Improved Metabolite Prediction Using Microbiome Data-Based Elastic Net Models.

ABSTRACT: Microbiome data are becoming increasingly available in large health cohorts, yet metabolomics data are still scant. While many studies generate microbiome data, they lack matched metabolomics data or have considerable missing proportions of metabolites. Since metabolomics is key to understanding microbial and general biological activities, the possibility of imputing individual metabolites or inferring metabolomics pathways from microbial taxonomy or metagenomics is intriguing. Importantly, current metabolomics profiling methods such as the HMP Unified Metabolic Analysis Network (HUMAnN) have unknown accuracy and are limited in their ability to predict individual metabolites. To address this gap, we developed a novel metabolite prediction method, and we present its application and evaluati

SUBMITTER: Xie J 

PROVIDER: S-EPMC8573316 | biostudies-literature | 2021

REPOSITORIES: biostudies-literature