通过对生物医学文献的链接预测,重新利用阿尔茨海默病的非药物干预措施
medRxiv : the preprint server for health sciences
|June 9, 2023
概括
这项研究引入了一个新的知识图,ADInt,以识别阿尔茨海默病 (AD) 的非药物干预措施. R-GCN模型成功预测了潜在的疗法,包括光动力疗法和食补充剂,为AD管理提供了新的途径.
科学领域:
- 生物医学信息学是生物医学信息学.
- 计算生物学是一种计算生物学.
- 神经科学是一个神经科学.
背景情况:
- 阿尔茨海默病 (AD) 管理需要新的干预措施.
- 非药物干预 (NPI) 是有前途的,但尚未得到充分研究.
- 计算药物重新定位是识别新制药干预措施 (PI) 的关键策略.
研究的目的:
- 在生物医学知识图上使用链接预测来预测阿尔茨海默病 (AD) 的新型非药物干预 (NPI).
- 构建和评估一个综合知识图 (ADInt),整合食补充剂数据和语义关系.
- 为了比较各种知识图嵌入和 NPI 预测图形卷积网络模型的性能.
主要方法:
- 通过集成SuppKG和SemMedDB开发了ADInt知识图,包含AD概念和干预措施.
- 对知识图表表示学习的六个模型进行了比较 (TransE,RotatE,DistMult,ComplEX,R-GCN,CompGCN).
- 利用R-GCN模型进行链接预测,并采用发现模式来阐明预测NPI的机制途径.
主要成果:
- ADInt知识图包括162,213个节点和1,017,319个边缘.
- R-GCN模型在时间切片和临床试验测试集上都表现出卓越的性能.
- 确定了 (光动力学疗法,预防,阿尔茨海默氏症) 和 (状,预防,阿尔茨海默氏症) 的可信机制途径.
结论:
- 介绍了一种用于扩展知识图和发现AD的NPI的新方法.
- 采用发现模式来阐明机制,解决神经网络的解释性挑战.
- 该方法可适应用于识别药物不良反应和药物相互作用.
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