DOME: recommendations for supervised machine learning validation in biology

DOME is a set of community-wide recommendations for reporting supervised machine learning–based analyses applied to biological studies. Broad adoption of these recommendations will help improve machine learning assessment and reproducibility.

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Bibliographische Detailangaben
Veröffentlicht in:	Nature methods 2021-10, Vol.18 (10), p.1122-1127
Hauptverfasser:	Walsh, Ian, Fishman, Dmytro, Garcia-Gasulla, Dario, Titma, Tiina, Pollastri, Gianluca, Harrow, Jennifer, Psomopoulos, Fotis E., Tosatto, Silvio C. E.
Format:	Artikel
Sprache:	eng
Schlagworte:	631/114/1305 631/114/2416 631/114/794 631/1647/794 Algorithms Bioinformatics Biological Microscopy Biological research Biological Techniques Biology, Experimental Biomedical and Life Sciences Biomedical Engineering/Biotechnology Comment Computational Biology - methods Computational Biology - standards Domes Guidelines as Topic Humans Learning algorithms Life Sciences Machine learning Methods Models, Biological Proteomics Research Design Supervised Machine Learning Technology application
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Beschreibung
Zusammenfassung:	DOME is a set of community-wide recommendations for reporting supervised machine learning–based analyses applied to biological studies. Broad adoption of these recommendations will help improve machine learning assessment and reproducibility.
ISSN:	1548-7091 1548-7105
DOI:	10.1038/s41592-021-01205-4