发表机构
Università degli Studi di Milano; Università della Svizzera italiana (USI); Politecnico di Milano; Aalto University; FGV（米兰大学; 瑞士意大利语大学; 米兰理工大学; 阿尔托大学; 杰图利奥·瓦加斯基金会）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
本文研究双曲图表示学习在生物医学知识图谱中用于孟德尔疾病鉴别诊断，证明其在低维下优于欧几里得基线，并能利用层次结构支持诊断推理。
AI 中文摘要
生物医学知识图谱将本体论派生的层次结构与表型、疾病、基因、蛋白质和患者等异质实体之间的横向关联相结合。这种混合结构引发了一个问题：自然捕捉树状组织的双曲嵌入，在纯层次图之外是否仍然有用。我们提出了一项关于在患者整合的生物医学图上进行孟德尔疾病鉴别诊断的双曲图表示学习的初步研究。在孤立的本体子图上的实验表明，双曲模型在比欧几里得基线低得多的维度下实现了强劲的性能。然后，我们在一个链接预测任务上评估模型，该任务为每位患者对候选疾病进行排序。结果表明，双曲嵌入可以利用生物医学层次结构，同时支持在异质患者级图上的诊断推理。
英文摘要
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.
