DESIGN AND DEVELOPMENT OF ADVANCED SIMILARITY MEASURE FOR RECONSTRUCTING GRN USING mRNA EXPRESSION PROFILES

Abstract

Gene Regulatory Networks (GRNs) reconstruction aims to infer relationships of potential regulation among the genes. With the rapid growth of the biotechnology, such as Ribonucleic acid (RNA)-sequencing and gene chip microarray, the generated high-throughput data provide gene–gene interaction relationships with more opportunities based on gene expression data. Several approaches are introduced to reconstruct the GRNs, but low accuracy is a major drawback. Hence, this paper introduces the hybrid distance measure and the Pearson’s correlation coefficient for reconstructing GRN. The hybrid distance, such as Tversky index, Tanimoto similarity, and Minkowski distance, is employed to connect the edges. The asymmetric partial correlation network is introduced for determining two influence functions for every pair, and edge direction is determined among them. However, the direction of edges is unknown usually and seems difficult to be identified based on gene expression data. Thus, it extends the data processing inequality applying in the directed network for removing the transitive interactions. The influence value of every node is calculated for identifying the significant regulator. The performance of the proposed Hybrid Distance_Entropy based GRN Reconstruction method is analyzed in terms of correlation, reconstruction error, precision, and recall, which provides superior results with values 0.9450, 0.00052, 0.9095, and 0.8913 based on dataset-1.