TY - GEN
T1 - Identifying influential vertices in boolean networks through dynamical voter rank
AU - Wang, Jiannan
AU - Feng, Xiangnan
AU - Yin, Ziqiao
AU - Guo, Binghui
AU - Zheng, Zhiming
N1 - Publisher Copyright:
© 2017 IEEE.
PY - 2017/7/2
Y1 - 2017/7/2
N2 - Boolean network model has been applied to describe a series of biological systems. The stability of attractors of certain type of Boolean Networks is considered as one of the key directions to investigate the properties of Boolean network model. Due to the vast heterogeneity in topological and dynamical properties among different vertices, a small fraction of vertices could make a great influence on the dynamics. In this paper, we propose a dynamical voter rank algorithm to identify the influential vertices regarding the stability. In this algorithm, the voting score takes into account not only the topological properties, but also the dynamical properties of the vertices. The dynamical voter rank algorithm is observed to be more efficient than high degree adaptive, eigenvector centrality and Google PageRank algorithms in cases of both real and classical Boolean network model simulation. Our work provides an efficient method to identify the important vertices in Boolean networks, which may help to locate certain kinds of virulence genes.
AB - Boolean network model has been applied to describe a series of biological systems. The stability of attractors of certain type of Boolean Networks is considered as one of the key directions to investigate the properties of Boolean network model. Due to the vast heterogeneity in topological and dynamical properties among different vertices, a small fraction of vertices could make a great influence on the dynamics. In this paper, we propose a dynamical voter rank algorithm to identify the influential vertices regarding the stability. In this algorithm, the voting score takes into account not only the topological properties, but also the dynamical properties of the vertices. The dynamical voter rank algorithm is observed to be more efficient than high degree adaptive, eigenvector centrality and Google PageRank algorithms in cases of both real and classical Boolean network model simulation. Our work provides an efficient method to identify the important vertices in Boolean networks, which may help to locate certain kinds of virulence genes.
KW - Boolean network
KW - dynamical voter rank
KW - influential vertices
KW - stability
UR - https://www.scopus.com/pages/publications/85046643275
U2 - 10.1109/ITNEC.2017.8284892
DO - 10.1109/ITNEC.2017.8284892
M3 - 会议稿件
AN - SCOPUS:85046643275
T3 - Proceedings of the 2017 IEEE 2nd Information Technology, Networking, Electronic and Automation Control Conference, ITNEC 2017
SP - 1016
EP - 1020
BT - Proceedings of the 2017 IEEE 2nd Information Technology, Networking, Electronic and Automation Control Conference, ITNEC 2017
A2 - Xu, Bing
PB - Institute of Electrical and Electronics Engineers Inc.
T2 - 2nd IEEE Information Technology, Networking, Electronic and Automation Control Conference, ITNEC 2017
Y2 - 15 December 2017 through 17 December 2017
ER -