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Grzegorczyk, M.; Husmeier, D. (2009)
Publisher: Curran Associates
Languages: English
Types: Part of book or chapter of book
  • The results below are discovered through our pilot algorithms. Let us know how we are doing!

    • [1] N. Friedman, M. Linial, I. Nachman, and D. Pe'er. Using Bayesian networks to analyze expression data. Journal of Computational Biology, 7:601-620, 2000.
    • [2] K. Sachs, O. Perez, D. Pe'er, D. A. Lauffenburger, and G. P. Nolan. Protein-signaling networks derived from multiparameter single-cell data. Science, 308:523-529, 2005.
    • [3] V. A. Smith, J. Yu, T. V. Smulders, A. J. Hartemink, and E. D. Jarvi. Computational inference of neural information flow networks. PLoS Computational Biology, 2:1436-1449, 2006.
    • [4] M. Talih and N. Hengartner. Structural learning with time-varying components: Tracking the crosssection of financial time series. Journal of the Royal Statistical Society B, 67(3):321-341, 2005.
    • [5] X. Xuan and K. Murphy. Modeling changing dependency structure in multivariate time series. In Zoubin (ICML 2007), pages 1055-1062. Omnipress, 2007.
    • [6] J. W. Robinson and A. J. Hartemink. Non-stationary dynamic Bayesian networks. In D. Koller, D. Schu1369-1376. Morgan Kaufmann Publishers, 2009.
    • [7] S. Le`bre. Analyse de processus stochastiques pour la ge´nomique : e´tude du mode`le MTD et infe´rence de re´seaux baye´siens dynamiques. PhD thesis, Universite´ d'Evry-Val-d'Essonne, 2008.
    • [8] D. Heckerman, D. Geiger, and D. M. Chickering. Learning Bayesian networks: The combination of knowledge and statistical data. Machine Learning, 20:245-274, 1995.
    • [9] C. Andrieu and A. Doucet. Joint Bayesian model selection and estimation of noisy sinusoids via reversible jump MCMC. IEEE Transactions on Signal Processing, 47(10):2667-2676, 1999.
    • [10] D. Geiger and D. Heckerman. Learning Gaussian networks. In Proceedings of the Tenth Conference on Uncertainty in Artificial Intelligence, pages 235-243, San Francisco, CA., 1994. Morgan Kaufmann.
    • [11] M. Grzegorczyk, D. Husmeier, K. Edwards, P. Ghazal, and A. Millar. Modelling non-stationary gene reg24(18):2071-2078, 2008.
    • [12] Y. Ko, C. Zhai, and S.L. Rodriguez-Zas. Inference of gene pathways using Gaussian mixture models. 2007.
    • [13] A. Nobile and A.T. Fearnside. Bayesian finite mixtures with an unknown number of components: The allocation sampler. Statistics and Computing, 17(2):147-162, 2007.
    • [14] G. Schwarz. Estimating the dimension of a model. Annals of Statistics, 6:461-464, 1978.
    • [15] P. Green. Reversible jump Markov chain Monte Carlo computation and Bayesian model determination. Biometrika, 82:711-732, 1995.
    • [16] N. Friedman and D. Koller. Being Bayesian about network structure. Machine Learning, 50:95-126, 2003.
    • [17] P. Giudici and R. Castelo. Improving Markov chain Monte Carlo model search for data mining. Machine Learning, 50:127-158, 2003.
    • [18] D. Madigan and J. York. Bayesian graphical models for discrete data. International Statistical Review, 63:215-232, 1995.
    • [19] P. Fearnhead. Exact and efficient Bayesian inference for multiple changepoint problems. Statistics and Computing, 16:203-213, 2006.
    • [20] S. S. Shen-Orr, R. Milo, S. Mangan, and U. Alon.
    • Network motifs in the transcriptional regulation network of Escherichia coli. Nature Genetics, 31:64-68, 2002.
    • [21] M. K. Dougherty, J. Muller, D. A. Ritt, M. Zhou, X. Z. Zhou, T. D. Copeland, T. P. Conrads, T. D. Veenstra, K. P. Lu, and D. K. Morrison. Regulation of Raf-1 by direct feedback phosphorylation. Molecular Cell, 17:215-224, 2005.
    • [22] A. J. Hartemink. Principled Computational Methods for the Validation and Discovery of Genetic Regulatory Networks. PhD thesis, MIT, 2001.
    • [23] A. P. Dempster, N. M. Laird, and D. B. Rubin. Maximum likelihood from incomplete data via the EM algorithm. Journal of the Royal Statistical Society, B39(1):1-38, 1977.
    • [24] I. T. Nabney. NETLAB: Algorithms for Pattern Recognition. Springer Verlag, New York, 2004.
    • [25] A. Gelman and D. B. Rubin. Inference from iterative simulation using multiple sequences. Statistical Science, 7:457-472, 1992.
    • [26] W.K. Lim, K. Wang, C. Lefebvre, and A. Califano. Comparative analysis of microarray normalization procedures: effects on reverse engineering gene networks. Bioinformatics, 23(13):i282-i288, 2007.
    • [27] K. D. Edwards, P. E. Anderson, A. Hall, N. S. Salathia, J. C.W. Locke, J. R. Lynn, M. Straume, J. Q. of the Arabidopsis circadian clock. The Plant Cell, 18:639-650, 2006.
    • [28] T.C. Mockler, T.P. Michael, H.D. Priest, R. Shen, C.M. Sullivan, S.A. Givan, C. McEntee, S.A. Kay, and and promoter analysis. Cold Spring Harbor Symposia on Quantitative Biology, 72:353-363, 2007.
    • [29] C. R. McClung. Plant circadian rhythms. Plant Cell, 18:792-803, 2006.
    • [30] J.C.W. Locke, M.M. Southern, L. Kozma-Bognar, V. Hibberd, P.E. Brown, M.S. Turner, and A.J. Millar. Systems Biology, 1:(online), 2005.
    • [31] S. Imoto, S. Kim, T. Goto, , S. Aburatani, K. Tashiro, Satoru Kuhara, and Satoru Miyano. Bayesian netof Bioinformatics and Computational Biology, 1(2):231-252, 2003.
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