目标可控性:复杂网络中的前贪算法,满足卡尔曼的等级条件
Seyedeh Fatemeh Khezri1, Ali Ebrahimi2, Changiz Eslahchi1,2
1Department of Computer and Data Sciences, Shahid Beheshti University, Tehran 1983969411, Iran.
Bioinformatics (Oxford, England)
|October 23, 2024
概括
本研究介绍了一种用于网络控制的新贪算法,通过整合结构和动态属性来增强目标可控性. 该方法有效地识别了最小的驱动组,优于现有的方法,并揭示了乳腺癌的潜在候选药物.
科学领域:
- 复杂的网络 复杂的网络
- 系统生物学 系统生物学
- 控制理论 控制理论
背景情况:
- 网络可控性对于理解系统动态和外部信号影响至关重要.
- 目标可控性和结构可控性是NP难题,通常需要单独考虑.
- 卡尔曼的等级条件对于有效的驾驶员设置控制至关重要,但结构性方法并不总是满足.
研究的目的:
- 在大型复杂网络中开发一个有效的目标可控算法.
- 将卡尔曼的等级条件与结构可控性相结合,以加强网络控制.
- 在乳腺癌网络中识别潜在的药物重用候选人.
主要方法:
- 为了高效的目标可控性,开发了一个前贪算法.
- 该算法与巴拉巴西等人集成. 的结构可控性方法.
- 在各种网络拓和蛋白质相互作用网络中进行了实证评估.
主要成果:
- 与现有方法相比,拟议的算法始终需要较少的驱动器顶点来有效控制网络.
- 结构和动态方法的整合产生了更全面的控制策略.
- 对乳腺癌网络的应用确定了潜在的药物重用候选人.
结论:
- 解决网络可控性的结构和动态方面对于先进的控制策略至关重要.
- 开发的算法为复杂系统中高效的目标可控性提供了卓越的方法.
- 这些发现对生物医学研究,特别是癌症治疗有重大影响.
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