Rosa Aghdam

Rosa received her B.Sc. in Statistiucs from Shahid Beheshti University and her M.Sc. in Statistics from Tehran University

Inferring Gene Regulatory Networks (GRNs) from gene expression data is a major challenge in systems biology. The Path Consistency (PC) algorithm is one of the popular methods in this field. However, as an order dependent algorithm, PC algorithm is not robust because it achieves different network topologies if gene orders are permuted. In addition, the performance of this algorithm depends on the threshold value used for independence tests. Consequently, selecting suitable sequential ordering of nodes and an appropriate threshold value for the inputs of PC algorithm are challenges to infer a good GRN. We propose heuristic algorithms to infer GRNs. The effectiveness of proposed methods is benchmarked through several networks from the DREAM challenge and the widely used SOS DNA repair network in Escherichia coli. The results indicate that the new algorithms are suitable for learning GRNs and it considerably improves the precision of network inference.

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