An adaptive GA - PSO approach with gene clustering to infer S-system models of gene regulatory networks

Computer Journal

Volumn 54, Issue 9, 2011, Pages 1449-1464

  Lee, Wei Po ^a   Hsiao, Yu Ting ^a  

^a NATIONAL SUN YAT SEN UNIVERSITY   (Taiwan)

Author keywords

gene clustering; gene regulatory network; genetic algorithm; particle swarm optimization; S system; systems biology

Indexed keywords

GENE CLUSTERING; GENE REGULATORY NETWORK; PARTICLE SWARM; S-SYSTEMS; SYSTEMS BIOLOGY;

ADAPTIVE ALGORITHMS; GENETIC ALGORITHMS; PARTICLE SWARM OPTIMIZATION (PSO);

GENE EXPRESSION;

EID: 80052595485     PISSN: 00104620     EISSN: 14602067     Source Type: Journal    
DOI: 10.1093/comjnl/bxr038     Document Type: Article

References (25)

1. Ingolia, N.T. andWeissman, J.S. (2008) Systems biology: Reverse engineering the cell. Nature, 454, 1059-1062.
2. Karlebach, G. and Shamir, R. (2008) Modelling and analysis of gene regulatory networks. Nat. Rev. Mol. Cell Biol., 9, 770-780.
3. deJong, H. (2002) Modeling and simulation of genetic regulatory systems: A literature review. J. Comput. Biol., 9, 67-103.
4. Lee, W.-P. and Tzou, W.-S. (2009) Computational methods for discovering gene networks from expression data. Brief Bioinf., 10, 408-423.
5. Kennedy, J. and Eberhart, R. (2001) Swarm Intelligence. Morgan Kaufmann Publishers, CA.
6. Angeline, P.J. (1998) Evolutionary Optimization Versus Particle Swarm Optimization: Philosophy And Performance Difference. Proc. Int. Conf. Evolutionary Programming, San Diego, CA, March 25-27, pp. 601-610. Springer, Berlin.
7. Eberhart, R.C. and Shi,Y. (1998) Comparison Between Genetic Algorithms and Particle Swarm Optimization. Proc. Int. Conf. Evolutionary Programming, San Diego, CA, March 25-27, pp. 611-616. Springer, Berlin.
8. Settles, M. and Soule, T. (2005) Breeding Swarms: A GA/PSO Hybrid. Proc. Genetic and Evolutionary Computation Conf., Washington DC, June 25-29, pp. 161-168. ACM Press, MA.
9. Grimaccia, F., Mussetta, M. and Zich, R. (2006) Genetical swarm optimization: Self-adaptive hybrid evolutionary algorithm for electromagnetics. IEEE Trans. Antennas Propag., 55, 781-785.
10. Elhossini, A., Areibi, S. and Dony, R. (2010) Strength pareto particle swarm optimization and hybrid EA-PSO for multiobjective optimization. Evol. Comput., 18, 127-156.
11. Noman, N. and Iba, H. (2005) Inference of Gene Regulatory Networks Using S-system and Differential Evolution. Proc. Genetic and Evolutionary Computation Conf., Washington DC, June 25-29, pp. 439-446. ACM Press, MA.
12. Lee, W.-P. and Yang, K.-C. (2008) A clustering-based approach for inferring recurrent neural networks as gene regulatory networks. Neurocomputing, 71, 600-610.
13. Ho, S.-Y., Hsieh, C.-H., Yu, C.-F. and Huang, H.-L. (2007) An intelligent two-stage evolutionary algorithm for dynamic pathway identification from gene expression profiles. IEEE/ACM Trans. Comput. Biol. Bioinf., 4, 648-660.
14. Engelbrecht,A.P. (2005) Fundamentals of Computational Swarm Intelligence.Wiley,West Sussex.
15. Kohonen, T. (1990) The self-organizing map. Proc. IEEE, 78, 1464-1480.
16. Mallat, S. (1989)A theory for multiresolution signal decomposition: Thewavelet representation. IEEE Trans. Pattern Anal. Mach. Intell., 11, 674-693.
17. Daubechies, I. (1990) The wavelet transform, time-frequency localization and signal analysis. IEEE Trans. Inf. Theory, 36, 961-1005.
18. Cao, H., Kang, L. and Chen, Y. (2000) Evolutionary modeling of systems of ordinary differential equations with genetic programming. Genet. Program. Evol. Mach., 1, 309-337.
19. Tucker,W. and Moulton, V. (2006) Parameter reconstruction for biochemical networks using interval analysis. Reliab. Comput., 12, 389-402.
20. Wang, F.-S. (2001) Hybrid differential evolution for problems of kinetic parameter estimation and dynamic optimization of an ethanol fermentation process. Chem. Eng. Sci., 40, 2876-2855.
21. Hlavacck, W.S. and Savageau, M.A. (1996) Rules for coupled expression of regulator and effector genes in inducible circuits. J. Mol. Biol., 255, 121-139.
22. Vu, T. and Vohradsky, J. (2002) Genexp: A genetic network simulation environment. Bioinformatics, 18, 1400-1401.
23. van Someren, E.P., Wessels, L., Reinders, M. and Backer, E. (2002) Regularization and Noise Injection for Improving Genetic Network Models. In Zhang,W. and Shmulevich, I. (eds), Computational and Statistical Approaches to Genomics, pp. 211- 226. Kluwer, Dordrecht.
24. Bishop, C.M. (1994) Training with noise is equivalent to Tikhonov regularization. Neural Comput., 7, 108-116.
25. Wen, X. et al. (1998) Large-scale temporal gene expression mapping of central nervous system development. Proc. Natl. Acad. Sci. USA, 95, 334-339.