DOME: Recommendations for supervised machine learning validation in biology
This addresses the problem of inconsistent validation practices for researchers in biology, though it is incremental as it builds on existing calls for better standards.
The paper tackles the lack of standardization in validating supervised machine learning methods in biology by proposing DOME, a set of community-wide recommendations to improve scrutiny and understanding of performance and limitations.
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.