Meta-Analysis in Autism Gene Expression Dataset with Biclustering Methods using Random Cuckoo Search Algorithm

Nature inspired Computing techniques have been significant tools for solving multiple NPI hard optimization problems. Microarray technology is a dominant method for observing the expression level of thousands of genes in parallel applying this technology the expression levels of genes are determined. In this paper, investigation is done for autism data set. Assessing a bicluster for gene expression autism dataset is an optimization problem. Nature inspired computing are admired for solving real-world problems are enhancing huge, difficult and vibrant. with cause of the size and difficulty of the problems, it is essential to locate as optimization method. Cuckoo search algorithm, is able to measure by capturing the near best optimal solution within a short duration. In this paper, cuckoo search algorithm has been analyzed with autism dataset. The algorithm shows that Random cuckoo search gives better result than PSO. The attained results prove the high performance of the proposed algorithm in comparison of the other algorithms and also our algorithm can reveal the hidden gene expression patterns from large quantities of expression.