Non-homogeneous dynamic Bayesian networks with Bayesian regularization for inferring gene regulatory networks with gradually time-varying structure

Machine Learning

Volumn 90, Issue 2, 2013, Pages 191-230

  Dondelinger, Frank ^a,b   Lèbre, Sophie ^c   Husmeier, Dirk ^a,d  

^a BIOMATHEMATICS AND STATISTICS SCOTLAND   (United Kingdom)

^b UNIVERSITY OF EDINBURGH   (United Kingdom)

^c UNIVERSITÉ DE STRASBOURG   (France)

^d UNIVERSITY OF GLASGOW   (United Kingdom)

Author keywords

Dynamic Bayesian networks; Gene expression time series; Hierarchical Bayesian models; Multiple changepoint processes; Reversible jump Markov chain Monte Carlo; Systems and synthetic biology

Indexed keywords

CHANGE-POINTS; DYNAMIC BAYESIAN NETWORKS; GENE EXPRESSION TIME SERIES; HIERARCHICAL BAYESIAN MODELS; REVERSIBLE JUMP MARKOV CHAIN MONTE CARLO; SYNTHETIC BIOLOGY;

BIOLOGY; GENE EXPRESSION; HIERARCHICAL SYSTEMS; TIME SERIES; TIME VARYING NETWORKS; YEAST;

BAYESIAN NETWORKS;

EID: 84880569964     PISSN: 08856125     EISSN: 15730565     Source Type: Journal    
DOI: 10.1007/s10994-012-5311-x     Document Type: Article

References (39)

