Reconstructing genetic networks with concurrent representation of instantaneous and time-delayed interactions

2011 IEEE Congress of Evolutionary Computation, CEC 2011

Volumn , Issue , 2011, Pages 1840-1847

  Morshed, Nizamul ^a   Chetty, Madhu ^a  

^a MONASH UNIVERSITY   (Australia)

Author keywords

[No Author keywords available]

Indexed keywords

BIOLOGICAL PROCESS; CURRENT MODELING; EVOLUTIONARY SEARCH STRATEGY; GENE EXPRESSION DATA; GENE REGULATORY NETWORKS; GENETIC INTERACTION; GENETIC NETWORKS; LIVING ORGANISMS; RECONSTRUCTION METHOD; SYNTHETIC DATA; TIME-DELAYED INTERACTIONS;

BAYESIAN NETWORKS; BIOLOGY; GENE EXPRESSION; INFERENCE ENGINES; INFORMATION THEORY; YEAST;

EVOLUTIONARY ALGORITHMS;

EID: 80052000978     PISSN: None     EISSN: None     Source Type: Conference Proceeding    
DOI: 10.1109/CEC.2011.5949839     Document Type: Conference Paper

References (30)

1. H. Kishino and P. Waddell, "Correspondence analysis of genes and tissue types and finding genetic links from microarray data", GENOME INFORMATICS SERIES, pp. 83-95, 2000.
2. S. Bay, L. Chrisman, A. Pohorille, and J. Shrager, "Temporal aggregation bias and inference of causal regulatory networks", Journal of Computational Biology, vol. 11, no. 5, pp. 971-985, 2004. (Pubitemid 39488545)
3. R. Ram and M. Chetty, "Learning Structure of a Gene Regulatory Network", 2007.
4. N. Friedman, M. Linial, I. Nachman, and D. Pe'er, "Using Bayesian networks to analyze expression data", Journal of computational biology, vol. 7, no. 3-4, pp. 601-620, 2000.
5. R. Ram, M. Chetty, and T. Dix, "Causal Modeling of Gene Regulatory Network", In Computational Intelligence and Bioinformatics and Computational Biology, 2006. CIBCB'06. 2006 IEEE Symposium on, pp. 1-8, IEEE, 2006.
6. S. Kim, S. Imoto, and S. Miyano, "Dynamic Bayesian network and nonparametric regression for nonlinear modeling of gene networks from time series gene expression data", Biosystems, vol. 75, no. 1-3, pp. 57-65, 2004. (Pubitemid 38887551)
7. N. Morshed and M. Chetty, "Information theoretic dynamic bayesian network approach for reconstructing genetic networks", In Proceedings of the 11h IASTED International Conference on Artificial Intelligence and Applications, pp. 236-243, 2011.
8. B. Perrin, L. Ralaivola, A. Mazurie, S. Bottani, J. Mallet, and F. d'Alché Buc, "Gene networks inference using dynamic Bayesian networks", Bioinformatics, vol. 19, no. suppl 2, p. ii138, 2003.
9. Z. Xing and D. Wu, "Modeling multiple time units delayed gene regulatory network using dynamic Bayesian network", In Proceedings of the Sixth IEEE International Conference on Data Mining - Workshops, pp. 190-195, IEEE, 2006.
10. W. Zhao, E. Serpedin, and E. Dougherty, "Inferring connectivity of genetic regulatory networks using information-theoretic criteria", IEEE/ACM Transactions on Computational Biology and Bioinformatics, pp. 262-274, 2007.
11. P. Zoppoli, S. Morganella, and M. Ceccarelli, "TimeDelay-ARACNE: Reverse engineering of gene networks from time-course data by an information theoretic approach", BMC bioinformatics, vol. 11, no. 1, p. 154, 2010.
12. A. Margolin, I. Nemenman, K. Basso, C. Wiggins, G. Stolovitzky, R. Favera, and A. Califano, "ARACNE: an algorithm for the reconstruction of gene regulatory networks in a mammalian cellular context", BMC bioinformatics, vol. 7, no. Suppl 1, p. S7, 2006.
