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

Journal: PLoS computational biology
Published Date: Jun 1, 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.

Authors

  • Qiang Gu
    Department of Biomedical Engineering, Oregon Health & Science University, Portland, Oregon, United States of America.
  • Anup Kumar
    Department of Urology, University of Central Florida College of Medicine and Global Robotics Institute, Florida Hospital-Celebration Health, Celebration, FL, USA.
  • Simon Bray
    Bioinformatics Group, Department of Computer Science, University of Freiburg, Freiburg, Germany.
  • Allison Creason
    Department of Biomedical Engineering, Oregon Health & Science University, Portland, Oregon, United States of America.
  • Alireza Khanteymoori
    Bioinformatics Group, Department of Computer Science, University of Freiburg, Freiburg, Germany.
  • Vahid Jalili
    Department of Biomedical Engineering, Oregon Health & Science University, Portland, OR, USA.
  • Björn Grüning
    Bioinformatics Group, Department of Computer Science, University of Freiburg, Georges-Koehler-Allee 106, 79110 Freiburg, Germany.
  • Jeremy Goecks
    Department of Biomedical Engineering, Oregon Health & Science University, Portland, OR, USA. Electronic address: goecksj@ohsu.edu.

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