| Title | DOME: recommendations for supervised machine learning validation in biology. |
| Publication Type | Journal Article |
| Year of Publication | 2021 |
| Authors | Walsh, I, Fishman, D, Garcia-Gasulla, D, Titma, T, Pollastri, G, Harrow, J, Psomopoulos, FE, Tosatto, SCE |
| Corporate Authors | ELIXIR Machine Learning Focus Group |
| Journal | Nat Methods |
| Volume | 18 |
| Issue | 10 |
| Pagination | 1122-1127 |
| Date Published | 2021 10 |
| ISSN | 1548-7105 |
| Keywords | Algorithms; Computational Biology; Guidelines as Topic; Humans; Models, Biological; Research Design; Supervised Machine Learning |
| DOI | 10.1038/s41592-021-01205-4 |
| Alternate Journal | Nat Methods |
| PubMed ID | 34316068 |
| Grant List | 778247 / / EC | EU Framework Programme for Research and Innovation H2020 | H2020 Priority Excellent Science | H2020 Marie Skłodowska-Curie Actions (H2020 Excellent Science - Marie Skłodowska-Curie Actions) / 823886 / / EC | EU Framework Programme for Research and Innovation H2020 | H2020 Priority Excellent Science | H2020 Marie Skłodowska-Curie Actions (H2020 Excellent Science - Marie Skłodowska-Curie Actions) / 2017483NH8 / / Ministero dell'Istruzione, dell'Università e della Ricerca (Ministry of Education, University and Research) / PRG1095 / / Eesti Teadusagentuur (Estonian Research Council) / PSG59 / / Eesti Teadusagentuur (Estonian Research Council) / |
