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

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. We introduce a novel feature selecti...

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Bibliographic Details
Published in:	PLoS computational biology 2022-05, Vol.18 (5), p.e1010180-e1010180
Main Authors:	Valcárcel, Luis V, San José-Enériz, Edurne, Cendoya, Xabier, Rubio, Ángel, Agirre, Xabier, Prósper, Felipe, Planes, Francisco J
Format:	Article
Language:	eng
Subjects:	Algorithms Analysis Cancer Computer and Information Sciences Datasets Drug metabolism Engineering and Technology Feature selection Humans Linear Models Machine Learning Medicine and Health Sciences Metabolism Methods Methotrexate Neoplasms - drug therapy Neoplasms - metabolism Optimization Physical Sciences Physiological aspects Regression analysis Research and Analysis Methods Signal to noise ratio Variables
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Summary:	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. 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 accuracy for feature selection in high-dimensional 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.
ISSN:	1553-7358 1553-734X 1553-7358