Reproducibility standards for machine learning in the life sciences
| Autor: | Benjamin J. Heil, Michael M. Hoffman, Casey S. Greene, Florian Markowetz, Su-In Lee, Stephanie C. Hicks |
|---|---|
| Jazyk: | angličtina |
| Rok vydání: | 2021 |
| Předmět: |
0303 health sciences
Computer science business.industry Best practice MEDLINE Computational Biology Reproducibility of Results Cell Biology Biochemistry Article Machine Learning 03 medical and health sciences 0302 clinical medicine Workflow Code (cryptography) Software engineering business Molecular Biology 030217 neurology & neurosurgery Software 030304 developmental biology Biotechnology |
| Zdroj: | Nat Methods |
| Popis: | 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. |
| Databáze: | OpenAIRE |
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