Inference of gene regulatory networks from time-series microarray data
The regulation of gene expression is a dynamic process, hence it is of vital interest to identify and characterize these dynamic processes. Since genes work in a cascade of networks, gene regulatory network (GRN) reconstruction is a crucial process for thorough understanding of the underlying biological interactions. We present here a technique based on partial correlations to infer the GRN. Our proposed technique takes into account the possible and variable time delays between various genes. The results have shown that our proposed algorithm has results consistent with the existing biological knowledge. Our algorithm is implemented in R.