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bioRxiv · 10.1101/2020.11.18.388579

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

Valcarcel, L. V.·San Jose-Eneriz, E.·Cendoya, X.·Rubio, A.·Agirre, X.·Prosper, F.·Planes, F. J.
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Abstract

MotivationWith 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. ResultsWe 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. AvailabilityA Matlab implementation of BOSO is available as a Supplementary Material. Contactfplanes@tecnun.es Supplementary InformationSupplementary data are available at Bioinformatics online.

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Valcarcel, L. V., San Jose-Eneriz, E., Cendoya, X., Rubio, A., Agirre, X., Prosper, F., Planes, F. J.. 2020-11-20. BOSO: a novel feature selection algorithm for linear regression with high-dimensional data. https://doi.org/10.1101/2020.11.18.388579

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