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English(EN) Graph Neural Network based Hierarchy-Aware Embeddings of Knowledge Graphs: Applications to Yeast Phenotype Prediction

GNNs 创建层级感知的知识图谱嵌入用于酵母表型预测

研究人员开发了一种新颖的方法,使用图神经网络(GNNs)为知识图谱创建层级感知的嵌入。该方法结合了源自本体的语义损失,以更好地表示领域知识。该方法应用于预测酵母表型,在双基因敲除实验中取得了 0.360 的平均 R^2 分数,优于基线模型。结合语义损失进一步将预测性能提高到 R^2=0.377,证明了本体结构在定量预测中的价值,并可能指导生物学发现。 AI

影响 通过改进知识图谱和本体的定量预测来增强生物学发现。

排序理由 学术论文,详细介绍了一种新的知识图谱嵌入方法及其应用。

在 arXiv cs.AI 阅读 →

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GNNs 创建层级感知的知识图谱嵌入用于酵母表型预测

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学术论文,详细介绍了一种新的知识图谱嵌入方法及其应用。
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报道来源 [2]

  1. arXiv cs.LG TIER_1 English(EN) · Filip Kronstr\"om, Alexander H. Gower, Daniel Brunns{\aa}ker, Ievgeniia A. Tiukova, Ross D. King ·

    基于图神经网络的层次感知知识图谱嵌入及其在酵母表型预测中的应用

    arXiv:2605.03690v1 Announce Type: new Abstract: We present a method for finding hierarchy-aware embeddings of knowledge graphs (KGs) using graph neural networks (GNNs) enriched with a semantic loss derived from underlying ontologies. This method yields embeddings that better refl…

  2. arXiv cs.AI TIER_1 English(EN) · Ross D. King ·

    基于图神经网络的层次感知知识图谱嵌入及其在酵母表型预测中的应用

    We present a method for finding hierarchy-aware embeddings of knowledge graphs (KGs) using graph neural networks (GNNs) enriched with a semantic loss derived from underlying ontologies. This method yields embeddings that better reflect domain knowledge. To demonstrate their utili…