An Order-independent Algorithm for Inferring Directed Gene Regulatory Networks from Incomplete Data

The inference of gene regulatory networks from incomplete data can be a challenging task in bioinformatics. This study presents a method for inferring gene regularity networks (GRN) from incomplete gene expression data sets. Regulation of gene expression and revealing the structure and dynamics of a gene regulatory network is of great interest and represents a considerably challenging computational problem [1]. If we understand the biological activity from signal emulsion to metabolic dynamics, then we can prioritize potential drug targets of various diseases, devise effective therapeutics, and discover the novel pathways [2].