What is DOME-ML?

DOME-ML (or simply DOME) is an acronym standing for Data, Optimization, Model and Evaluation in Machine Learning. DOME is a set of community-wide guidelines, recommendations and checklists spanning these four areas aiming to help establish standards of supervised machine learning validation in biology. 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.

What is the scope of the recommendations?

The recommendations cover four separate aspects covering the major areas of ML:

Data

Preprocessing data properly, and using it in a knowledgeable manner is the only way to obtain good generalization


Optimization

Problems associated with a poor choice of optimization strategy.


Model

Fallacies of important aspects of the ML models (black-box models where interpretability is required, dissemination level of the model components, computational requirements to execute trained models)


Evaluation

Valid assessment methodology for any final model with adequate and comprehensive measures.