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

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

Research output: Contribution to journalArticlepeer-review

2 Scopus citations


Supervised machine learning is an essential but difficult to use approach in biomedical data analysis. The Galaxy-ML toolkit ( 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 (, 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.

Original languageEnglish (US)
Article number1009014
JournalPLoS computational biology
Issue number6
StatePublished - Jun 2021

ASJC Scopus subject areas

  • Ecology, Evolution, Behavior and Systematics
  • Ecology
  • Modeling and Simulation
  • Molecular Biology
  • Genetics
  • Cellular and Molecular Neuroscience
  • Computational Theory and Mathematics


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