Interpretation of the DOME Recommendations for Machine Learning in Proteomics and Metabolomics

Machine learning is increasingly applied in proteomics and metabolomics to predict molecular structure, function, and physicochemical properties, including behavior in chromatography, ion mobility, and tandem mass spectrometry. These must be described in sufficient detail to apply or evaluate the performance of trained models. Here we look at and interpret the recently published and general DOME (Data, Optimization, Model, Evaluation) recommendations for conducting and reporting on machine learning in the specific context of proteomics and metabolomics.

Automated summary

This article discusses the specific guidelines for conducting and reporting machine learning in proteomics and metabolomics, emphasizing the importance of describing molecular structure, function, and physicochemical properties.