Year: 2021
Type: article
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.
Source: Nature Methods
Authors: Ian Walsh, Dmytro Fishman, Dario Garcia-Gasulla, Tiina Titma, Gianluca Pollastri, ELIXIR Machine Learning Focus Group, Emidio Capriotti, Rita Casadio, Salvador Capella-Gutierrez, Davide Cirillo, Alessio Del Conte, Alexandros C. Dimopoulos, Victoria Dominguez Del Angel, Joaquin Dopazo, Piero Fariselli, José Maria Fernández, Florian Huber, Anna Kreshuk, Tom Lenaerts, Pier Luigi Martelli, Arcadi Navarro, Pilib Ó Broin, Janet Piñero, Damiano Piovesan, Martin Reczko, Francesco Ronzano, Venkata Satagopam, Castrense Savojardo, Vojtech Spiwok, Marco Antonio Tangaro, Giacomo Tartari, David Salgado, Alfonso Valencia, Federico Zambelli, Jennifer Harrow, Fotis E. Psomopoulos, Silvio C. E. Tosatto
Institutions: Agency for Science, Technology and Research, Bioprocessing Technology Institute, University of Tartu, Barcelona Supercomputing Center, Tallinn University of Technology, University College Dublin, ELIXIR, Centre for Research and Technology Hellas, University of Bologna, University of Padua, Institute of Biomembranes, Bioenergetics and Molecular Biotechnologies, National Research Council, Alexander Fleming Biomedical Sciences Research Center, Centre National de la Recherche Scientifique, Université Paris-Saclay, Fundación Progreso y Salud, University of Turin, Netherlands eScience Center, European Molecular Biology Laboratory, European Molecular Biology Laboratory, Université Libre de Bruxelles, Vrije Universiteit Brussel, Institució Catalana de Recerca i Estudis Avançats, Universitat Pompeu Fabra, Barcelona Institute of Science and Technology, Centre for Genomic Regulation, Ollscoil na Gaillimhe – University of Galway, Hospital Del Mar, Municipal Institute for Medical Research, Hospital del Mar Research Institute, University of Luxembourg, Luxembourg Centre for Systems Biomedicine, University of Chemistry and Technology, Prague, Inserm, Aix-Marseille Université, University of Milan
Language: English
Subfield: Information Systems and Management
Field: Decision Sciences
Domain: Social Sciences
Open Access status: hybrid
Estimated APC paid: $11,390
Awards: Computational analysis and learning from biological data (OmicNet), G1888088267, G1953887145, G2599072453, G2697670400, G2975900969, G3078054322, G434337789, G4898532384, G4920617668, G5422581293, Algorithmic and Artificial Intelligence Approaches for Digital Health, Driving the functional characterization of intrinsically disordered proteins, G8681696325, Repeat protein Function Refinement, Annotation and Classification of Topologies
OpenAlex ID: W3090528923