Journal of Global Optimization
Linear Modeling of Genetic Networks from Experimental Data
Proceedings of the Eighth International Conference on Intelligent Systems for Molecular Biology
Reconstructing gene networks from large scale gene expression data
Reconstructing gene networks from large scale gene expression data
Discovering Gene Networks with a Neural-Genetic Hybrid
IEEE/ACM Transactions on Computational Biology and Bioinformatics (TCBB)
Analyzing time series gene expression data
Bioinformatics
Differential Evolution: A Practical Approach to Global Optimization (Natural Computing Series)
Differential Evolution: A Practical Approach to Global Optimization (Natural Computing Series)
Neural Networks: A Comprehensive Foundation (3rd Edition)
Neural Networks: A Comprehensive Foundation (3rd Edition)
IEEE/ACM Transactions on Computational Biology and Bioinformatics (TCBB)
GECCO'03 Proceedings of the 2003 international conference on Genetic and evolutionary computation: PartI
A study of particle swarm optimization in gene regulatory networks inference
ISNN'06 Proceedings of the Third international conference on Advances in Neural Networks - Volume Part III
A hybrid of genetic algorithm and particle swarm optimization for recurrent network design
IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics
Survey of clustering algorithms
IEEE Transactions on Neural Networks
An introduction to simulated evolutionary optimization
IEEE Transactions on Neural Networks
Computers in Biology and Medicine
RNN based MIMO channel prediction
Signal Processing
CEC'09 Proceedings of the Eleventh conference on Congress on Evolutionary Computation
Predicted modified PSO with time-varying accelerator coefficients
International Journal of Bio-Inspired Computation
EA'09 Proceedings of the 9th international conference on Artificial evolution
Parameter Estimation Using Metaheuristics in Systems Biology: A Comprehensive Review
IEEE/ACM Transactions on Computational Biology and Bioinformatics (TCBB)
Using group-decided Watts-Strogatz particle swarm optimisation to direct orbits of chaotic systems
International Journal of Wireless and Mobile Computing
IEEE/ACM Transactions on Computational Biology and Bioinformatics (TCBB)
A differential evolution algorithm with intersect mutation operator
Applied Soft Computing
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In the last decade, recurrent neural networks (RNNs) have attracted more efforts in inferring genetic regulatory networks (GRNs), using time series gene expression data from microarray experiments. This is critically important for revealing fundamental cellular processes, investigating gene functions, and understanding their relations. However, RNNs are well known for training difficulty. Traditional gradient descent-based methods are easily stuck in local minima and the computation of the derivatives is also not always possible. Here, the performance of three evolutionary-swarm computation technology-based methods, known as differential evolution (DE), particle swarm optimization (PSO), and the hybrid of DE and PSO (DEPSO), in training RNNs is investigated. Furthermore, the gene networks are reconstructed via the identification of the gene interactions, which are explained through corresponding connection weight matrices. The experimental results on two data sets studied in this paper demonstrate that the DEPSO algorithm performs better in RNN training. Also, the RNN-based model can provide meaningful insight in capturing the nonlinear dynamics of genetic networks and revealing genetic regulatory interactions.