BOSO: A novel feature selection algorithm for linear regression with high-dimensional data.
Journal
PLoS computational biology
ISSN: 1553-7358
Titre abrégé: PLoS Comput Biol
Pays: United States
ID NLM: 101238922
Informations de publication
Date de publication:
05 2022
05 2022
Historique:
received:
23
06
2021
accepted:
07
05
2022
revised:
10
06
2022
pubmed:
1
6
2022
medline:
15
6
2022
entrez:
31
5
2022
Statut:
epublish
Résumé
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.
Identifiants
pubmed: 35639775
doi: 10.1371/journal.pcbi.1010180
pii: PCOMPBIOL-D-21-01162
pmc: PMC9187084
doi:
Types de publication
Journal Article
Research Support, Non-U.S. Gov't
Langues
eng
Sous-ensembles de citation
IM
Pagination
e1010180Subventions
Organisme : Cancer Research UK
ID : C355/A26819
Pays : United Kingdom
Déclaration de conflit d'intérêts
The authors have declared that no competing interests exist.
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