Radial basis function neural network with genetic algorithm for discrimination of recombination hotspots in saccharomyces cerevisiae

Abstract

Recombination influences the evolution of saccharomyces cerevisiae. Genomic regions where recombinations occurs are known as recombination hotspots. There are two kind of hotspots for recombination. The spots where recombination occurs more frequently are called recombination hot spots and regions where recombination occurs less frequently are known as cold spots. In this work, we have formulated methods based on neural network models for the classification of these hot and cold recombination spots on the basis of compositional features of nucleotide sequences. These models were validated using tenfold cross validation technique. The classification accuracy of 83%, 82%, and 78% were achieved using radial basis function neural network with genetic algorithm, radial basis function neural network and multilayer perceptron models respectively. Moreover, the performance of these model were evaluated on different ct classification measurements. Furthermore, results indicate that redial basis function neural network with genetic algorithm gives best result.