A self-tuning genetic algorithm with applications in biomarker discovery

Abstract

Recent developments in the field of -omics technologies brought great potential for conducting biomedical research in very efficient manner, but also raised a plethora of new computational challenges to be addressed. Extremely high dimensionality accompanied with poor signal-to-noise ratio and small sample size of data resulting from high-throughput experiments pose previously unprecedented problem, creating an increasing demand for innovative analytical strategies. In this work we propose an island model-based genetic algorithm for multivariate feature selection in the context of -omics data, which accommodates to a particular classification scenario via dynamic tuning of its parameters. We demonstrate it on two publicly available data sets containing gene expression profiles corresponding to the two distinct biomedical questions. We show that the algorithm consistently outperforms two additional feature selection schemes across data sets, regardless to which method is used in the subsequent classification step.