Galaxy-ML: An accessible, reproducible, and scalable machine learning toolkit for biomedicine.

Gu, Qiang; Kumar, Anup; Bray, Simon; Creason, Allison; Khanteymoori, Alireza; Jalili, Vahid; Grüning, Björn; Goecks, Jeremy

Gu, Qiang; Kumar, Anup; Bray, Simon; Creason, Allison; Khanteymoori, Alireza; Jalili, Vahid; Grüning, Björn; Goecks, Jeremy.

Afiliação

Gu Q; Department of Biomedical Engineering, Oregon Health & Science University, Portland, Oregon, United States of America.
Kumar A; The Knight Cancer Institute, Oregon Health & Science University, Portland, Oregon, United States of America.
Bray S; Bioinformatics Group, Department of Computer Science, University of Freiburg, Freiburg, Germany.
Creason A; Bioinformatics Group, Department of Computer Science, University of Freiburg, Freiburg, Germany.
Khanteymoori A; Department of Biomedical Engineering, Oregon Health & Science University, Portland, Oregon, United States of America.
Jalili V; The Knight Cancer Institute, Oregon Health & Science University, Portland, Oregon, United States of America.
Grüning B; Bioinformatics Group, Department of Computer Science, University of Freiburg, Freiburg, Germany.
Goecks J; Department of Biomedical Engineering, Oregon Health & Science University, Portland, Oregon, United States of America.

PLoS Comput Biol ; 17(6): e1009014, 2021 06.

Article em En | MEDLINE | ID: mdl-34061826

Assuntos

Biologia Computacional/métodos; Aprendizado de Máquina; Reprodutibilidade dos Testes; Software

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Texto completo: 1 Coleções: 01-internacional Base de dados: MEDLINE Assunto principal: Biologia Computacional / Aprendizado de Máquina Idioma: En Revista: PLoS Comput Biol Assunto da revista: BIOLOGIA / INFORMATICA MEDICA Ano de publicação: 2021 Tipo de documento: Article País de afiliação: Estados Unidos

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