多DS-MDA:将多个数据源集成到异质网络中,以预测新代谢物-药物关联
Xiuhong Li1, Hao Yuan1, Xiaoliang Wu1
1College of Bioinformatics Science and Technology, Harbin Medical University, China.
Computers in biology and medicine
|June 5, 2023
概括
本研究介绍了MultiDS-MDA,这是一个计算框架,集成了各种数据来识别代谢物-药物关联. 这种方法可以提高对药物对新陈代谢的影响的理解,并有助于药物开发.
科学领域:
- 生物化学和药理学 生物化学和药理学
- 计算生物学 计算生物学
- 系统生物学 系统生物学
背景情况:
- 代谢过程对生命至关重要;异常的代谢物度与疾病有关.
- 药物显著影响新陈代谢,影响疗效,毒性和相互作用.
- 目前对代谢物与药物相关性的理解是不完整的,因为数据有限.
研究的目的:
- 开发一个计算框架来推断可靠的代谢物-药物关联.
- 整合多个数据源进行全面分析.
- 改善对代谢物和药物之间的相互作用的预测.
主要方法:
- 提出了一个计算框架,MultiDS-MDA.
- 综合化学结构,代谢物-基因/疾病,药物-基因/疾病的关系.
- 利用了基因本体学 (GO) 和疾病本体学 (DO) 数据,以及已知的连接.
主要成果:
- 通过5倍交叉验证实现了0.911的AUROC和0.907的AUPRC.
- 与现有方法相比,表现出优越的性能.
- 案例研究证实了特定代谢物-药物对的可靠性和效率.
结论:
- 多DS-MDA有效地通过整合各种数据来识别代谢物-药物关联.
- 该框架为探索代谢物-药物相互作用提供了一个强大的工具.
- 这项研究有助于推进药物开发和组合疗法.
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