通过PubMed规模的文本挖掘,将生物背景与蛋白质-蛋白质相互作用联系起来
Daniel N Sosa1, Rogier Hintzen2, Betty Xiong1
1Stanford University, Department of Biomedical Data Science, Stanford, CA, USA.
Journal of biomedical informatics
|August 12, 2023
概括
这项研究引入了一种方法,将关键的生物背景,如细胞类型,添加到药物-基因-疾病知识图中. 这通过丰富提取的生物医学知识,改善了对罕见疾病的药物重新定位.
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
- 药理学 药理学是指药理学的学科.
背景情况:
- 药物再利用具有显著的临床潜力,特别是在罕见疾病中.
- 准确的生物医学知识图需要生物背景 (例如,细胞类型,组织).
- 现有的知识图缺乏必要的细胞类型和组织信息.
研究的目的:
- 开发一种方法,将生物背景与从文本中提取的蛋白质-蛋白质相互作用联系起来.
- 为了创建大规模的,自动生成的培训语境协会分类器的 corpora.
- 用特定的生物细节来增强来自文本的知识库.
主要方法:
- 用语法,语义和元话语特征将上下文关联作为分类任务.
- 介绍从PubMed生成的"内部人体"用于分类器培训.
- 使用精确的语法线索来识别提取关系中的细胞类型和组织相关性.
主要成果:
- 获得了0.955的细胞类型识别和0.862的组织识别F1分数.
- 证明直观,可解释的特征可以有效地解决上下文关联.
- 成功地将细胞类型上下文纳入登革热的蛋白质-蛋白质网络.
结论:
- 开发的框架使生物医学知识图表能够以必要的生物背景来丰富.
- 这种方法促进了更有原则的药理学发现和药物的重新用途.
- "内部人体"提供了一个可扩展的资源,用于培训具有上下文意识的生物医学关系提取者.
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