Recursive Memetic Algorithm for gene selection in microarray data

Abstract Feature selection algorithm contributes a lot in the domain of medical diagnosis. Choosing a small subset of genes that enable a classifier to predict the presence or type of disease accurately is a difficult optimisation problem due to the size of the microarray data. The dual task of achieving higher accuracy and a small number of features makes it a challenging research problem. In our work, we have developed a Recursive Memetic Algorithm (RMA) model for selection of genes. It is a variant of Memetic Algorithm (MA) and performs much better than MA as well as Genetic Algorithm (GA). RMA has been applied on seven microarray datasets namely, AMLGSE2191, Colon, DLBCL, Leukaemia, Prostate, MLL and SRBCT. Encouraging results obtained by the proposed model, reported in this article, are biologically validated with the use of Gene Oncology, KEGG pathways and heat maps.

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