Abstract
Dynamic Bayesian networks (DBNs) have received increasing attention from the computational biology community as models of gene regulatory networks. However, conventional DBNs are based on the homogeneous Markov assumption and cannot deal with inhomogeneity and nonstationarity in temporal processes. The present chapter provides a detailed discussion of how the homogeneity assumption can be relaxed. The improved method is evaluated on simulated data, where the network structure is allowed to change with time, and on gene expression time series during morphogenesis in Drosophila melanogaster.
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References
Robinson JW, Hartemink AJ (2009) Non-stationary dynamic Bayesian networks. In Koller D, Schuurmans D, Bengio Y et al editors, Advances in Neural Information Processing Systems (NIPS), volume 21, 1369–1376. Morgan Kaufmann Publishers.
Grzegorczyk M, Husmeier D (2009) Non-stationary continuous dynamic Bayesian networks. In Bengio Y, Schuurmans D, Lafferty J et al editors, Advances in Neural Information Processing Systems (NIPS), volume 22, 682–690.
Ahmed A, Xing EP (2009) Recovering time-varying networks of dependencies in social and biological studies. Proceedings of the National Academy of Sciences 106:11878–11883.
Talih M, Hengartner N (2005) Structural learning with time-varying components: Tracking the cross-section of financial time series. Journal of the Royal Statistical Society B 67(3):321–341.
Xuan X, Murphy K (2007) Modeling changing dependency structure in multivariate time series. In Ghahramani Z editor, Proceedings of the 24th Annual International Conference on Machine Learning (ICML 2007), 1055–1062. Omnipress.
Lèbre S (2007) Stochastic process analysis for Genomics and Dynamic Bayesian Networks inference. Ph.D. thesis, Université d’Evry-Val-d’Essonne, France.
Lèbre S, Becq J, Devaux F et al. (2010) Statistical inference of the time-varying structure of gene-regulation networks. BMC Systems Biology 4(130).
Kolar M, Song L, Xing E (2009) Sparsistent learning of varying-coefficient models with structural changes. In Bengio Y, Schuurmans D, Lafferty J et al editors, Advances in Neural Information Processing Systems (NIPS), volume 22, 1006–1014.
Larget B, Simon DL (1999) Markov chain Monte Carlo algorithms for the Bayesian analysis of phylogenetic trees. Molecular Biology and Evolution 16(6):750–759.
Arbeitman M, Furlong E, Imam F et al. (2002) Gene expression during the life cycle of Drosophila melanogaster. Science 297(5590):2270–2275.
Zhao W, Serpedin E, Dougherty E (2006) Inferring gene regulatory networks from time series data using the minimum description length principle. Bioinformatics 22(17):2129.
Giot L, Bader JS, Brouwer C et al (2003) A protein interaction map of drosophila melanogaster. Science 302:1727–1736.
Yu J, Pacifico S, Liu G et al. (2008) DroID: the Drosophila Interactions Database, a comprehensive resource for annotated gene and protein interactions. BMC Genomics 9(461).
Guo F, Hanneke S, Fu W et al. (2007) Recovering temporally rewiring networks: A model-based approach. In Proceedings of the 24th international conference on Machine learning page 328. ACM.
Andrieu C, Doucet A (1999) Joint Bayesian model selection and estimation of noisy sinusoids via reversible jump MCMC. IEEE Transactions on Signal Processing 47(10):2667–2676.
Green P (1995) Reversible jump Markov chain Monte Carlo computation and Bayesian model determination. Biometrika 82:711–732.
Werhli AV, Husmeier D (2008) Gene regulatory network reconstruction by Bayesian integration of prior knowledge and/or different experimental conditions. Journal of Bioinformatics and Computational Biology 6(3):543–572.
Elgar S, Han J, Taylor M (2008) mef2 activity levels differentially affect gene expression during Drosophila muscle development. Proceedings of the National Academy of Sciences 105(3):918.
Gelman A, Rubin D (1992) Inference from iterative simulation using multiple sequences. Statistical science 7(4):457–472.
Hand DJ (2009) Measuring classifier performance: a coherent alternative to the area under the roc curve. Machine Learning 77:103–123.
Davis J, Goadrich M (2006) The relationship between precision-recall and ROC curves. In ICML ’06: Proceedings of the 23rd international conference on Machine Learning 233–240. ACM, New York, NY, USA. ISBN 1-59593-383-2. doi: http://doi.acm.org/10.1145/1143844.1143874.
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Lèbre, S., Dondelinger, F., Husmeier, D. (2012). Nonhomogeneous Dynamic Bayesian Networks in Systems Biology. In: Wang, J., Tan, A., Tian, T. (eds) Next Generation Microarray Bioinformatics. Methods in Molecular Biology, vol 802. Humana Press. https://doi.org/10.1007/978-1-61779-400-1_13
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DOI: https://doi.org/10.1007/978-1-61779-400-1_13
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