A Dynamic Multiagent Genetic Algorithm for Gene Regulatory Network Reconstruction Based on Fuzzy Cognitive Maps PROJECT TITLE :A Dynamic Multiagent Genetic Algorithm for Gene Regulatory Network Reconstruction Based on Fuzzy Cognitive MapsABSTRACT:In order to reconstruct giant-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 mostly on fuzzy cognitive maps (FCMs) during 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 issues in mind. All agents live during a lattice-like atmosphere, and therefore the neighbors of each agent are changed dynamically consistent with their energy in each generation. dMAGAFCM-GRN can learn continuous states directly for FCMs from knowledge. Within the experiments, the performance of dMAGAFCM-GRN is validated on both large-scale artificial information and therefore the benchmark DREAM3 and DREAM4. The experimental results show that dMAGAFCM-GRN is ready to effectively learn FCMs with 200 nodes; that's, 40 00zero weights would like to be optimized. The systematic comparison with five existing algorithms shows that dMAGAFCM-GRN outperforms all different algorithms and will approximate the time series with high accuracy. Did you like this research project? To get this research project Guidelines, Training and Code... Click Here facebook twitter google+ linkedin stumble pinterest Ferromagnetic FePt/Au Core/Shell Nanoparticles Prepared by Solvothermal Annealing Long-term stable 60 GHz optical two-tone signal by destructive optical interference obtained from RF phase adjustment