Wang Bingbo, Ma Xiujuan, Wang Cunchi, Zhang Mingjie, Gong Qianhua, Gao Lin
School of Computer Science and Technology, Xidian University, Xi'an 710071, China.
Entropy (Basel). 2022 Jul 15;24(7):979. doi: 10.3390/e24070979.
The determination of directed control paths in complex networks is important because control paths indicate the structure of the propagation of control signals through edges. A challenging problem is to identify them in complex networked systems characterized by different types of interactions that form multilayer networks. In this study, we describe a graph pattern called the conserved control path, which allows us to model a common control structure among different types of relations. We present a practical conserved control path detection method (CoPath), which is based on a maximum-weighted matching, to determine the paths that play the most consistent roles in controlling signal transmission in multilayer networks. As a pragmatic application, we demonstrate that the control paths detected in a multilayered pan-cancer network are statistically more consistent. Additionally, they lead to the effective identification of drug targets, thereby demonstrating their power in predicting key pathways that influence multiple cancers.
确定复杂网络中的定向控制路径很重要，因为控制路径指示了控制信号通过边传播的结构。一个具有挑战性的问题是在以形成多层网络的不同类型相互作用为特征的复杂网络系统中识别它们。在本研究中，我们描述了一种称为保守控制路径的图模式，它使我们能够对不同类型关系之间的共同控制结构进行建模。我们提出了一种基于最大加权匹配的实用保守控制路径检测方法（CoPath），以确定在多层网络中控制信号传输时发挥最一致作用的路径。作为一个实际应用，我们证明了在多层泛癌网络中检测到的控制路径在统计上更一致。此外，它们能够有效识别药物靶点，从而证明了它们在预测影响多种癌症的关键途径方面的能力。
