Inference of gene regulatory network with regulation type based on signed graph convolutional network from time-series data

Gene regulatory network GRN inference has been an essential challenge in systems biology. Currently, most existing methods for GRN reconstruction ignore the information about the regulation types, such as activation or inhibition regulation. Additionally, concerning the characteristics of time-series data, most methods employ the same approach to process the time-series expression values of different samples, without considering the differences in gene expression values among them. To this end, this work proposes the SGCGRNT model Signed Graph Convolutional neural network for GRN Inference from Time-series data , which utilizes a signed graph convolutional network to infer GRNs with both the direction and regulatory type from time-series data. In addition, we define Spear-man’s Rank Correlation Mutual Information S-RMI to enable SGCGRNT to adapt to various types of gene expression data. Furthermore, the sampling idea of GraphSAGE is adopted, which can significantly save time and resources when processing large sample datasets. Experimental results demonstrate SGCGRNT can accurately predict GRNs with both direction and regulation types.