Practical Guidance for Training Machine Learning Models in Metabolomics and Mass Spectrometry Research
This tutorial offers a step-by-step guide for analytical chemists to train machine learning models for MS-based metabolomics. It covers data preparation, feature engineering, model selection, evaluation, and interpretation, along with real-world examples, common pitfalls, and a complete practice dataset with code available.
Authors
- Qiming Shen (ORCID: https://orcid.org/0009-0001-4910-4754)
- Zhifeng Tang (ORCID: https://orcid.org/0000-0001-5591-8175)
- Tao Huan (ORCID: https://orcid.org/0000-0001-6295-2435)
- Zhao Chen (ORCID: https://orcid.org/0000-0002-8817-8270)
- Tingting Zhao (ORCID: https://orcid.org/0000-0001-9376-6715)
- Xiaoxiao Li (ORCID: https://orcid.org/0000-0002-8833-0244)
Institutions
- University of British Columbia (CA)
- University of Alberta (CA)
- Technologies pour la Santé (FR)
- IONICS Mass Spectrometry (Canada) (CA)
- AAC Technologies (China) (CN)
Publication Details
- Journal
- Analytical Chemistry
- Published
- 2025-10-17
- DOI
- https://doi.org/10.1021/acs.analchem.5c01417
- Citations
- 1
- Primary Topic
- Metabolomics and Mass Spectrometry Studies
- Type
- article
- Field-Weighted Citation Impact
- 0.81