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English(EN) Hyperbolic Graph Representation Learning for Differential Diagnosis on Biomedical Knowledge Graphs

双曲嵌入在生物医学鉴别诊断方面展现出潜力

研究人员探索了在生物医学知识图谱中将双曲图表示学习用于鉴别诊断。该方法旨在利用双曲嵌入捕获的自然树状结构,以处理超越纯粹分层数据的任务。在本体子图上的初步实验表明,与欧氏基线相比,双曲模型在较低维度下可以获得强劲的性能。对患者集成图的链接预测任务的进一步评估表明,双曲嵌入可以有效地利用生物医学分层结构来辅助诊断推理。 AI

影响 这项研究通过更好地利用复杂的生物医学数据结构,有望带来更准确、更高效的诊断工具。

排序理由 该集群包含一篇发表在arXiv上的研究论文,详细介绍了一种新的生物医学知识图谱表示学习方法。[lever_c_demoted from research: ic=1 ai=1.0]

在 arXiv cs.AI 阅读 →

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双曲嵌入在生物医学鉴别诊断方面展现出潜力

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该集群包含一篇发表在arXiv上的研究论文,详细介绍了一种新的生物医学知识图谱表示学习方法。[lever_c_demoted from research: ic=1 ai=1.0]
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报道来源 [1]

  1. arXiv cs.AI TIER_1 English(EN) · Pietro Miotto, Lucia Mellini, Tommaso Marzi, Cesare Alippi, Elena Casiraghi, Alberto Paccanaro, Giorgio Valentini, Mauricio Soto-Gomez ·

    用于生物医学知识图谱鉴别诊断的双曲图表示学习

    arXiv:2609.18481v1 Announce Type: new 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 wh…