Hyperbolic Graph Representation Learning for Differential Diagnosis on Biomedical Knowledge Graphs

2026-09-16

AI Summary

研究探讨了在生物医学知识图谱中使用双曲图表示学习进行孟德尔疾病鉴别诊断，实验表明该方法能在较低维度上有效利用层次结构并支持异质患者级图的诊断推理。

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.

Problem

• Hyperbolic Embeddings
• Biomedical Knowledge Graphs
• Differential Diagnosis
• Mendelian Diseases
• Graph Representation Learning

Innovation

• Hyperbolic Embeddings
• Differential Diagnosis
• Biomedical Knowledge Graphs
• Mendelian Diseases