Construction and Comparison of Gene Regulatory Networks of Human Hiv-1 VPR Microarray Datasets by Radial Basis Neural Network Approach

Gene Regulatory Network (GRN) construction by using neural network approach is very important and useful approach for analyzing microarray gene expression microarray datasets. The human HIV-1 Vpr mutant microarray time series gene expression value carries the experimentally validated interaction records. Firstly, the subtractive clustering approach is used to cluster the microarray data. Secondly, GRN is constructed within cluster centers of HIV-1 Vpr mutant dataset using Radial Basis Neural Network approach. The optimized output of genetic network is found using genetic algorithm. Then the influence of range of cluster centers of data clusters and also GRN outputs are compared. It is found that proximity of gene expressed values in wild type cell line HIV-1 is higher than other two HIV-1 Vpr mutants. In this paper, the nature of network output functions are also identified.