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

被引：8

作者：

Palmblad, Magnus ^{[9
]}

Boecker, Sebastian ^{[1
]}

Degroeve, Sven ^{[2
,3
]}

Kohlbacher, Oliver ^{[4
]}

Kall, Lukas ^{[5
]}

Noble, William Stafford ^{[6
,7
]}

Wilhelm, Mathias ^{[8
]}

机构：

[1] Friedrich Schiller Univ, Fac Math & Comp Sci, D-07743 Jena, Germany

[2] VIB, VIB UGent Ctr Med Biotechnol, Ghent, Belgium

[3] Univ Ghent, Dept Biomol Med, B-9052 Ghent, Belgium

[4] Eberhard Karls Univ Tubingen, WSI ZBIT, D-72076 Tubingen, Germany

[5] Royal Inst Technol KTH, Sch Engn Sci Chem Biotechnol & Hlth, Sci Life Lab, S-17121 Solna, Sweden

[6] Univ Washington, Dept Genome Sci, Seattle, WA 98195 USA

[7] Univ Washington, Paul G Allen Sch Comp Sci & Engn, Seattle, WA 98195 USA

[8] Tech Univ Munich TUM, Computat Mass Spectrometry, D-85354 Freising Weihenstephan, Germany

[9] Leiden Univ, Ctr Prote & Metabol, Med Ctr, NL-2300 RC Leiden, Netherlands

来源：

JOURNAL OF PROTEOME RESEARCH | 2022年 / 21卷 / 04期

关键词：

D O I：

10.1021/acs.jproteome.1c00900

中图分类号：

Q5 [生物化学];

学科分类号：

071010 ; 081704 ;

摘要：

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.

引用

页码：1204 / 1207

页数：4

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