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

TL;DR

This paper introduces DMoVGPE, a new model that predicts gut microbiome metabolite profiles using sequencing data, improving accuracy and interpretability.

Contribution

DMoVGPE introduces a dynamic gating mechanism and ARD for better prediction and interpretability of gut microbial metabolites.

Findings

DMoVGPE outperforms existing models in predicting metabolite profiles from microbial data.

The model identifies meaningful associations between microbial taxa and metabolites.

ARD mechanism improves feature relevance and model interpretability.

Abstract

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.

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 layers and dropout for regularization, to select the most relevant Gaussian Process experts. During training, the gating network refines expert selection, dynamically adjusting their contribution based on the input features. The model also incorporates an Automatic Relevance Determination (ARD) mechanism, which assigns relevance scores to microbial features by evaluating their predictive power. Features linked to metabolite profiles are given smaller length scales to increase their influence, while irrelevant features are down-weighted through larger length scales, improving both prediction accuracy and interpretability.

Through extensive evaluations on various datasets, DMoVGPE consistently achieves higher prediction performance than existing models. Furthermore, our model reveals significant associations between specific microbial taxa and metabolites, aligning well with findings from existing studies. These results highlight DMoVGPE’s potential to provide accurate predictions and to uncover biologically meaningful relationships, paving the way for its application in disease research and personalized healthcare strategies.

The online version contains supplementary material available at 10.1186/s12859-025-06110-7.

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