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 / I. Walsh, D. Fishman, D. Garcia-Gasulla, T. Titma, G. Pollastri, E. Capriotti, R. Casadio, S. Capella-Gutierrez, D. Cirillo, A. Del Conte, A.C. Dimopoulos, V.D. Del Angel, J. Dopazo, P. Fariselli, J.M. Fernandez, F. Huber, A. Kreshuk, T. Lenaerts, P.L. Martelli, A. Navarro, P.O. Broin, J. Pinero, D. Piovesan, M. Reczko, F. Ronzano, V. Satagopam, C. Savojardo, V. Spiwok, M.A. Tangaro, G. Tartari, D. Salgado, A. Valencia, F. Zambelli, J. Harrow, F.E. Psomopoulos, S.C.E. Tosatto. - In: NATURE METHODS. - ISSN 1548-7091. - (2021). [Epub ahead of print] [10.1038/s41592-021-01205-4]
DOME: recommendations for supervised machine learning validation in biology
F. Zambelli;
2021
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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