Machine Learning-Based Retention Time Prediction of Trimethylsilyl Derivatives of Metabolites.

ABSTRACT: In gas chromatography-mass spectrometry-based untargeted metabolomics, metabolites are identified by comparing mass spectra and chromatographic retention time with reference databases or standard materials. In that sense, machine learning has been used to predict the retention time of metabolites lacking reference data. However, the retention time prediction of trimethylsilyl derivatives of metabolites, typically analyzed in untargeted metabolomics using gas chromatography, has been poorly explored. Here, we provide a rationalized framework for machine learning-based retention time prediction of trimethylsilyl derivatives of metabolites in gas chromatography. We compared different machine learning paradigms, in addition to exploring the influence of the computational molecular structure re

SUBMITTER: de Cripan SM 

PROVIDER: S-EPMC9024754 | biostudies-literature | 2022 Apr

REPOSITORIES: biostudies-literature