An efficient hybrid methodology for detection of cancer-causing gene using CSC for micro array data

Cancer is deadly diseases still exist with a lot of subtypes which makes lot of challenges in a biomedical research. The data available of gene expression with relevant gene selection with eliminating redundant genes is challenging for role of classifiers. The availability of multiple scopes of gene expression data is curse, the selection of gene is play vital role for refining gene expression data classification performance. The major role of this article is to derive a heuristic approach to pick the highly relevant genes in gene expression data for the cancer therapy. This article demonstrates a modified bio-inspired algorithm namely cuckoo search with crossover (CSC) for choosing genes from technology of micro array that are able to classify numerous cancer sub-types with extraordinary accuracy. The experiment results are done with five benchmark cancer gene expression datasets. The results depict that CSC is outperforms than CS and other well-known approaches. It returns 99% accuracy in a classification for the dataset namely prostate, lung and lymphoma for top 200 genes. Leukemia and colon dataset CSC is 96.98% and 98.54% respectively.