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

Authors :
Björn Grüning
Allison L. Creason
Jeremy Goecks
Anup Kumar
Alireza Khanteymoori
Vahid Jalili
Simon Bray
Qiang Gu
Source :
PLoS Computational Biology, Vol 17, Iss 6, p e1009014 (2021), PLoS Computational Biology
Publication Year :
2021
Publisher :
Public Library of Science (PLoS), 2021.

Abstract

Supervised machine learning is an essential but difficult to use approach in biomedical data analysis. The Galaxy-ML toolkit (https://galaxyproject.org/community/machine-learning/) makes supervised machine learning more accessible to biomedical scientists by enabling them to perform end-to-end reproducible machine learning analyses at large scale using only a web browser. Galaxy-ML extends Galaxy (https://galaxyproject.org), a biomedical computational workbench used by tens of thousands of scientists across the world, with a suite of tools for all aspects of supervised machine learning.

Details

Language :
English
ISSN :
15537358
Volume :
17
Issue :
6
Database :
OpenAIRE
Journal :
PLoS Computational Biology
Accession number :
edsair.doi.dedup.....3ac864b1013bcac02022f608822f2717