Hyperbolic Graph Representation Learning for Differential Diagnosis on Biomedical Knowledge Graphs

Abstract:Biomedical knowledge graphs combine ontology-derived hierarchies with transversal associations among heterogeneous entities such as phenotypes, diseases, genes, proteins, and patients. This hybrid structure raises the question of whether hyperbolic embeddings, which naturally capture tree-like organization, remain useful beyond purely hierarchical graphs. We present a preliminary study of hyperbolic graph representation learning for Mendelian-disease differential diagnosis on a patient-integrated biomedical graph. Experiments on isolated ontology subgraphs show that hyperbolic models achieve strong performance in substantially lower dimensions than Euclidean baselines. We then evaluate the models on a link-prediction task that ranks candidate diseases for each patient. Results suggest that hyperbolic embeddings can exploit biomedical hierarchical structure while supporting diagnostic reasoning over heterogeneous patient-level graphs.

Artificial Intelligence (cs.AI); Machine Learning (cs.LG)

arXiv:2609.18481 [cs.AI]

(or arXiv:2609.18481v1 [cs.AI] for this version)

https://doi.org/10.48550/arXiv.2609.18481 Focus to learn more arXiv-issued DOI via DataCite (pending registration)

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