Abstract
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 dMAGAFCM-GRN. In dMAGAFCM-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. dMAGAFCM-GRN can learn continuous states directly for FCMs from data. In the experiments, the performance of dMAGAFCM-GRN is validated on both large-scale synthetic data and the benchmark DREAM3 and DREAM4. The experimental results show that dMAGAFCM-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 dMAGAFCM-GRN outperforms all other algorithms and can approximate the time series with high accuracy.
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