Predicting Outcomes of Hormone and Chemotherapy in the Molecular Taxonomy of Breast Cancer International Consortium (METABRIC) Study by Biochemically-inspired Machine Learning.

Mucaki, Eliseos J; Baranova, Katherina; Pham, Huy Q; Rezaeian, Iman; Angelov, Dimo; Ngom, Alioune; Rueda, Luis; Rogan, Peter K

Predicting Outcomes of Hormone and Chemotherapy in the Molecular Taxonomy of Breast Cancer International Consortium (METABRIC) Study by Biochemically-inspired Machine Learning.

Mucaki, Eliseos J; Baranova, Katherina; Pham, Huy Q; Rezaeian, Iman; Angelov, Dimo; Ngom, Alioune; Rueda, Luis; Rogan, Peter K.

Afiliação

Mucaki EJ; Deparment of Biochemistry , University of Western Ontario, London, Canada.
Baranova K; Deparment of Biochemistry , University of Western Ontario, London, Canada.
Pham HQ; School of Computer Science, University of Windsor, Windsor, Canada.
Rezaeian I; School of Computer Science, University of Windsor, Windsor, Canada.
Angelov D; Department of Computer Science, University of Western Ontario, London, Canada.
Ngom A; School of Computer Science, University of Windsor, Windsor, Canada.
Rueda L; School of Computer Science, University of Windsor, Windsor, Canada.
Rogan PK; Deparment of Biochemistry , University of Western Ontario, London, Canada.

F1000Res ; 5: 2124, 2016.

Article em En | MEDLINE | ID: mdl-28620450

Palavras-chave

Gene expression signatures; breast cancer; chemotherapy resistance; hormone therapy; machine learning; random forest; support vector machine

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Texto completo: 1 Coleções: 01-internacional Base de dados: MEDLINE Tipo de estudo: Prognostic_studies / Risk_factors_studies Idioma: En Ano de publicação: 2016 Tipo de documento: Article

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