Interpretation of the DOME Recommendations for Machine Learning in Proteomics and Metabolomics.
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Palmblad M, Böcker S, Degroeve S, Kohlbacher O, Käll L, Noble WS, Wilhelm M
J. Proteome Res. 21 (4) 1204-1207 [2022-04-01; online 2022-02-04]
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.
PubMed 35119864
DOI 10.1021/acs.jproteome.1c00900
Crossref 10.1021/acs.jproteome.1c00900