Gene regulatory networks with variable-order dynamic Bayesian networks

We introduce a probabilistic framework for building higher-order gene regulatory networks, which automatically finds the delays of regulatory interactions. A variable-order Markov chain Monte Carlo method with a new acceptance mechanism is proposed to find the optimal order and the structure of a dynamic Bayesian network (DBN). Experiments on cell cycle expression data indicate that the variable-order DBN (VDBN) better fits the data and gives biologically more plausible regulatory networks.