从文献中提取阿尔帕通尿症知识，构建疾病关联图谱。
Knowledge Graph Extraction from Biomedical Literature for Alkaptonuria Rare Disease
- 用PubTator3文本挖掘法大规模抽取生物医学关系
- 构建两个规模不同的知识图谱并验证其准确性
- 发现潜在致病基因、共病和治疗靶点，适合罕见病研究者
阿尔帕通尿症（AKU）是一种由HGD基因突变引起的超罕见常染色体隐性代谢病，导致体内同型尿酸（HGA）积累，引发早发性脊柱关节病、肾及前列腺结石、心血管并发症等系统性表现。由于疾病极为罕见，相关临床数据和文献数量有限，现有生物医学知识图谱中常缺少或未包含AKU信息。本文基于PubTator3方法，实现大规模文献关系抽取，构建了两个不同规模的知识图谱，经已有生化知识验证后，用于识别与AKU相关的基因、疾病及疗法。该计算框架揭示了该病的系统性交互机制、共病关系及潜在治疗靶点，验证了其在分析罕见代谢病中的有效性。
原文摘要 · Abstract (English)
Alkaptonuria (AKU) is an ultra-rare autosomal recessive metabolic disorder caused by mutations in the HGD (Homogentisate 1,2-Dioxygenase) gene, leading to a pathological accumulation of homogentisic acid (HGA) in body fluids and tissues. This leads to systemic manifestations, including premature spondyloarthropathy, renal and prostatic stones, and cardiovascular complications. Being ultra-rare, the amount of data related to the disease is limited, both in terms of clinical data and literature. Knowledge graphs (KGs) can help connect the limited knowledge about the disease (basic mechanisms, manifestations and existing therapies) with other knowledge; however, AKU is frequently underrepresented or entirely absent in existing biomedical KGs. In this work, we apply a text-mining methodology based on PubTator3 for large-scale extraction of biomedical relations. We construct two KGs of different sizes, validate them using existing biochemical knowledge and use them to extract genes, diseases and therapies possibly related to AKU. This computational framework reveals the systemic interactions of the disease, its comorbidities, and potential therapeutic targets, demonstrating the efficacy of our approach in analyzing rare metabolic disorders.
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