• Corpus ID: 220250505

Recommendations for machine learning validation in biology

@article{Walsh2020RecommendationsFM,
  title={Recommendations for machine learning validation in biology},
  author={Ian Walsh and Dmytro Fishman and Dario Garc{\'i}a-Gasulla and Tiina Titma and Jennifer L. Harrow and Fotis Psomopoulos and Silvio C. E. Tosatto},
  journal={ArXiv},
  year={2020},
  volume={abs/2006.16189}
}
Modern biology frequently relies on machine learning to provide predictions and improve decision processes. There have been recent calls for more scrutiny on machine learning performance and possible limitations. Here we present a set of community-wide recommendations aiming to help establish standards of machine learning validation in biology. Adopting a structured methods description for machine learning based on DOME (data, optimization, model, evaluation) will allow both reviewers and… 

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