4.8 Editorial Material

DOME: recommendations for supervised machine learning validation in biology

期刊

NATURE METHODS
卷 18, 期 10, 页码 1122-1127

出版社

NATURE PORTFOLIO
DOI: 10.1038/s41592-021-01205-4

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资金

  1. ELIXIR
  2. Agency for Science, Technology and Research (A*STAR), Singapore [C210112057]
  3. Estonian Research Council [PRG1095, PSG59]
  4. Estonian Research Council (ERA-NET TRANSCAN-2 (BioEndoCar))
  5. ELIXIR [2014-2020.4.01.16-0271]
  6. European Regional Development Fund through EXCITE Center of Excellence
  7. European Union's Horizon 2020 research and innovation programme under Marie Skodowska-Curie Grant [778247, 823886]
  8. Italian Ministry of University and Research PRIN 2017 grant [2017483NH8]

向作者/读者索取更多资源

DOME is a set of community-wide recommendations for reporting supervised machine learning-based analyses applied to biological studies, with the goal of improving machine learning assessment and reproducibility.
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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