13. R. Ram, M. Chetty, and T. Dix, "Fuzzy model for gene regulatory network", In Evolutionary Computation, 2006. CEC 2006. IEEE Congress on, pp. 1450-1455, IEEE, 2006. (Pubitemid 47130668)
14. E. Sakamoto and H. Iba, "Inferring a system of differential equations for a gene regulatory network by using genetic programming", In Evolutionary Computation, 2001. Proceedings of the 2001 Congress on, vol. 1, pp. 720-726, IEEE, 2001. (Pubitemid 32806409)
15. M. Savageau, "years of S-systems", Canonical Nonlinear Modeling. S-systems Approach to Understand Complexity, pp. 1-44, 20.
16. J. Pearl, Causality: models, reasoning, and inference. Cambridge Univ Pr, 2000.
17. N. Friedman, K. Murphy, and S. Russell, "Learning the structure of dynamic probabilistic networks", In Proc. Fourteenth Conference on Uncertainty in Artificial Intelligence (UAI98), pp. 139-147, Citeseer, 1998.
18. M. Frigault, L. Wang, A. Singhal, and S. Jajodia, "Measuring network security using dynamic bayesian network", In Proceedings of the 4th ACM workshop on Quality of protection, pp. 23-30, ACM, 2008.
19. V. Chaitankar, P. Ghosh, E. Perkins, P. Gong, Y. Deng, and C. Zhang, "A novel gene network inference algorithm using predictive minimum description length approach", BMC Systems Biology, vol. 4, no. Suppl 1, p. S7, 2010.
20. T. Cover, J. Thomas, J. Wiley, et al., Elements of information theory, vol. 306. Wiley Online Library, 1991.
21. L. de Campos, "A scoring function for learning Bayesian networks based on mutual information and conditional independence tests", The Journal of Machine Learning Research, vol. 7, pp. 2149-2187, 2006. (Pubitemid 44582045)
22. S. Kullback, Information theory and statistics. Wiley, 1968.
23. F. Morchen and A. Ultsch, "Optimizing time series discretization for knowledge discovery", In Proceedings of the eleventh ACM SIGKDD international conference on Knowledge discovery in data mining, pp. 660-665, ACM, 2005. (Pubitemid 43218336)
24. N. Sugimoto and H. Iba, "Inference of gene regulatory networks by means of dynamic differential bayesian networks and nonparametric regression", GENOME INFORMATICS SERIES, vol. 15, no. 2, p. 121, 2004.
25. D. Eaton and K. Murphy, "Bayesian structure learning using dynamic programming and MCMC", Proceedings of the Twenty-Third Confererence on Uncertainty in Artificial Intelligence (UAI 2007), 2007.
26. M. Koivisto and K. Sood, "Exact Bayesian structure discovery in Bayesian networks", The Journal of Machine Learning Research, vol. 5, pp. 549-573, 2004.
27. N. Noman and H. Iba, "Inferring gene regulatory networks using differential evolution with local search heuristics", IEEE/ACM Transactions on Computational Biology and Bioinformatics, pp. 634-647, 2007. (Pubitemid 350125886)
28. I. Cantone, L. Marucci, F. Iorio, M. Ricci, V. Belcastro, M. Bansal, S. Santini, M. Di Bernardo, D. Di Bernardo, and M. Cosma, "A yeast synthetic network for in vivo assessment of reverse-engineering and modeling approaches", Cell, vol. 137, no. 1, pp. 172-181, 2009.
29. G. Della Gatta, M. Bansal, A. Ambesi-Impiombato, D. Antonini, C. Missero, and D. Di Bernardo, "Direct targets of the TRP63 transcription factor revealed by a combination of gene expression profiling and reverse engineering", Genome research, vol. 18, no. 6, p. 939, 2008.
30. J. Yu, V. Smith, P. Wang, A. Hartemink, and E. Jarvis, "Advances to Bayesian network inference for generating causal networks from observational biological data", Bioinformatics, vol. 20, no. 18, p. 3594, 2004.