Intelligent evolutionary algorithms for large parameter optimization problems
IEEE Transactions on Evolutionary Computation
IEEE Transactions on Systems, Man, and Cybernetics, Part A: Systems and Humans
IEEE Transactions on Fuzzy Systems
IEEE Transactions on Information Theory
Data classification with a generalized Gaussian components based density estimation algorithm
IJCNN'09 Proceedings of the 2009 international joint conference on Neural Networks
Feasible prediction in S-system models of genetic networks
Expert Systems with Applications: An International Journal
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)
Information Sciences: an International Journal
IEEE/ACM Transactions on Computational Biology and Bioinformatics (TCBB)
Evolving gene regulatory networks: a sensitivity-based approach
ICIC'11 Proceedings of the 7th international conference on Intelligent Computing: bio-inspired computing and applications
Inference of Biological S-System Using the Separable Estimation Method and the Genetic Algorithm
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
State and parameter estimation for nonlinear biological phenomena modeled by S-systems
Digital Signal Processing
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From gene expression profiles, it is desirable to rebuild cellular dynamic regulation networks to discover more delicate and substantial functions in molecular biology, biochemistry, bioengineering and pharmaceutics. S-system model is suitable to characterize biochemical network systems and capable to analyze the regulatory system dynamics. However, inference of an S-system model of N-gene genetic networks has 2N(N+1) parameters in a set of non-linear differential equations to be optimized. This paper proposes an intelligent two-stage evolutionary algorithm (iTEA) to efficiently infer the S-system models of genetic networks from time-series data of gene expression. To cope with curse of dimensionality, the proposed algorithm consists of two stages where each uses a divide-and-conquer strategy. The optimization problem is first decomposed into N subproblems having 2(N+1) parameters each. At the first stage, each subproblem is solved using a novel intelligent genetic algorithm (IGA) with intelligent crossover based on orthogonal experimental design (OED). At the second stage, the obtained N solutions to the N subproblems are combined and refined using an OED-based simulated annealing algorithm for handling noisy gene expression profiles. The effectiveness of iTEA is evaluated using simulated expression patterns with and without noise running on a single-processor PC. It is shown that 1) IGA is efficient enough to solve subproblems; 2) IGA is significantly superior to the existing method SPXGA; and 3) iTEA performs well in inferring S-system models for dynamic pathway identification.