Interpretation of the DOME Recommendations for Machine Learning in Proteomics and Metabolomics.
J Proteome Res
; 21(4): 1204-1207, 2022 04 01.
Article
en En
| MEDLINE
| ID: mdl-35119864
ABSTRACT
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.
Texto completo:
1
Banco de datos:
MEDLINE
Asunto principal:
Proteómica
/
Metabolómica
Tipo de estudio:
Prognostic_studies
Idioma:
En
Año:
2022
Tipo del documento:
Article