Longitudinal Metabolomics Data Analysis Informed by Mechanistic Models.

ABSTRACT: Background: Metabolomics measurements are noisy, often characterized by a small sample size and missing entries. While data-driven methods have shown promise in terms of analyzing metabolomics data, e.g., revealing biomarkers of various phenotypes, metabolomics data analysis can significantly benefit from incorporating prior information about metabolic mechanisms. This paper introduces a novel data analysis approach to incorporate mechanistic models in metabolomics data analysis. Methods: We arranged time-resolved metabolomics measurements of plasma samples collected during a meal challenge test from the COPSAC₂₀₀₀ cohort as a third-order tensor: subjects by metabolites by time samples. Simulated challenge test data generated using a human whol

SUBMITTER: Li L 

PROVIDER: S-EPMC11766892 | biostudies-literature | 2024 Dec

REPOSITORIES: biostudies-literature