医学知识图谱中基于双曲图的疾病鉴别诊断研究
Hyperbolic Graph Representation Learning for Differential Diagnosis on Biomedical Knowledge Graphs
这是篇关于用双曲几何方法处理医学知识图谱的论文，研究如何利用这种结构来辅助疾病诊断，挺有意思的。
本文提出了一种基于双曲图表示学习的方法，用于在医学知识图谱上进行孟德尔遗传病的鉴别诊断。实验表明，在孤立的本体子图上，双曲模型在显著低于欧几里得基线的维度下实现了优异的性能。随后，该方法在链接预测任务上被评估，用于为每位患者排名候选疾病。结果表明，双曲嵌入能够利用生物医学的层级结构，同时支持跨异构患者级图进行诊断推理。
Hyperbolic Graph Representation Learning for Differential Diagnosis on Biomedical Knowledge Graphs
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.