整合多个来源的药物信息,以集成药物相互作用网络
Ji Lv1, Guixia Liu1, Yuan Ju2
1College of Computer Science and Technology, Jilin University, Changchun, China; Key Laboratory of Symbolic Computation and Knowledge Engineering of Ministry of Education, Jilin University, Changchun, China.
Computers in biology and medicine
|June 1, 2023
概括
一种新方法整合了多种药物数据源,以识别药物组和预测相互作用,帮助抗菌素耐药性研究. 这种方法提高了对药物协同作用和对抗作用的理解.
科学领域:
- 药理学 药理学是指药理学的学科.
- 计算生物学 计算生物学
- 生物信息学是一种生物信息学.
背景情况:
- 描述药物相互作用对于提高药物疗效和打击抗菌素耐药性至关重要.
- 药物相互作用分析的实验方法是资源密集的.
- 现有的计算方法很难有效地整合各种药物信息来源.
研究的目的:
- 开发一种用于整合多来源药物信息的新型计算方法.
- 有效地识别药物组并分析组级药物相互作用.
- 为了提高药物相互作用的预测.
主要方法:
- 一种相似度矩阵融合 (SMF) 方法被提出,以整合结构,制药,表型和治疗药物的相似性.
- SMF与t分布式随机邻居嵌入 (t-SNE) 和等级聚类相结合.
- 开发了新的指标,边缘纯度和边缘规范化的相互信息,以评估集群质量.
主要成果:
- SMF方法成功地整合了各种药物信息,从而产生了高度单色的药物组 (纯粹协同或对抗).
- 与其他方法相比,SMF在集群质量评估方面表现优越.
- 聚类药物相互作用网络在预测新的药物相互作用时达到0.741的准确性.
结论:
- SMF方法提供了一种全面的方法来理解药物组及其相互作用.
- 这种方法提高了药物相互作用的预测,并有助于管理抗菌素耐药性.
- 多源药物数据的整合为药物药理学提供了宝贵的见解.
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