MAMSI: Integration of Multiassay Liquid Chromatography-Mass Spectrometry Metabolomics Data Using Multiview Machine Learning.

ABSTRACT: Liquid chromatography-mass spectrometry (LC-MS) is a commonly used analytical technique in untargeted metabolomics. However, the diverse chemical and physical properties of metabolites often require the use of several different analytical assays for broad metabolome coverage. Conventionally, each assay is analyzed separately, but this fails to capture interassay relationships, making multiassay biomarker discovery and data interpretation difficult. Here we propose a workflow to integrate multiassay metabolomics data, designed to enable biomarker discovery and elucidation of unknown metabolites. We employ a multiblock-partial least-squares model (MB-PLS) coupled with multiblock variable importance in projection to estimate the importance of predictors to the outcome variable. Then we cluste

SUBMITTER: Kopecky L 

PROVIDER: S-EPMC12291033 | biostudies-literature | 2025 Jul

REPOSITORIES: biostudies-literature