因果知识图分析确定了药物的不良影响
Sumyyah Toonsi1, Paul N Schofield2, Robert Hoehndorf1,3,4
1Computer, Electrical and Mathematical Sciences & Engineering Division, King Abdullah University of Science and Technology, Thuwal 23955, Saudi Arabia.
Bioinformatics (Oxford, England)
|December 12, 2025
概括
我们介绍了因果知识图 (CKG),将生物医学知识图与因果推理结合起来. 这种方法成功地确定了已知的和新的不良药物影响,改善了药物适用性预测.
科学领域:
- 生物医学信息学 生物医学信息学
- 因果推理因果推理
- 知识表示 知识表示
背景情况:
- 生物医学知识图和因果模型是有价值的,但没有联系.
- 知识图缺乏概率语义;因果模型缺乏背景知识整合.
研究的目的:
- 弥合知识图和因果模型之间的差距.
- 开发因果知识图 (CKG) 用于原则性因果推断和假设制定.
主要方法:
- 扩展了具有正式因果语义的知识图,创建了因果知识图 (CKG).
- 构建了一个药物-疾病 CKG (DD-CKG) 整合疾病路径,药物指示和副作用.
- 应用DD-CKG到英国生物银行和MIMIC-IV进行大规模中介药物效应分析.
主要成果:
- 使用背景知识,CKG使解惑和假设制定成为可能.
- 成功复制已知的不良药物反应,并确定了新的候选效应.
- 通过将预测的效果与现有数据库相结合,改善了共享药物指示的预测.
结论:
- CKG为可扩展的因果推理提供了一个可概括的,基于知识的框架.
- 该方法证明了临床相关性,并支持自动化大规模调解分析.
- 这种方法提高了对药物效应和疾病进展的理解.
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