Abstract Motivation With the frenetic growth of high-dimensional datasets in different biomedical domains, there is an urgent need to develop predictive methods able to deal with this complexity. Feature selection is a relevant strategy in machine learning to address this challenge. Results We introduce a novel feature selection algorithm for linear regression called BOSO (Bilevel Optimization Selector Operator). We conducted a benchmark of BOSO with key algorithms in the literature, finding a superior performance in highdimensional datasets. Proof-of-concept of BOSO for predicting drug sensitivity in cancer is presented. A detailed analysis is carried out for methotrexate, a well-studied drug targeting cancer metabolism. Availability A Matlab implementation of BOSO is available as a Supplementary Material. Contact fplanes@tecnun.es Supplementary Information Supplementary data are available at Bioinformatics online.

BOSO: a novel feature selection algorithm for linear regression with high-dimensional data