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Identification of Effective Genes from HIV-1 VPR Microarray Datasets after Constructing GA-Optimized Genetic Network
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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.
Keywords
HIV-1, Microarray Data, Subtractive Clustering, Radial Basis Neural Network, Optimization, Euclidian Distance.
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