Parallel Bayesian network structure learning with application to gene networks

Bayesian networks (BN) are probabilistic graphical models which are widely utilized in various research areas, including modeling complex biological interactions in the cell. Learning the structure of a BN is an NP-hard problem and exact solutions are limited to a few tens of variables. In this work, we present a parallel BN structure learning algorithm that combines principles of both heuristic and exact approaches and facilitates learning of larger networks. We demonstrate the applicability of our approach by an implementation on a Cray AMD cluster, and present experimental results for the problem of inferring gene networks. Our approach is work-optimal and achieves nearly perfect scaling.