  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.
