在AcuPoint-Disease网络上识别基于动机的PageRank的关键节点.
Xuelong Yu1, Xiao Liu2, Li Luo3
1Engineering Research Center of Security Technology of Complex Network System, School of Computer Science and Engineering, Northeastern University, Shenyang 110169, China.
Evidence-based complementary and alternative medicine : eCAM
|December 14, 2023
概括
这项研究引入了一种新的算法,通过考虑高阶相互作用来识别针网络中的关键针点. 这种新的方法提高了准确性,并提供了一种更可靠的方法来优化针处方.
科学领域:
- 网络科学 网络科学
- 传统中国医药 传统中国医药
- 计算生物学 计算生物学
背景情况:
- 现有的关键针点挖掘算法使用二元协同作用,忽视了更高阶的相互作用和午线模式.
- 目前的评估方法缺乏广泛的适用性和普遍性.
研究的目的:
- 提出一个高特异性的关键针点挖掘算法,使用针点疾病网络 (DNA) 中的3节点模式.
- 为算法评估引入通用评估标准 (分辨率,网络损失,准确性).
- 根据传统中医的原则,确定具有重大全球网络影响的关键针点.
主要方法:
- 开发了一个关键的针点挖掘算法,基于针点疾病网络 (DNA) 中的3节点图案.
- 引入了新的,普遍的评估标准:分辨率,网络损失和准确性.
- 根据分析的分布特征将针点划分为19个区域.
主要成果:
- 拟议的算法在识别关键针点方面获得了63%的准确性,比现有方法的效率高出14%至21%.
- 确定了关键的重点,严重破坏了网络连接,并表现出高度的协同合作.
- 经过证明的稳定性和特异性,确保确定关键针点的可靠性.
结论:
- 该算法可靠地根据网络拓和高协同性识别核心针点.
- 这些发现支持通过探索有针对性的,高影响力的针点组合来优化针处方.
- 这种方法增强了对 меридиан系统内针点关系的理解.
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