A Model-Free Greedy Gene Selection for Microarray Sample Class Prediction

Microarray data analysis is notoriously challenging as it involves a huge number of genes compared to only a limited number of samples. Gene selection, to detect the most significantly differentially expressed genes under different categories of conditions, is both computationally and biologically interesting, and has become a central research focus in all studies that use gene expression microarray technology. Despite many existing efforts, better gene selection methods that can effectively identify biologically significant biomarkers, yet computationally efficient, are still in need. In this paper, a model-free greedy (MFG) gene selection method is proposed, which implements several intuitive heuristics but doesn´t assume any statistical distribution on the expression data. The experimental results on three real microarray datasets showed that the MFG method combined with a support vector machine (SVM) classifier or a k-nearest neighbor (KNN) classifier is efficient and robust in identifying discriminatory genes