Reproducibility standards for machine learning in the life sciences

• DOI: 10.1038/s41592-021-01256-7
• Publication Date: 2021-08-30T16:03:07Z
• Authors: Benjamin J. Heil; Michael M. Hoffman; Florian Markowetz; Su-In Lee; Casey S. Greene; Stephanie C. Hicks
• Keywords: Machine Learning; 0301 basic medicine; 03 medical and health sciences; Computational Biology; Reproducibility of Results; Software

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.