Clustering-based approach to identify solutions for the inference of regulatory networks

2005 IEEE Congress on Evolutionary Computation, IEEE CEC 2005. Proceedings

Volumn 1, Issue , 2005, Pages 660-667

  Spieth, Christian ^a   Streichert, Felix ^a   Speer, Nora ^a   Zell, Andreas ^a  

^a UNIVERSITY OF TÜBINGEN   (Germany)

Author keywords

[No Author keywords available]

Indexed keywords

CONVERGENCE OF NUMERICAL METHODS; EVOLUTIONARY ALGORITHMS; GENES; INFERENCE ENGINES; INFORMATION ANALYSIS; MATHEMATICAL MODELS; MODAL ANALYSIS; OPTIMIZATION; PROBABILITY; PROBLEM SOLVING;

CLUSTERING; GENE REGULATORY NETWORKS; OPTIMIZATION STRATEGIES;

NEURAL NETWORKS;

EID: 27144439477     PISSN: None     EISSN: None     Source Type: Conference Proceeding    
DOI: None     Document Type: Conference Paper

References (31)

1. S. Ando, E. Sakamoto, and H. Iba. Evolutionary modeling and inference of gene network. Information Sciences, 145(3-4):237-259, 2002.
2. D. Beasley, D. Bull, and R. Martin. A sequential niche technique for multimodal function optimization. Evolutionary Computation, 1(2):101-125, 1993.
3. D. Corne and C. Pridgeon. Investigating issues in the reconstructability of genetic regulatory networks. In Proceedings of the IEEE Congress on Evolutionary Computation, pages 582-589, 2004.
4. H. de Jong. Modeling and simulation of genetic regulatory systems: A literature review. Journal of Computational Biology, 9(1):67-103, January 2002.
5. M. Ester, H.-P. Kriegel, J. Sander, and X. Xiaowei. A density-based algorithm for discovering clusters in large spatial databases with noise. In Proceedings of the International Conference on Knowledge Discovery and Data Mining, pages 226-231, 1996.
6. N. Friedman, M. Linial, I. Nachman, and D. Pe'er. Using Bayesian networks to analyze expression data. In Proceedings of the Conference on Research in Computational Molecular Biology, pages 127-135, 2000.
7. D. E. Goldberg and J. Richardson. Genetic algorithms with sharing for multimodal function optimization. In Proceedings of the International Conference on Genetic Algorithms, pages 41-49, 1987.
8. N. Hansen and A. Ostermeier. Adapting arbitrary normal mutation distributions in evolution strategies: the covariance matrix adaptation. In Proceedings of the IEEE Congress on Evolutionary Computation, pages 312-317, 1996.
9. W. Hlavacek and M. Savageau. Rules for coupled expression of regulator and effector genes in inducible circuits. Journal of Molecular Biology, 255:121-139, 1996.
10. J. H. Holland. Adaption in Natural and Artificial Systems: An Introductory Analysis with Applications to Biology, Control and Artificial Systems. University Press of Michigan, 1975.
11. D. Husmeier. Sensitivity and specificity of inferring genetic regulatory interactions from microarray experiments with dynamic Bayesian networks. Bioinformatics, 19(17):2271-2282, 2003.
12. S. Imoto, T. Goto, and S. Miyano. Estimation of genetic networks and functional structures between genes by using Bayesian networks and nonparametric regression. In Proceedings of the Pacific Symposium on Biocomputing, pages 175-186, 2002.
13. 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.
14. J. R. Koza. Genetic Programming: On the Programming of Computers by Means of Natural Selection. MIT Press, 1992.
15. Y. Maki, D. Tominaga, M. Okamoto, S. Watanabe, and Y. Eguchi. Development of a system for the inference of large scale genetic networks. In Proceedings of the Pacific Symposium on Biocomputing, volume 6, pages 446-458, 2001.
16. T. Moriyama, A. Shinohara., M. Takeda, O. Maruyama, T. Goto, S. Miyano, and S. Kuhara. A system to find genetic networks using weighted network model. In Genome Informatics, volume 10, pages 186-195, 1999.
17. K. Noda, A. Shinohara, M. Takeda, S. Matsumoto, S. Miyano, and S. Ku. Finding genetic network from experiments by weighted network model. Genome Informatics, 9:141-150, 1998.
18. I. Ono, R. Yoshiaki Seike, N. Ono, and M. Matsui. An evolutionary algorithm taking account of mutual interactions among substances for inference of genetic networks. In Proceedings of the IEEE Congress on Evolutionary Computation, pages 2060-2067, 2004.
19. A. Petrowski. A clearing procedure as a niching method for genetic algorithms. In Proceedings of the IEEE Congress on Evolutionary Computation, pages 798-803, 1996.
20. I. Rechenberg. Evolutionsstrategie - Optimierung technischer Systeme nach Prinzipien der biologischen Evolution. Frommann-Holzboog, 1973.
21. E. Sakamoto and H. Iba. Inferring a system of differential equations for a gene regulatory network by using genetic programming. In Proceedings of the IEEE Congress on Evolutionary Computation, pages 720-726, 2001.
22. J. Sander, M. Ester, H.-P. Kriegel, and X. Xiaowei. Density-based clustering in spatial databases, the algorithm GDBSCAN and its applications. Data Mining and Knowledge Discovery, 2(2):169-194, 1998.
23. M. A. Savageau. 20 years of S-systems. In E. Voit, editor, Canonical Nonlinear Modeling. S-systems Approach to Understand Complexity, pages 1-44, 1991.
24. H.-P. Schwefel. Numerical optimization of computer models. John Wiley & Sons, 1981.
25. C. Spieth, F. Streichert, N. Speer, and A. Zell. Iteratively inferring gene regulatory networks with virtual knockout experiments. In Proceedings of the European Workshop on Evolutionary Bioinformatics, volume 3005 of Lecture Notes in Computer Science, pages 102-111, 2004.
26. C. Spieth, F. Streichert, N. Speer, and A. Zell. Multiobjective model optimization for inferring gene regulatory networks. In E. Z. Carlos A. Coello Coello, Arturo Hernndez Aguirre, editor, Proceedings of the Conference on Evolutionary Multi-Criterion Optimization, volume 3410 of Lecture Notes in Computer Science, pages 607-620, 2005.
27. F. Streichert, G. Stein, H. Ulmer, and A. Zell. A clustering based niching ea for multimodal search spaces. In Proceedings of the International Conference Evolution Artificielle, volume 2936 of Lecture Notes in Computer Science, pages 293-304, 2003.
28. D. Tominaga, N. Kog, and M. Okamoto. Efficient numeral optimization technique based on genetic algorithm for inverse problem. In Proceedings of the Genetic and Evolutionary Computation Conference, pages 251-258, 2000.
29. A. Törn. Cluster analysis using seed points and density-determined hyperspheres as an aid to global optimization. IEEE Transactions on Systems, Man and Cybernetics, 7:610-616, 1977.
30. D. Weaver, C. Workman, and G. Stormo. Modeling regulatory networks with weight matrices. In Proceedings of the Pacific Symposium on Biocomputing, volume 4, pages 112-123, 1999.
31. M. Q. Zhang. Large-scale gene expression data analysis: A new challenge to computational biologists. Genome Research, 9(8):681-688, 1999.