A lightweight single-view contrastive learning hypergraph neural network for food-microbe-disease association prediction.

ABSTRACT:

Background

Identifying potential associations among food, gut microbiota and disease is fundamental for elucidating interaction mechanisms and advancing personalized healthy dietary strategies. While computational methods have been extensively applied to predict microbiota-disease associations, methods on predicting food-microbiota relationships remain limited, particularly regarding higher-order food-microbiota-disease interactions.

Results

In this work, we construct a food-microbe-disease (FMD) database encompassing 190 food items, 219 gut microbiota species, and 163 disease entities, resulting in 17,065 FMD associations. We then propose a lightweight single-view contrastive learning hypergraph neural network (LSCHNN) for FMD association prediction on the sparse FMD datase

SUBMITTER: Hu J 

PROVIDER: S-EPMC12584493 | biostudies-literature | 2025 Nov

REPOSITORIES: biostudies-literature