Clinicians are interested in the estimation of robust
and relevant genetic signatures from gene sequencing data.
Many machine learning approaches have been proposed trying
to address well-known issues of this complex task (feature or
gene selection, classification or model selection, and prediction
assessment). Addressing this problem often requires a deep
knowledge of these methods and some of them demand high
computational resources that may not be affordable. In this
paper, an exhaustive study that includes different types of feature
selection methods and classifiers is presented, providing clinicians an useful insight of the most suitable methods for this purpose. Predictions assessment is performed using a bootstrap crossvalidation strategy as an honest validation scheme. The results of this study for six benchmark datasets show that filter or embedded methods are preferred, in general, to wrapper methods
according to their better statistical significant results, in terms
of accuracy, and lower demand for computational resources.