1. Ahmed, A., & Xing, E. P. (2009). Recovering time-varying networks of dependencies in social and biological studies. Proceedings of the National Academy of Sciences, 106, 11878-11883.
2. Andrianantoandro, E., Basu, S., Karig, D., & Weiss, R. (2006). Synthetic biology: new engineering rules for an emerging discipline. Molecular Systems Biology, 2(1), E1-E14.
3. 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.
4. Arbeitman, M., Furlong, E., Imam, F., Johnson, E., Null, B., Baker, B., Krasnow, M., Scott, M., Davis, R., & White, K. (2002). Gene expression during the life cycle of Drosophila melanogaster. Science, 297(5590), 2270-2275.
5. Cantone, I., Marucci, L., Iorio, F., Ricci, M.A., Belcastro, V., Bansal, M., Santini, S., di Bernardo, M., di Bernardo, D., & Cosma, M. P. (2009). A yeast synthetic network for in vivo assessment of reverse-engineering and modeling approaches. Cell, 137(1), 172-181.
6. Davis, J., & Goadrich, M. (2006). The relationship between precision-recall and ROC curves. In Proceedings of the 23rd international conference on machine learning (p. 240). New York: ACM.
7. Dondelinger, F. (2012). A machine learning approach to reconstructing signalling pathways and interaction networks in biology. PhD thesis, University of Edinburgh (in preparation).
8. Dondelinger, F., Lebre, S., & Husmeier, D. (2010). Heterogeneous continuous dynamic Bayesian networks with flexible structure and inter-time segment information sharing. In Proceedings of the 27th international conference on machine learning (ICML).
9. Formstecher, E., Aresta, S., Collura, V., Hamburger, A., Meil, A., Trehin, A., Reverdy, C., Betin, V., Maire, S., Brun, C., et al. (2005). Protein interaction mapping: a Drosophila case study. Genome Research, 15(3), 376.
10. Gelman, A., & Rubin, D. (1992). Inference from iterative simulation using multiple sequences. Statistical Science, 7(4), 457-472.
11. Green, P. (1995). Reversible jump Markov chain Monte Carlo computation and Bayesian model determination. Biometrika, 82, 711-732.
12. Grzegorczyk, M., & Husmeier, D. (2009). Non-stationary continuous dynamic Bayesian networks. In Y. Bengio, D. Schuurmans, J. Lafferty, C. K. I. Williams, & A. Culotta (Eds.), Advances in neural information processing systems (NIPS) (Vol. 22, pp. 682-690).
13. Grzegorczyk, M., & Husmeier, D. (2011). Non-homogeneous dynamic Bayesian networks for continuous data. Machine Learning, 83, 355-419.
14. Guo, F., Hanneke, S., Fu, W., & Xing, E. (2007). Recovering temporally rewiring networks: a model-based approach. In Proceedings of the 24th international conference on machine learning (p. 328). New York: ACM.
15. Hastings, W. K. (1970). Monte Carlo sampling methods using Markov chains and their applications. Biometrika, 57, 97-109.
16. Homyk, T. Jr, & Emerson, C. Jr (1988). Functional interactions between unlinked muscle genes within haploinsufficient regions of the Drosophila genome. Genetics, 119(1), 105.
17. Husmeier, D., & McGuire, G. (2003). Detecting recombination in 4-taxa DNA sequence alignments with Bayesian hidden Markov models and Markov chain Monte Carlo. Molecular Biology and Evolution, 20(3), 315-337.
18. Husmeier, D., Dondelinger, F., & Lèbre, S. (2010). Inter-time segment information sharing for non-homogeneous dynamic Bayesian networks. In J. Lafferty (Ed.), Proceedings of the twenty-fourth annual conference on neural information processing systems (NIPS) (Vol. 23, pp. 901-909). New York: Curran Associates.
19. Kolar, M., Song, L., & Xing, E. (2009). Sparsistent learning of varying-coefficient models with structural changes. In Y. Bengio, D. Schuurmans, J. Lafferty, C. K. I. Williams, & A. Culotta (Eds.), Advances in neural information processing systems (NIPS) (Vol. 22, pp. 1006-1014).
20. Larget, B., & Simon, D. L. (1999). Markov chain Monte Carlo algorithms for the Bayesian analysis of phylogenetic trees. Molecular Biology and Evolution, 16(6), 750-759.
21. Lèbre, S. (2007). Stochastic process analysis for genomics and dynamic Bayesian networks inference. PhD thesis, Université d'Evry-Val-d'Essonne, France.
22. Lèbre, S., Becq, J., Devaux, F., Lelandais, G., & Stumpf, M. (2010). Statistical inference of the time-varying structure of gene-regulation networks. BMC Systems Biology, 4, 130.
23. Locke, J., Kozma-Bognár, L., Gould, P., Fehér, B., Kevei, E., Nagy, F., Turner, M., Hall, A., & Millar, A. (2006). Experimental validation of a predicted feedback loop in the multi-oscillator clock of Arabidopsis thaliana. Molecular Systems Biology, 2(1), 59.
24. Montana, E., & Littleton, J. (2004). Characterization of a hypercontraction-induced myopathy in Drosophila caused by mutations in mhc. The Journal of Cell Biology, 164(7), 1045.
25. Nongthomba, U., Cummins, M., Clark, S., Vigoreaux, J., & Sparrow, J. (2003). Suppression of muscle hypercontraction by mutations in the myosin heavy chain gene of Drosophila melanogaster. Genetics, 164(1), 209.
26. Parkhurst, S., & Ish-Horowicz, D. (1991). WIMP, a dominant maternal-effect mutation, reduces transcription of a specific subset of segmentation genes in Drosophila. Genes & Development, 5(3), 341.
27. Pokhilko, A., Hodge, S., Stratford, K., Knox, K., Edwards, K., Thomson, A., Mizuno, T., & Millar, A. (2010). Data assimilation constrains new connections and components in a complex, eukaryotic circadian clock model. Molecular Systems Biology, 6(1), 416.
28. Prill, R. J., Marbach, D., Saez-Rodriguez, J., Sorger, P. K., Alexopoulos, L. G., Xue, X., Clarke, N. D., Altan-Bonnet, G., & Stolovitzky, G. (2010). Towards a rigorous assessment of systems biology models: the DREAM3 challenges. PLoS ONE, 5(2), e9202.
29. Punskaya, E., Andrieu, C., Doucet, A., & Fitzgerald, W. (2002). Bayesian curve fitting using MCMC with applications to signal segmentation. IEEE Transactions on Signal Processing, 50(3), 747-758.
30. Robinson, J. W., & Hartemink, A. J. (2009). Non-stationary dynamic Bayesian networks. In D. Koller, D. Schuurmans, Y. Bengio, & L. Bottou (Eds.), Advances in neural information processing systems (NIPS) (Vol. 21, pp. 1369-1376). San Mateo: Morgan Kaufmann.
31. Robinson, J., & Hartemink, A. (2010). Learning non-stationary dynamic Bayesian networks. Journal of Machine Learning Research, 11, 3647-3680.
32. Sanchez, C., Lachaize, C., Janody, F., Bellon, B., Roeder, L., Euzenat, J., Rechenmann, F., & Jacq, B. (1999). Grasping at molecular interactions and genetic networks in Drosophila melanogaster using FlyNets, an internet database. Nucleic Acids Research, 27(1), 89.
33. Sims, D., Bursteinas, B., Gao, Q., Zvelebil, M., & Baum, B. (2006). FLIGHT: database and tools for the integration and cross-correlation of large-scale RNAi phenotypic datasets. Nucleic Acids Research, 34(suppl 1), D479.
34. 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.
35. Wang, Z., Kuruoglu, E., Yang, X., Xu, Y., & Huang, T. (2011). Time varying dynamic Bayesian network for non-stationary events modeling and online inference. IEEE Transactions on Signal Processing, 4(59), 1553.
36. Werhli, A. V., & 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.
37. Xuan, X., & Murphy, K. (2007). Modeling changing dependency structure in multivariate time series. In Z. Ghahramani (Ed.), Proceedings of the 24th annual international conference on machine learning (ICML 2007) (pp. 1055-1062). New York: Omnipress.
38. Zellner, A. (1986). On assessing prior distributions and Bayesian regression analysis with g-prior distributions. In P. Goel & A. Zellner (Eds.), Bayesian inference and decision techniques (pp. 233-243). Amsterdam: Elsevier.
39. 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.