Understanding multi-way interactions among biological entities—such as genes, proteins, diseases, pathways, and drugs—is essential for elucidating disease mechanisms and advancing targeted therapeutics. However, traditional graph-based models rely on pairwise relationships and often fail to capture higher-order structural semantics in heterogeneous biomedical networks, leading to information loss.
To address this limitation, this study proposes a novel deep learning framework based on Hypergraph Neural Networks (HGNNs) to model complex, high-order biomedical relationships derived from the large-scale CROssBARv2 knowledge graph. The CROssBARv2 system is a comprehensive resource that integrates heterogeneous biological and biomedical data from multiple databases into a unified knowledge graph, comprising millions of nodes and relationships across diverse entity types such as genes, proteins, drugs, and diseases. The dataset is further enriched with literature-derived associations from PubTator 3.0, improving both coverage and biological relevance. Disease-centric heterogeneous hypergraphs are constructed, where each hyperedge represents a disease and its 1-hop multi-relational neighbours, enabling explicit modelling of multi-entity interactions.
For robust binary classification, an advanced negative sampling strategy is employed at a 1:1 ratio, allowing the model to learn highly discriminative features while avoiding biases introduced by random sampling. The learned node embeddings are subsequently aggregated using an attention-based feature fusion mechanism, which dynamically weights the contribution of each node to estimate interaction probabilities.
The proposed framework enables accurate prediction of gene–disease–pathway–drug associations and facilitates large-scale inference of previously unknown relationships. Overall, it provides a scalable and effective computational pipeline for disease-specific relation prediction and novel drug repurposing applications.

Co-authors: Tunca Dogan