Predicting metabolomic profiles from microbial composition through neural ordinary differential equations.

ABSTRACT: Characterizing the metabolic profile of a microbial community is crucial for understanding its biological function and its impact on the host or environment. Metabolomics experiments directly measuring these profiles are difficult and expensive, while sequencing methods quantifying the species composition of microbial communities are well-developed and relatively cost-effective. Computational methods that are capable of predicting metabolomic profiles from microbial compositions can save considerable efforts needed for metabolomic profiling experimentally. Yet, despite existing efforts, we still lack a computational method with high prediction power, general applicability, and great interpretability. Here we develop a method - mNODE (Metabolomic profile predictor using Neural Ordinary Diff

SUBMITTER: Wang T 

PROVIDER: S-EPMC10786629 | biostudies-literature | 2023 Mar

REPOSITORIES: biostudies-literature