A Boolean-based machine learning framework identifies predictive biomarkers of HSP90-targeted therapy response in prostate cancer.

Shin, Sung-Young; Centenera, Margaret M; Hodgson, Joshua T; Nguyen, Elizabeth V; Butler, Lisa M; Daly, Roger J; Nguyen, Lan K

Shin, Sung-Young; Centenera, Margaret M; Hodgson, Joshua T; Nguyen, Elizabeth V; Butler, Lisa M; Daly, Roger J; Nguyen, Lan K.

Affiliation

Shin SY; Department of Biochemistry and Molecular Biology, Monash University, Clayton, VIC, Australia.
Centenera MM; Cancer Program, Biomedicine Discovery Institute, Monash University, Clayton, VIC, Australia.
Hodgson JT; South Australian Immunogenomics Cancer Institute and Freemasons Foundation Centre for Men's Health, University of Adelaide, Adelaide, SA, Australia.
Nguyen EV; South Australian Health and Medical Research Institute, Adelaide, SA, Australia.
Butler LM; South Australian Immunogenomics Cancer Institute and Freemasons Foundation Centre for Men's Health, University of Adelaide, Adelaide, SA, Australia.
Daly RJ; South Australian Health and Medical Research Institute, Adelaide, SA, Australia.
Nguyen LK; Department of Biochemistry and Molecular Biology, Monash University, Clayton, VIC, Australia.

Front Mol Biosci ; 10: 1094321, 2023.

Article de En | MEDLINE | ID: mdl-36743211

Mots clés

17-AAG; Boolean function minimization; Hsp90 inhibitor; feature selection; machine learning; precision oncology; predictive biomarker; prostate cancer

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Texte intégral: 1 Collection: 01-internacional Base de données: MEDLINE Type d'étude: Prognostic_studies / Risk_factors_studies Langue: En Journal: Front Mol Biosci Année: 2023 Type de document: Article Pays d'affiliation: Australie Pays de publication: Suisse

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