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 supervised machine learning validation in biology. Adopting a structured methods description for machine learning based on data, optimization, model, evaluation (DOME) will aim to help both reviewers and readers to better understand and assess the performance and limitations of a method or outcome. The recommendations are formulated as questions to anyone wishing to pursue implementation of a machine learning algorithm. Answers to these questions can be easily included in the supplementary material of published papers.
@article{arxiv.2006.16189,
title = {DOME: Recommendations for supervised machine learning validation in biology},
author = {Ian Walsh and Dmytro Fishman and Dario Garcia-Gasulla and Tiina Titma and Gianluca Pollastri and The ELIXIR Machine Learning focus group and Jen Harrow and Fotis E. Psomopoulos and Silvio C. E. Tosatto},
journal= {arXiv preprint arXiv:2006.16189},
year = {2021}
}