Efficient solution of nonlinear ordinary differential equations expressed in S-system canonical form
SIAM Journal on Numerical Analysis
Journal of Global Optimization
Evolutionary modeling and inference of gene network
Information Sciences—Informatics and Computer Science: An International Journal - Bioinformatics-selected papers from 4th CBGI & 6th JCIS Proceedings
A Trigonometric Mutation Operation to Differential Evolution
Journal of Global Optimization
Computational Modeling of Genetic and Biochemical Networks (Computational Molecular Biology)
Computational Modeling of Genetic and Biochemical Networks (Computational Molecular Biology)
System design by constraint adaptation and differential evolution
IEEE Transactions on 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
Inferring Gene Regulatory Networks using Differential Evolution with Local Search Heuristics
IEEE/ACM Transactions on Computational Biology and Bioinformatics (TCBB)
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
Inference of gene expression networks using memetic gene expression programming
ACSC '09 Proceedings of the Thirty-Second Australasian Conference on Computer Science - Volume 91
Evolving random boolean networks with genetic algorithms for regulatory networks reconstruction
Proceedings of the 13th annual conference on Genetic and evolutionary computation
Parameter Estimation Using Metaheuristics in Systems Biology: A Comprehensive Review
IEEE/ACM Transactions on Computational Biology and Bioinformatics (TCBB)
SEAL'12 Proceedings of the 9th international conference on Simulated Evolution and Learning
Inferring large scale genetic networks with S-system model
Proceedings of the 15th annual conference on Genetic and evolutionary computation
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In this work we present an improved evolutionary method for inferring S-system model of genetic networks from the time series data of gene expression. We employed Differential Evolution (DE) for optimizing the network parameters to capture the dynamics in gene expression data. In a preliminary investigation we ascertain the suitability of DE for a multimodal and strongly non-linear problem like gene network estimation. An extension of the fitness function for attaining the sparse structure of biological networks has been proposed. For estimating the parameter values more accurately an enhancement of the optimization procedure has been also suggested. The effectiveness of the proposed method was justified performing experiments on a genetic network using different numbers of artificially created time series data.