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Advances in Bioinformatics
Volume 2010 (2010), Article ID 749848, 17 pages
http://dx.doi.org/10.1155/2010/749848
Research Article

Modelling Nonstationary Gene Regulatory Processes

1Department of Statistics, TU Dortmund University, 44221 Dortmund, Germany
2Biomathematics and Statistics Scotland, JCMB, The King's Buildings, Edinburgh EH93JZ, UK

Received 18 December 2009; Accepted 29 April 2010

Academic Editor: Yves Van de Peer

Copyright © 2010 Marco Grzegorcyzk et al. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

Linked References

  1. C. J. Needham, J. R. Bradford, A. J. Bulpitt, and D. R. Westhead, “A primer on learning in Bayesian networks for computational biology,” PLoS computational biology, vol. 3, no. 8, article e129, 2007. View at Publisher · View at Google Scholar · View at Scopus
  2. N. Friedman and D. Koller, “Being Bayesian about network structure. A Bayesian approach to structure discovery in Bayesian networks,” Machine Learning, vol. 50, no. 1-2, pp. 95–125, 2003. View at Publisher · View at Google Scholar · View at Scopus
  3. M. Grzegorczyk and D. Husmeier, “Improving the structure MCMC sampler for Bayesian networks by introducing a new edge reversal move,” Machine Learning, vol. 71, no. 2-3, pp. 265–305, 2008. View at Publisher · View at Google Scholar · View at Scopus
  4. M. Grzegorczyk, D. Husmeier, K. D. Edwards, P. Ghazal, and A. J. Millar, “Modelling non-stationary gene regulatory processes with a non-homogeneous Bayesian network and the allocation sampler,” Bioinformatics, vol. 24, no. 18, pp. 2071–2078, 2008. View at Publisher · View at Google Scholar · View at Scopus
  5. A. Nobile and A. T. Fearnside, “Bayesian finite mixtures with an unknown number of components: the allocation sampler,” Statistics and Computing, vol. 17, no. 2, pp. 147–162, 2007. View at Publisher · View at Google Scholar · View at Scopus
  6. M. A. Suchard, R. E. Weiss, K. S. Dorman, and J. S. Sinsheimer, “Inferring spatial phylogenetic variation along nucleotide sequences: a multiple changepoint model,” Journal of the American Statistical Association, vol. 98, no. 462, pp. 427–437, 2003. View at Publisher · View at Google Scholar · View at Scopus
  7. M. Grzegorczyk and D. Husmeier, “Modelling non-stationary gene regulatory processes with a non-homogeneous dynamic Bayesian network and the change point process,” in Proceedings of the 6th International Workshop on Computational Systems Biology, T. Manninen, C. Wiuf, H. Lähdesmäki et al., Eds., vol. 48 of TICSP Series, pp. 51–54, Tampere, Finland, 2009.
  8. S. Imoto, S. Kim, T. Goto et al., “Bayesian network and nonparametric heteroscedastic regression for nonlinear modeling of genetic network,” Journal of Bioinformatics and Computational Biology, vol. 1, no. 2, pp. 231–252, 2003. View at Google Scholar · View at Scopus
  9. Y. Ko, C. Zhai, and S. L. Rodriguez-Zas, “Inference of gene pathways using Gaussian mixture models,” in Proceedings of the IEEE International Conference on Bioinformatics and Biomedicine (BIBM '07), pp. 362–367, Fremont, Calif, USA, November 2007. View at Publisher · View at Google Scholar · View at Scopus
  10. Y. Ko, C. X. Zhai, and S. Rodriguez-Zas, “Inference of gene pathways using mixture Bayesian networks,” BMC Systems Biology, vol. 3, article 54, 2009. View at Publisher · View at Google Scholar · View at Scopus
  11. G. Schwarz, “Estimating the dimension of a model,” Annals of Statistics, vol. 6, pp. 461–464, 1978. View at Google Scholar
