Predicting human health from biofluid-based metabolomics using machine learning.

ABSTRACT: Biofluid-based metabolomics has the potential to provide highly accurate, minimally invasive diagnostics. Metabolomics studies using mass spectrometry typically reduce the high-dimensional data to only a small number of statistically significant features, that are often chemically identified-where each feature corresponds to a mass-to-charge ratio, retention time, and intensity. This practice may remove a substantial amount of predictive signal. To test the utility of the complete feature set, we train machine learning models for health state-prediction in 35 human metabolomics studies, representing 148 individual data sets. Models trained with all features outperform those using only significant features and frequently provide high predictive performance across nine health state categorie

SUBMITTER: Evans ED 

PROVIDER: S-EPMC7572502 | biostudies-literature | 2020 Oct

REPOSITORIES: biostudies-literature