用基因富集分析挖掘知识图谱规则，发现阿尔茨海默病新药候选
Explainable Enrichment-Driven GrAph Reasoner (EDGAR) for Large Knowledge Graphs with Applications in Drug Repurposing
- 基于基因富集分析挖掘实体局部规则，实现可解释的链接预测
- 在ROBOKOP上找出1246个新药候选，前10个获文献验证
- 首次将富集分析用于大规模知识图谱补全，适合药物重定位研究
知识图谱（KG）表示现实世界实体间的连接与关系。本文提出一种名为增强驱动图推理器（EDGAR）的链接预测框架，通过挖掘实体局部规则推断新边。该方法利用基因富集分析——一种用于识别差异表达基因集合中共同机制的成熟统计方法——使推理结果具备可解释性和可排序性，每个基于富集的规则附带显著性p值。我们在大型生物医学知识图谱ROBOKOP上验证了该框架的有效性，以阿尔茨海默病（AD）药物重定位为例。初始阶段，从图谱中提取14种已知药物，并通过富集分析识别出20个上下文生物标志物，揭示与药物疗效共享的功能通路。随后，利用前1000个富集结果，系统筛选出1246个新的AD治疗药物候选。前10名候选药物经医学文献证据验证。EDGAR已部署于ROBOKOP，配备网页用户界面。这是首个将富集分析应用于大规模图谱补全和药物重定位的研究。
原文摘要 · Abstract (English)
Knowledge graphs (KGs) represent connections and relationships between real-world entities. We propose a link prediction framework for KGs named Enrichment-Driven GrAph Reasoner (EDGAR), which infers new edges by mining entity-local rules. This approach leverages enrichment analysis, a well-established statistical method used to identify mechanisms common to sets of differentially expressed genes. EDGAR's inference results are inherently explainable and rankable, with p-values indicating the statistical significance of each enrichment-based rule. We demonstrate the framework's effectiveness on a large-scale biomedical KG, ROBOKOP, focusing on drug repurposing for Alzheimer disease (AD) as a case study. Initially, we extracted 14 known drugs from the KG and identified 20 contextual biomarkers through enrichment analysis, revealing functional pathways relevant to shared drug efficacy for AD. Subsequently, using the top 1000 enrichment results, our system identified 1246 additional drug candidates for AD treatment. The top 10 candidates were validated using evidence from medical literature. EDGAR is deployed within ROBOKOP, complete with a web user interface. This is the first study to apply enrichment analysis to large graph completion and drug repurposing.
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