An SVM-wrapped multiobjective evolutionary feature selection approach for identifying cancer-microRNA markers

MicroRNAs (miRNAs), have been shown to play important roles in gene regulation and various biological processes. Recent studies have revealed that abnormal expression of some specific miRNAs often results in the development of cancer. Microarray datasets containing the expression profiles of several miRNAs are being used for identification of miRNAs which are differentially expressed in normal and malignant tissue samples. In this article, a multiobjective feature selection approach is proposed for this purpose. The proposed method uses Genetic Algorithm for multiobjective optimization and support vector machine (SVM) classifier as a wrapper for evaluating the chromosomes that encode feature subsets. The performance has been demonstrated on real-life miRNA datasets for and the identified miRNA markers are reported. Moreover biological significance tests have been carried out for the obtained markers.