Construction of GA-optimized Radial Basis Neural Network from HIV-1 Vpr Mutant Microarray Gene Expression Data

Finding the most significant gene from microarray time series data is important for designing drugs of particular disease. Con- struction of Neural Network through protein interactions is a vital and useful approach to develop new drugs target. Some of the computational tools are being utilized for predicting the viral-host interactions. The database of human HIV-1 Vpr mutant gene expression microarray time series expression value contain records of experimentally validated interactions. The main problem to analyze this type of microarray data is classification problem as because human HIV-1 Vpr mutant cell is an infected dendritic cell. We firstly, have clustered the gene microarray time series data using subtractive clustering method then construct Radial Basis Neural Network on cluster of HIV-1 Vpr mutant microarray time series data. The network output is optimized by using Genetic Algorithm and from the optimized value of network output we got a significant gene which lead to drug discovery in future.