Inference of gene regulatory networks using s-system and differential evolution
GECCO '05 Proceedings of the 7th annual conference on Genetic and evolutionary computation
Inference of genetic networks using S-system: information criteria for model selection
Proceedings of the 8th annual conference on Genetic and evolutionary computation
Comparing mathematical models on the problem of network inference
Proceedings of the 8th annual conference on Genetic and evolutionary computation
Comparing evolutionary algorithms on the problem of network inference
Proceedings of the 8th annual conference on Genetic and evolutionary computation
Inferring gene regulatory networks from temporal expression profiles under time-delay and noise
Computational Biology and Chemistry
Inferring Gene Regulatory Networks using Differential Evolution with Local Search Heuristics
IEEE/ACM Transactions on Computational Biology and Bioinformatics (TCBB)
IEEE/ACM Transactions on Computational Biology and Bioinformatics (TCBB)
SETN '08 Proceedings of the 5th Hellenic conference on Artificial Intelligence: Theories, Models and Applications
Using dynamic bayesian networks to infer gene regulatory networks from expression profiles
Proceedings of the 2009 ACM symposium on Applied Computing
Parameter estimation in modulated, unbranched reaction chains within biochemical systems
Computational Biology and Chemistry
Inference of genetic networks using linear programming machines: application of a priori knowledge
IJCNN'09 Proceedings of the 2009 international joint conference on Neural Networks
A prior knowledge based approach to infer gene regulatory networks
ISB '10 Proceedings of the International Symposium on Biocomputing
Bio-mimetic evolutionary reverse engineering of genetic regulatory networks
EvoBIO'07 Proceedings of the 5th European conference on Evolutionary computation, machine learning and data mining in bioinformatics
ICIC'09 Proceedings of the Intelligent computing 5th international conference on Emerging intelligent computing technology and applications
Feasible prediction in S-system models of genetic networks
Expert Systems with Applications: An International Journal
Inference of gene expression networks using memetic gene expression programming
ACSC '09 Proceedings of the Thirty-Second Australasian Conference on Computer Science - Volume 91
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)
Parameter Estimation Using Metaheuristics in Systems Biology: A Comprehensive Review
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)
IEEE/ACM Transactions on Computational Biology and Bioinformatics (TCBB)
Qualitative Reasoning for Biological Network Inference from Systematic Perturbation Experiments
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
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
Inferring large scale genetic networks with S-system model
Proceedings of the 15th annual conference on Genetic and evolutionary computation
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Motivation: To resolve the high-dimensionality of the genetic network inference problem in the S-system model, a problem decomposition strategy has been proposed. While this strategy certainly shows promise, it cannot provide a model readily applicable to the computational simulation of the genetic network when the given time-series data contain measurement noise. This is a significant limitation of the problem decomposition, given that our analysis and understanding of the genetic network depend on the computational simulation. Results: We propose a new method for inferring S-system models of large-scale genetic networks. The proposed method is based on the problem decomposition strategy and a cooperative coevolutionary algorithm. As the subproblems divided by the problem decomposition strategy are solved simultaneously using the cooperative coevolutionary algorithm, the proposed method can be used to infer any S-system model ready for computational simulation. To verify the effectiveness of the proposed method, we apply it to two artificial genetic network inference problems. Finally, the proposed method is used to analyze the actual DNA microarray data. Contact: skimura@gsc.riken.jp Supplementary information: See Bioinformatics Online.