is solely derived from microarray gene expression data. These approaches tend to lack in generality and offer no independent validation as they are too reliant on the pathway observables that guide pathway generation. By contrast, alternative approaches that use prior biological knowledge to validate pathways inferred from gene expression data 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 that combines insights from the reverse engineering and knowledge-based approaches to increase the biological plausibility of automatically generated regulatory networks and describe an application of this approach to transcriptional data from a mouse model of neuroprotection during stroke.