Gene regulatory network model identification using artificial bee colony and swarm intelligence

2012 IEEE Congress on Evolutionary Computation, CEC 2012

Volumn , Issue , 2012, Pages

  Forghany, Zary ^a   Davarynejad, Mohsen ^b   Snaar Jagalska, B Ewa ^a  

^a LEIDEN UNIVERSITY   (Netherlands)

^b DELFT UNIVERSITY OF TECHNOLOGY   (Netherlands)

Author keywords

[No Author keywords available]

Indexed keywords

ABC ALGORITHMS; ARTIFICIAL BEE COLONIES; COMPLEX STRUCTURE; COUPLED DIFFERENTIAL EQUATIONS; EVOLUTIONARY ALGORITHMS (EAS); GENE REGULATORY NETWORK MODEL; GENE REGULATORY NETWORKS; INTERACTION MECHANISMS; MEDIUM SIZE; MOLECULAR LEVELS; POWER-LAW; RANDOM TOPOLOGY; S-SYSTEMS; SEARCH SCHEME; SWARM INTELLIGENCE; UNDERLYING SYSTEMS;

ARTIFICIAL INTELLIGENCE; DIFFERENTIAL EQUATIONS; DYNAMICS; INVERSE PROBLEMS; NONLINEAR DYNAMICAL SYSTEMS; PARTICLE SWARM OPTIMIZATION (PSO);

GENES;

EID: 84866846939     PISSN: None     EISSN: None     Source Type: Conference Proceeding    
DOI: 10.1109/CEC.2012.6256461     Document Type: Conference Paper

References (28)

1. D. Bratton and J. Kennedy. Defining a standard for particle swarm optimization. In Swarm Intelligence Symposium, pages 120-127, 2007.
2. H.J. Bremermann. Optimization through evolution and recombination. Self-organizing systems, pages 93-106, 1962.
3. A.R. Chowdhury and M. Chetty. An improved method to infer gene regulatory network using s-system. In Congress of Evolutionary Computation, (CEC'11), pages 1012-1019, 2011.
4. M. Clerc. The swarm and the queen: towards a deterministic and adaptive particle swarm optimization. In Congress on Evolutionary Computation (CEC'99), volume 3, pages 1951-1957.
5. M. Clerc. Particle swarm optimization. Wiley-ISTE, 2006.
6. M. Clerc. Stagnation analysis in particle swarm optimisation or what happens when nothing happens. 2006.
7. A. Crombach and P. Hogeweg. Evolution of evolvability in gene regulatory networks. PLoS computational biology, 4(7):e1000112, 2008.
8. M. Davarynejad, C.W. Ahn, J.L.M. Vrancken, J. van den Berg, and C.A. Coello Coello. Evolutionary hidden information detection by granulation-based fitness approximation. Applied Soft Computing, 10(3):719-729, 2010.
9. M. Davarynejad, J. Vrancken, J. van den Berg, and C.A. Coello Coello. A Fitness Granulation Approach for Large-Scale Structural Design Optimization. In Raymond Chiong, Thomas Weise, and Zbigniew Michalewicz, editors, Variants of Evolutionary Algorithms for Real-World Applications, pages 245-280. Springer-Verlag, 2012.
10. K. Deb and R.B. Agrawal. Simulated binary crossover for continuous search space. Complex systems, 9(2):115-148, 1995.
11. D.E. Goldberg. The Design of Innovation: Lessons from and for Competent Genetic Algorithms. Kluwer Academic Publishers, 2002.
12. J.H. Holland. Adaptation in natural and artificial systems, 1975.
13. D. Karaboga. An idea based on honey bee swarm for numerical optimization. Techn. Rep. TR06, Erciyes Univ. Press, Erciyes, 2005.
14. D. Karaboga and B. Akay. A comparative study of artificial bee colony algorithm. Applied Mathematics and Computation, 214(1):108-132, 2009.
15. J. Kennedy and R. Mendes. Population structure and particle swarm performance. In Congress on Evolutionary Computation (CEC'02), volume 2, pages 1671-1676, 2002.
16. S. Kikuchi, D. Tominaga, M. Arita, K. Takahashi, and M. Tomita. Dynamic modeling of genetic networks using genetic algorithm and s-system. Bioinformatics, 19(5):643-650, 2003.
17. S. Kimura, K. Ide, A. Kashihara, M. Kano, M. Hatakeyama, R. Masui, N. Nakagawa, S. Yokoyama, S. Kuramitsu, and A. Konagaya. Inference of s-system models of genetic networks using a cooperative coevolutionary algorithm. Bioinformatics, 21(7):1154, 2005.
18. J.R. Koza. Genetic programming: On the programming of computers by means of natural selection. 1992.
19. W.P. Lee and Y.T. Hsiao. An adaptive gapso approach with gene clustering to infer s-system models of gene regulatory networks. The Computer Journal, 54(9):1449-1464, 2011.
20. W.P. Lee and W.S. Tzou. Computational methods for discovering gene networks from expression data. Briefings in bioinformatics, 10(4):408-423, 2009.
21. V. Miranda, H. Keko, and A.J. Duque. Stochastic star communication topology in evolutionary particle swarms (epso). International Journal of Computational Intelligent Research, 4(2):105116, 2008.
22. T. Nakayama, S. Seno, Y. Takenaka, and H. Matsuda. Inference of s-system models of gene regulatory networks using immune algorithm. Journal of Bioinformatics and Computational Biology, 9:75-86, 2011.
23. S. Navlakha and Z. Bar-Joseph. Algorithms in nature: the convergence of systems biology and computational thinking. Molecular Systems Biology, 7(1), 2011.
24. N. Noman and H. Iba. Inference of gene regulatory networks using s-system and differential evolution. In Genetic and Evolutionary Computation Conference, Washington, DC, pages 439-446, 2005.
25. A. Sirbu, H. Ruskin, and M. Crane. Comparison of evolutionary algorithms in gene regulatory network model inference. BMC Bioinformatics, 11(1):59, 2010.
26. J. Sun, J.M. Garibaldi, and C. Hodgman. Parameter estimation using metaheuristics in systems biology: A comprehensive review. Computational Biology and Bioinformatics, IEEE/ACM Transactions on, 9(1):185-202, 2012.
27. D. Tominaga, M. Okamoto, Y. Maki, S. Watanabe, and Y. Eguchi. Non-linear numerical optimization technique based on a genetic algorithm for inverse problems: Towards the inference of genetic networks. In Computer Science and Biology (Proc. German Conf. on Bioinformatics), pages 127-140, 1999.
28. K.Y. Tsai and F.S. Wang. Evolutionary optimization with data collocation for reverse engineering of biological networks. Bioinformatics, 21(7):1180, 2005.