Identification of Effective Genes from HIV-1 VPR Microarray Datasets after Constructing GA-Optimized Genetic Network

Abstract

Identification of effective genes from microarray dataset is important for obtaining relationship between that gene and a particular disease. For this, Gene Regulatory Network (GRN) construction is important. In this paper, GA-optimized GRNs are constructed using radial basis neural network (RBN) approach within cluster center matrix of human HIV-1 Vpr mutant microarray time series gene expression data. After this, effective genes from each microarray dataset are found by calculating minimum Euclidean distance between optimized output value and corresponding dataset.