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

backgroundIdentifying 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.

resultsIn 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 dataset. LSCHNN formulates ternary FMD interactions as a hypergraph, in which foods, microbes, and diseases are represented by nodes and FMD triplets are represented by hyperedges, and leverages the biological features of foods, microbes, and diseases as node attributes. Subsequently, a hypergraph neural network is designed to learn the embeddings of foods, microbes, and diseases from the hypergraph and predict potential ternary FMD associations. Additionally, we incorporate a single-view contrastive learning mechanism that enhances the model's ability to extract discriminative features and improves generalization on sparse data. Comprehensive comparison experiments demonstrate that LSCHNN outperforms other state-of-the-art methods in terms of the precision of predicting ternary FMD associations and discovering more potential FMD associations. Case studies on two microbes further confirm the effectiveness of LSCHNN in identifying potential FMD associations.

conclusionsA novel computational model, LSCHNN, is proposed, marking the first integration of hypergraph neural networks with lightweight single-view contrastive learning for ternary FMD association prediction, providing a groundbreaking framework for precision nutrition and personalized dietary interventions.