期刊
IEEE TRANSACTIONS ON FUZZY SYSTEMS
卷 24, 期 2, 页码 419-431出版社
IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
DOI: 10.1109/TFUZZ.2015.2459756
关键词
Fuzzy cognitive maps; gene regulatory networks (GRNs); genetic algorithms; multiagent systems
资金
- Outstanding Young Scholar Program of National Natural Science Foundation of China (NSFC) [61522311]
- General Program of NSFC [61271301]
- Overseas, Hong Kong & Macao Scholars Collaborated Research Program of NSFC [61528205]
- Research Fund for the Doctoral Program of Higher Education of China [20130203110010]
- Fundamental Research Funds for the Central Universities [K5051202052]
In order to reconstruct large-scale gene regulatory networks (GRNs) with high accuracy, a robust evolutionary algorithm, a dynamic multiagent genetic algorithm (dMAGA), is proposed to reconstruct GRNs from time-series expression profiles based on fuzzy cognitive maps (FCMs) in this paper. The algorithm is labeled as dMAGA(FCM)-GRN. In dMAGA(FCM)-GRN, agents and their behaviors are designed with the intrinsic properties of GRN reconstruction problems in mind. All agents live in a lattice-like environment, and the neighbors of each agent are changed dynamically according to their energy in each generation. dMAGA(FCM)-GRN can learn continuous states directly for FCMs from data. In the experiments, the performance of dMAGA(FCM)-GRN is validated on both large-scale synthetic data and the benchmark DREAM3 and DREAM4. The experimental results show that dMAGA(FCM)-GRN is able to effectively learn FCMs with 200 nodes; that is, 40 000 weights need to be optimized. The systematic comparison with five existing algorithms shows that dMAGA(FCM)-GRN outperforms all other algorithms and can approximate the time series with high accuracy.
作者
我是这篇论文的作者
点击您的名字以认领此论文并将其添加到您的个人资料中。
推荐
暂无数据