Gene Regulatory Identification Based on the Novel Hybrid Time-Delayed Method - Bao, Wenzheng; Lin, Xiao; Yang, Bin; Chen, Baitong (Frontiers in Genetics, 2022)
Gene regulatory network (GRN) inference with biology data is a difficult and hot issue in system biology research field. To detect direct the associations of GRN more accurately, a novel two-step GRN inference technique based on time-delayed correlation coefficient (TDCC) time-delayed complex-valued S-system model (TDCVSS) is proposed. Firstly TDCC algorithm is utilized to construct an initial network. Secondly for each target gene TDCVSS model is utilized to prune the network topology in order to delete false-positive regulatory relationships. Complex-valued restricted additive tree and complex-valued differential evolution are proposed to approximate the optimal TDCVSS model. Finally the overall network could be inferred by integrating the regulations of all target genes. Two real gene expression datasets from E. coli and S. cerevisiae gene networks are used to evaluate the performance of our proposed two-step GRN inference algorithm. The results demonstrate that the proposed algorithm could infer more correctly GRN than classical methods and time-delayed methods.