Reproducibility standards for machine learning in the life sciences

Nat Methods. 2021 Oct;18(10):1132-1135. doi: 10.1038/s41592-021-01256-7.

Abstract

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.

Publication types

  • Research Support, N.I.H., Extramural
  • Research Support, Non-U.S. Gov't

MeSH terms

  • Computational Biology / methods*
  • Computational Biology / standards*
  • Machine Learning / standards*
  • Reproducibility of Results
  • Software