Inference of Biologically Relevant Gene Influence Networks Using the Directed Information Criterion

The systematic inference of biologically relevant influence networks remains a challenging problem in computational biology. Even though the availability of high-throughput data has enabled us to use probabilistic models to infer the plausible structure of such networks, their true interpretation of the biology of the process is questionable. In this work, we propose a probabilistic network inference methodology, based on the directed information criterion, which incorporates the biology of transcription within the framework, so as to enable experimentally verifiable inference. We use a publicly available embryonic kidney microarray dataset to demonstrate our results on the regulation of the Gata2/Gata3 genes