Research Techniques Made Simple: Feature Selection for Biomarker Discovery

Abstract

Molecular biomarkers can be powerful tools for aiding in the efficiency and precision of clinical decision-making. Feature selection methods, machine-learning, and biostatistics have been applied to discover subsets of molecular markers that identify target classes of clinical cases. For example, in the field of dermatology, these approaches have been used to develop predictive models that identify skin diseases, ranging from melanoma to psoriasis, based upon a variety of biomarkers. However, a continuous increase in the variety and size of datasets from which candidate biomarkers can be derived, and limitations in the computational tools used to analyze them, have hindered the interpretability of biomarker discovery studies. In this article, the various methods of feature selection are described along with the important steps needed to properly validate the performance of the selected methods. Limitations and suggestions toward uses of these methods are discussed.