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.

DOME: recommendations for supervised machine learning validation in biology

Fariselli P.;
2021-01-01

Abstract

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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Algorithms; Computational Biology; Humans; Models, Biological; Guidelines as Topic; Research Design; Supervised Machine Learning
Walsh I.; Fishman D.; Garcia-Gasulla D.; Titma T.; Pollastri G.; Capriotti E.; Casadio R.; Capella-Gutierrez S.; Cirillo D.; Del Conte A.; Dimopoulos A.C.; Del Angel V.D.; Dopazo J.; Fariselli P.; Fernandez J.M.; Huber F.; Kreshuk A.; Lenaerts T.; Martelli P.L.; Navarro A.; Broin P.O.; Pinero J.; Piovesan D.; Reczko M.; Ronzano F.; Satagopam V.; Savojardo C.; Spiwok V.; Tangaro M.A.; Tartari G.; Salgado D.; Valencia A.; Zambelli F.; Harrow J.; Psomopoulos F.E.; Tosatto S.C.E.
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Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/2318/1821153
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