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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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
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author Palmblad, Magnus
Böcker, Sebastian
Degroeve, Sven
Kohlbacher, Oliver
Käll, Lukas
Noble, William Stafford
Wilhelm, Mathias
author_facet Palmblad, Magnus
Böcker, Sebastian
Degroeve, Sven
Kohlbacher, Oliver
Käll, Lukas
Noble, William Stafford
Wilhelm, Mathias
author_sort Palmblad, Magnus
collection PubMed
description [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 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.
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spelling pubmed-89813112022-04-06 Interpretation of the DOME Recommendations for Machine Learning in Proteomics and Metabolomics Palmblad, Magnus Böcker, Sebastian Degroeve, Sven Kohlbacher, Oliver Käll, Lukas Noble, William Stafford Wilhelm, Mathias J Proteome Res [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 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. American Chemical Society 2022-02-04 2022-04-01 /pmc/articles/PMC8981311/ /pubmed/35119864 http://dx.doi.org/10.1021/acs.jproteome.1c00900 Text en © 2022 The Authors. Published by American Chemical Society https://creativecommons.org/licenses/by/4.0/Permits the broadest form of re-use including for commercial purposes, provided that author attribution and integrity are maintained (https://creativecommons.org/licenses/by/4.0/).
spellingShingle Palmblad, Magnus
Böcker, Sebastian
Degroeve, Sven
Kohlbacher, Oliver
Käll, Lukas
Noble, William Stafford
Wilhelm, Mathias
Interpretation of the DOME Recommendations for Machine Learning in Proteomics and Metabolomics
title Interpretation of the DOME Recommendations for Machine Learning in Proteomics and Metabolomics
title_full Interpretation of the DOME Recommendations for Machine Learning in Proteomics and Metabolomics
title_fullStr Interpretation of the DOME Recommendations for Machine Learning in Proteomics and Metabolomics
title_full_unstemmed Interpretation of the DOME Recommendations for Machine Learning in Proteomics and Metabolomics
title_short Interpretation of the DOME Recommendations for Machine Learning in Proteomics and Metabolomics
title_sort interpretation of the dome recommendations for machine learning in proteomics and metabolomics
url 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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