The max-min high-order dynamic Bayesian network learning for identifying gene regulatory networks from time-series microarray data

Abstract

We propose a new high-order dynamic Bayesian network (HO-DBN) learning approach, called Max-Min High-Order DBN (MMHO-DBN), for discrete time-series data. MMHO-DBN explicitly models the time lags between parents and target in an efficient manner. It extends the Max-Min Hill-Climbing Bayesian network (MMHC-BN) technique which was originally devised for learning a BN's structure from static data. Both Max-Min approaches are hybrid local learning methods which fuse concepts from both constraint-based Bayesian techniques and search-and-score Bayesian methods. The MMHO-DBN first uses constraint-based ideas to limit the space of potential structure and then applies search-and-score ideas to search for an optimal HO-DBN structure. We evaluated the ability of our MMHO-DBN approach to identify genetic regulatory networks (GRN's) from gene expression time-series data. Preliminary results on artificial and real gene expression time-series are encouraging and show that it is able to learn (long) time-delayed relationships between genes, and faster than current HO-DBN learning methods.