Longitudinal Metabolomics Data Analysis Informed by Mechanistic Models

Longitudinal Metabolomics Data Analysis Informed by Mechanistic Models

<b>Background</b>: 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 s...

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Bibliographic Details
Main Authors: Lu Li, Huub Hoefsloot, Barbara M. Bakker, David Horner, Morten A. Rasmussen, Age K. Smilde, Evrim Acar
Format: Article
Language:English
Published: MDPI AG 2024-12-01
Series:Metabolites
Subjects:
challenge tests
metabolic model
(coupled) tensor factorizations
longitudinal metabolomics data
knowledge-guided machine learning
Online Access:https://www.mdpi.com/2218-1989/15/1/2
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