Parisa Niloofar - Department of mathematical sciences, Faculty of statistics, university of Bojnord, Bojnord, Iran
Rosa Aghdam - School of Biological Sciences, Institute for Research in Fundamental Sciences (IPM), Tehran, Iran
Changiz Eslahchi - School of Biological Sciences, Institute for Research in Fundamental Sciences (IPM), Tehran, IranDepartment of Computer Sciences, Faculty of Mathematical Sciences, Shahid Beheshti University, Tehran, Iran

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].