Improving the classification accuracy using biomarkers selected from machine learning methods

High-dimensional data encountered in genomic and proteomic studies are often limited by the sample size but has a higher number of predictor variables. Therefore selecting the most relevant variables that are correlated with the outcome variable is a crucial step. This paper describes an approach for selecting a set of optimal variables to achieve a classification model with high predictive accuracy. The work described using a biological classifier published elsewhere but it can be generalized for any application.