Practical Guidance for Training Machine Learning Models in Metabolomics and Mass Spectrometry Research
Zhao Chen, Tingting Zhao, Qiming Shen, Zhifeng Tang, Xiaoxiao Li, Tao Huan
Abstract
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.
Topics & Concepts
Machine learningFeature (linguistics)Artificial intelligenceChemistryMetabolomicsTraining setTraining (meteorology)Code (set theory)Computer scienceChemometricsMass spectrometryData miningExperimental dataMetabolomics and Mass Spectrometry StudiesComputational Drug Discovery MethodsAdvanced Proteomics Techniques and Applications