Swarm intelligence
Field Guide to Dynamical Recurrent Networks
Field Guide to Dynamical Recurrent Networks
A new approach to analyzing gene expression time series data
Proceedings of the sixth annual international conference on Computational biology
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
Evolving genetic regulatory networks using an artificial genome
APBC '04 Proceedings of the second conference on Asia-Pacific bioinformatics - Volume 29
Discovering Gene Networks with a Neural-Genetic Hybrid
IEEE/ACM Transactions on Computational Biology and Bioinformatics (TCBB)
Analyzing time series gene expression data
Bioinformatics
Neural Networks: A Comprehensive Foundation (3rd Edition)
Neural Networks: A Comprehensive Foundation (3rd Edition)
GECCO'03 Proceedings of the 2003 international conference on Genetic and evolutionary computation: PartI
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
ANTS '08 Proceedings of the 6th international conference on Ant Colony Optimization and Swarm Intelligence
On temporal logic constraint solving for analyzing numerical data time series
Theoretical Computer Science
Parameter estimation for asymptotic regression model by particle swarm optimization
Proceedings of the first ACM/SIGEVO Summit on Genetic and Evolutionary Computation
Gene network inference using a swarm intelligence framework
Proceedings of the 11th Annual Conference Companion on Genetic and Evolutionary Computation Conference: Late Breaking Papers
Artificial Neural Network Based Algorithm for Biomolecular Interactions Modeling
IWANN '09 Proceedings of the 10th International Work-Conference on Artificial Neural Networks: Part I: Bio-Inspired Systems: Computational and Ambient Intelligence
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
Genetic Networks and Soft Computing
IEEE/ACM Transactions on Computational Biology and Bioinformatics (TCBB)
Using Qualitative Probability in Reverse-Engineering Gene Regulatory Networks
IEEE/ACM Transactions on Computational Biology and Bioinformatics (TCBB)
IEEE/ACM Transactions on Computational Biology and Bioinformatics (TCBB)
Inference of Biological S-System Using the Separable Estimation Method and the Genetic Algorithm
IEEE/ACM Transactions on Computational Biology and Bioinformatics (TCBB)
Gene regulatory network reverse engineering using population based incremental learning and K-means
Proceedings of the 14th annual conference companion on Genetic and evolutionary computation
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)
Reverse engineering of gene regulatory networks from biological data
Wiley Interdisciplinary Reviews: Data Mining and Knowledge Discovery
A novel bio-inspired approach based on the behavior of mosquitoes
Information Sciences: an International Journal
A novel strategy for plant breeding based on simulations of gene network models
International Journal of Bioinformatics Research and Applications
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Genetic regulatory network inference is critically important for revealing fundamental cellular processes, investigating gene functions, and understanding their relations. The availability of time series gene expression data makes it possible to investigate the gene activities of whole genomes, rather than those of only a pair of genes or among several genes. However, current computational methods do not sufficiently consider the temporal behavior of this type of data and lack the capability to capture the complex nonlinear system dynamics. We propose a recurrent neural network (RNN) and particle swarm optimization (PSO) approach to infer genetic regulatory networks from time series gene expression data. Under this framework, gene interaction is explained through a connection weight matrix. Based on the fact that the measured time points are limited and the assumption that the genetic networks are usually sparsely connected, we present a PSO-based search algorithm to unveil potential genetic network constructions that fit well with the time series data and explore possible gene interactions. Furthermore, PSO is used to train the RNN and determine the network parameters. Our approach has been applied to both synthetic and real data sets. The results demonstrate that the RNN/PSO can provide meaningful insights in understanding the nonlinear dynamics of the gene expression time series and revealing potential regulatory interactions between genes.