A review of feature selection techniques in bioinformatics

This article addresses the growing need for feature selection in bioinformatics, where high-dimensional biological data makes identifying relevant variables essential. Beyond the large pool of methods already developed in the machine learning and data mining fields, the authors note that specific bioinformatics applications have led to a wealth of newly proposed techniques, and they organize this landscape by providing a basic taxonomy of feature selection approaches.

The review discusses the use, variety, and potential of these techniques across a number of both common and upcoming bioinformatics applications, aiming to make interested readers aware of the possibilities of feature selection. By surveying methods and their applications together, it serves as a reference point for choosing and applying feature selection in biological data analysis.