Power-law formalism: a canonical nonlinear approach to modeling and analysis
WCNA '92 Proceedings of the first world congress on World congress of nonlinear analysts '92, volume IV
Journal of Global Optimization
A Trigonometric Mutation Operation to Differential Evolution
Journal of Global Optimization
Inference of gene regulatory networks using s-system and differential evolution
GECCO '05 Proceedings of the 7th annual conference on Genetic and evolutionary computation
GECCO '05 Proceedings of the 7th annual conference on Genetic and evolutionary computation
Inference of differential equation models by genetic programming
Information Sciences: an International Journal
Using dynamic bayesian networks to infer gene regulatory networks from expression profiles
Proceedings of the 2009 ACM symposium on Applied Computing
Reverse Engineering of Regulatory Relations in Gene Networks by a Probabilistic Approach
WILF '09 Proceedings of the 8th International Workshop on Fuzzy Logic and Applications
A prior knowledge based approach to infer gene regulatory networks
ISB '10 Proceedings of the International Symposium on Biocomputing
Meta analysis algorithms for microarray gene expression data using Gene Regulatory Networks
International Journal of Data Mining and Bioinformatics
Parameter Estimation Using Metaheuristics in Systems Biology: A Comprehensive Review
IEEE/ACM Transactions on Computational Biology and Bioinformatics (TCBB)
Information Sciences: an International Journal
IEEE/ACM Transactions on Computational Biology and Bioinformatics (TCBB)
A scalable approach for inferring transcriptional regulation in the yeast cell cycle
Proceedings of the 2nd ACM Conference on Bioinformatics, Computational Biology and Biomedicine
The inference of breast cancer metastasis through gene regulatory networks
Journal of Biomedical Informatics
AI'11 Proceedings of the 24th international conference on Advances in Artificial Intelligence
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
The Regulatory Network Computational Device
Genetic Programming and Evolvable Machines
A Constrained Evolutionary Computation Method for Detecting Controlling Regions of Cortical Networks
IEEE/ACM Transactions on Computational Biology and Bioinformatics (TCBB)
On the reconstruction of genetic network from partial microarray data
ICONIP'12 Proceedings of the 19th international conference on Neural Information Processing - Volume Part I
mDBN: motif based learning of gene regulatory networks using dynamic bayesian networks
Proceedings of the 15th annual conference on Genetic and evolutionary computation
Genetic programming with genetic regulatory networks: genetic programming
Proceedings of the 15th annual conference on Genetic and evolutionary computation
Adaptive Memetic Differential Evolution with Global and Local neighborhood-based mutation operators
Information Sciences: an International Journal
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We present a memetic algorithm for evolving the structure of biomolecular interactions and inferring the effective kinetic parameters from the time series data of gene expression using the decoupled Ssystem formalism. We propose an Information Criteria based fitness evaluation for gene network model selection instead of the conventional Mean Squared Error (MSE) based fitness evaluation. A hill-climbing local-search method has been incorporated in our evolutionary algorithm for efficiently attaining the skeletal architecture which is most frequently observed in biological networks. The suitability of the method is tested in gene circuit reconstruction experiments, varying the network dimension and/or characteristics, the amount of gene expression data used for inference and the noise level present in expression profiles. The reconstruction method inferred the network topology and the regulatory parameters with high accuracy. Nevertheless, the performance is limited to the amount of expression data used and the noise level present in the data. The proposed fitness function has been found more suitable for identifying correct network topology and for estimating the accurate parameter values compared to the existing ones. Finally, we applied the methodology for analyzing the cell-cycle gene expression data of budding yeast and reconstructed the network of some key regulators.