A Bayesian network based algorithm for gene regulatory network reconstruction

摘要

Bayesian network (BN) modeling is a commonly used method for constructing gene regulatory networks from gene microarray data. Learning the structures of BNs from data is of significant importance in applications of various fields. In this paper, we propose a Sparse Graph Search (SGS) algorithm that not only reduces BN computation times significantly but also obtains optimal network constructions by using hybrid approach that combines search-and-score with constraint-based method. The algorithm is applied to several sets of benchmark networks and is shown to outperform PC and TPDA algorithms.