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

[Image: see text] 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...

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Detalles Bibliográficos
Autores principales: Palmblad, Magnus, Böcker, Sebastian, Degroeve, Sven, Kohlbacher, Oliver, Käll, Lukas, Noble, William Stafford, Wilhelm, Mathias
Formato: Online Artículo Texto
Lenguaje:English
Publicado: American Chemical Society 2022
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8981311/
https://www.ncbi.nlm.nih.gov/pubmed/35119864
http://dx.doi.org/10.1021/acs.jproteome.1c00900

Internet

https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8981311/
https://www.ncbi.nlm.nih.gov/pubmed/35119864
http://dx.doi.org/10.1021/acs.jproteome.1c00900

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