Reverse engineering time series of gene expression data using Dynamic Bayesian networks and covariance matrix adaptation evolution strategy with explicit memory

Dynamic Bayesian networks are of particular interest to reverse engineering of gene regulatory networks from temporal transcriptional data. However, the problem of learning the structure of these networks is quite challenging. This is mainly due to the high dimensionality of the search space that makes exhaustive methods for structure learning not practical. Consequently, heuristic techniques such as Hill Climbing are used for DBN structure learning. Hill Climbing is not an efficient method for this purpose as it is prone to get trapped in local optima and the learned network is not very accurate.