KGiA:通过疾病意识知识图表增强来重新定位药物
Çerağ Oğuztüzün1, Zhenxiang Gao2, Hui Li2
1Center for Artificial Intelligence in Drug Discovery, Case Western Reserve University, 10900 Euclid Ave, Cleveland, 44106, OH, USA; Department of Computer Science, Case Western Reserve University, 10900 Euclid Ave, Cleveland, 44106, OH, USA.
Journal of biomedical informatics
|June 22, 2025
概括
本研究介绍了KGiA,这是一种使用反事实关系增强生物医学知识图 (KG) 的新方法. KGiA显著提高了药物重新定位的准确性,并确定了新的疾病候选药物.
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
- 药物发现 药物发现 药物发现
背景情况:
- 药物重新利用通过为现有药物找到新的用途来加速发展.
- 生物医学知识图 (KG) 有助于药物重新使用,但由于不完整而受到限制.
- 现有的KG方法难以将其推广到新的生物医学实体.
研究的目的:
- 开发一种诱导式图形增强方法 (KGiA),用于增强药物重定位.
- 为了使半诱导推理能够对看不见的生物医学实体进行概括.
- 改进基于KG的药物重定向的性能和通用性.
主要方法:
- 通过结合来自疾病特定拓模式的反事实关系,KGiA增强了知识图.
- 该方法应用于一个大规模的生物医学KG (1.6M三倍,100K实体,30K疾病).
- 半诱导推理允许对以前看不见的生物医学实体进行概括.
主要成果:
- 在五个架构中,KGiA在平均互惠等级 (MRR) 中提高了24倍的概括性.
- 拟议的方法高达32%的性能优于基于KG的最新药物重定向模型.
- 包括阿尔茨海默病在内的案例研究表明,KGiA在识别新型重用药物候选者的潜力.
结论:
- 增加知识图表与反事实关系可以改善基于KG的药物重定向.
- 在药物发现方面,KGiA提供了一种有希望的方法来克服KG的不完整性.
- 该方法提高了对现有药物的新疗法应用的识别能力.
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