Biomathematics & Statistics Scotland
There have been various attempts to reconstruct gene regulatory networks from microarray expression data in the past. However, owing to the limited number of independent experimental conditions and the noise inherent in the measurements, the results have been rather modest so far. For this reason it seems advisable to include biological prior knowledge, related, for instance, to transcription factor binding locations in promoter regions or partially known signalling pathways from the literature. We have developed a Bayesian approach to integrate expression data with multiple sources of prior knowledge, e.g. extracted from the Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway database. We have evaluated the proposed scheme on the Raf signalling pathway, a cellular signalling network describing the interaction of 11 phosphorylated proteins and phospholipids in mammalian immune system cells, demonstrating the benefits of combining biological knowledge with gene expression data.
Comparison of methods to predict the Raf regulatory network, with (DGE) and without (UGE) taking the edge directions into account. We determined the number of true positive interactions for a fixed number, five, of false positives.Bayesian networks (BN), and graphical Gaussian models (GGM), use information from the expression data. Biological knowledge from KEGG is used either in isolation (OnlyPrior) or using our new Bayesian integration scheme (BN&Prior).
Further details from: Dirk Husmeier
