Reproducibility standards for machine learning in the life sciences,Nature Methods

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Reproducibility standards for machine learning in the life sciences
Nature Methods ( IF 48.0 ) Pub Date : 2021-08-30 , DOI: 10.1038/s41592-021-01256-7
Benjamin J Heil ₁ , Michael M Hoffman _{2,

3,

4,

5} , Florian Markowetz ₆ , Su-In Lee ₇ , Casey S Greene _{8,

9} , Stephanie C Hicks ₁₀

Affiliation

Genomics and Computational Biology Graduate Group, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA.
Princess Margaret Cancer Centre, University Health Network, Toronto, Ontario, Canada.
Department of Medical Biophysics, University of Toronto, Toronto, Ontario, Canada.
Department of Computer Science, University of Toronto, Toronto, Ontario, Canada.
Vector Institute, Toronto, Ontario, Canada.
Cancer Research UK Cambridge Institute, University of Cambridge, Cambridge, UK.
Paul G. Allen School of Computer Science and Engineering, University of Washington, Seattle, WA, USA.
Department of Biochemistry and Molecular Genetics, University of Colorado School of Medicine, Aurora, CO, USA.
Center for Health AI, University of Colorado School of Medicine, Aurora, CO, USA.
Department of Biostatistics, Johns Hopkins Bloomberg School of Public Health, Baltimore, MD, USA.

To make machine-learning analyses in the life sciences more computationally reproducible, we propose standards based on data, model and code publication, programming best practices and workflow automation. By meeting these standards, the community of researchers applying machine-learning methods in the life sciences can ensure that their analyses are worthy of trust.

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