标注和索引与罕见疾病有关的科学文章
Hosein Azarbonyad1, Zubair Afzal2, Rik Iping3
1Elsevier B.V., Amsterdam, Noord Holland, The Netherlands. h.azarbonyad@elsevier.com.
Journal of biomedical semantics
|January 6, 2026
概括
一个新的框架使用OrphaNet分类系统高效地对罕见疾病的科学文献进行注释. 该系统通过实现可扩展的监测和发现来改善罕见疾病研究.
科学领域:
- 生物医学信息学 生物医学信息学
- 计算生物学 计算生物学
- 罕见疾病研究 罕见疾病研究
背景情况:
- 大约有3000万欧洲人患有罕见 (孤儿) 疾病,每2000人中影响不到1人.
- 确定针对特定罕见疾病的科学文献和指导方针是一个重大挑战.
- 现有的方法受到有限的注释数据和罕见疾病名称表示的变化所阻碍.
研究的目的:
- 从OrphaNet分类学中开发一种用于注释和索引罕见疾病概念的科学文本的新方法.
- 为了应对罕见病文献中数据稀缺性和词汇变异性的挑战.
- 为了实现可扩展的,罕见疾病研究的自动识别.
主要方法:
- 开发了一个框架,将TERMite引擎与Orpha.Net集成在一起.
- 关键组件包括策划的同义词扩展,标签规范化 (处理过时/重新命名的概念) 和模糊匹配.
- 该管道被应用于Scopus,以创建特定疾病的生物体,用于圣经计量和科学计量分析.
主要成果:
- 该方法在基准数据集上实现了92%的精度,75%的回忆率和83%的F1得分,超过了字符串匹配基线.
- 该系统生成适合分析研究活动的特定疾病体 (例如,按机构,国家,学科).
- 输出为罕见疾病监测仪表板提供动力,用于探索研究趋势.
结论:
- 本书介绍了第一个系统的,可扩展的语义框架,用于注释和索引罕见疾病文献.
- 自动化,可重复的管道推进了罕见疾病的生物医学语义.
- 该框架可以在研究领域内进行以疾病为中心的监测,评估和发现.
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