  12. J. W. Robinson and A. J. Hartemink, “Non-stationary dynamic Bayesian networks,” in Advances in Neural Information Processing Systems 21, D. Koller, D. Schuurmans, Y. Bengio, and L. Bottou, Eds., pp. 1369–1376, 2009. View at Google Scholar
  13. S. Lèbre, Analyse de processus stochastiques pour la génomique : étude du modèle MTD et inférence de réseaux bayésiens dynamiques, Ph.D. thesis, Université d'Evry-Val-d'Essonne, 2008.
  14. D. Geiger and D. Heckerman, “Learning Gaussian networks,” in Proceedings of the 10th Annual Conference on Uncertainty in Artificial Intelligence (UAI '94), R. López de Mántaras and D. Poole, Eds., Morgan Kaufmann, Seattle, Wash, USA, July 1994.
  15. G. F. Cooper and E. Herskovits, “A Bayesian method for the induction of probabilistic networks from data,” Machine Learning, vol. 9, no. 4, pp. 309–347, 1992. View at Publisher · View at Google Scholar · View at Scopus
  16. D. Heckerman and D. Geiger, “Learning Bayesian networks: a unification for discrete and Gaussian domains,” in Proceedings of the 11th Annual Conference on Uncertainty in Artificial Intelligence (UAI '95), P. Besnard and S. Hanks, Eds., Morgan Kaufmann, Montreal, Canada, August 1995.
  17. P. J. Green, “Reversible jump Markov chain Monte Carlo computation and Bayesian model determination,” Biometrika, vol. 82, pp. 711–732, 1995. View at Google Scholar
  18. D. Madigan and J. York, “Bayesian graphical models for discrete data,” International Statistical Review, vol. 63, pp. 215–232, 1995. View at Google Scholar
  19. K. Honda, A. Takaoka, and T. Taniguchi, “Type I inteferon gene induction by the interferon regulatory factor family of transcription factors,” Immunity, vol. 25, no. 3, pp. 349–360, 2006. View at Publisher · View at Google Scholar · View at Scopus
  20. P. A. Salomé and C. R. McClung, “The Arabidopsis thaliana clock,” Journal of Biological Rhythms, vol. 19, no. 5, pp. 425–435, 2004. View at Publisher · View at Google Scholar · View at Scopus
  21. P. Más, “Circadian clock function in Arabidopsis thaliana: time beyond transcription,” Trends in Cell Biology, vol. 18, no. 6, pp. 273–281, 2008. View at Publisher · View at Google Scholar · View at Scopus
  22. M. N. Arbeitman, E. E. M. Furlong, F. Imam et al., “Gene expression during the life cycle of Drosophila melanogaster,” Science, vol. 297, no. 5590, pp. 2270–2275, 2002. View at Publisher · View at Google Scholar · View at Scopus
  23. A. Gelman and D. B. Rubin, “Inference from iterative simulation using multiple sequences,” Statistical Science, vol. 7, pp. 457–472, 1992. View at Google Scholar
  24. D. Husmeier, “Sensitivity and specificity of inferring genetic regulatory interactions from microarray experiments with dynamic Bayesian networks,” Bioinformatics, vol. 19, no. 17, pp. 2271–2282, 2003. View at Publisher · View at Google Scholar · View at Scopus
  25. C. R. McClung, “Plant circadian rhythms,” Plant Cell, vol. 18, no. 4, pp. 792–803, 2006. View at Publisher · View at Google Scholar · View at Scopus
  26. J. C. Locke, M. M. Southern, L. Kozma-Bognár et al., “Extension of a genetic network model by iterative experimentation and mathematical analysis,” Molecular Systems Biology, vol. 1, article 13, 2005. View at Google Scholar · View at Scopus
  27. A. V. Werhli and D. Husmeier, “Gene regulatory network reconstruction by bayesian integration of prior knowledge and/or different experimental conditions,” Journal of Bioinformatics and Computational Biology, vol. 6, no. 3, pp. 543–572, 2008. View at Publisher · View at Google Scholar · View at Scopus