DMoVGPE: predicting gut microbial associated metabolites profiles with deep mixture of variational Gaussian Process experts.

ABSTRACT:

Background

Understanding the metabolic activities of the gut microbiome is vital for deciphering its impact on human health. While direct measurement of these metabolites through metabolomics is effective, it is often expensive and time-consuming. In contrast, microbial composition data obtained through sequencing is more accessible, making it a promising resource for predicting metabolite profiles. However, current computational models frequently face challenges related to limited prediction accuracy, generalizability, and interpretability.

Method

Here, we present the Deep Mixture of Variational Gaussian Process Experts (DMoVGPE) model, designed to overcome these issues. DMoVGPE utilizes a dynamic gating mechanism, implemented through a neural network with fully connected l

SUBMITTER: Weng Q 

PROVIDER: S-EPMC11951675 | biostudies-literature | 2025 Mar

REPOSITORIES: biostudies-literature