Learning dynamic Bayesian network models via cross-validation

Pattern Recognition Letters

Volumn 26, Issue 14, 2005, Pages 2295-2308

  Peña, Jose M ^a   Björkegren, Johan ^b   Tegnér, Jesper ^a,b  

^a LINKÖPING UNIVERSITY   (Sweden)

^b KAROLINSKA INSTITUTE   (Sweden)

Author keywords

Cross validation; Dynamic Bayesian network models; Learning.

Indexed keywords

INFORMATION ANALYSIS; MATHEMATICAL MODELS; SAMPLING;

CROSS-VALIDATION; DYNAMIC BAYESIAN NETWORK MODELS; GENERALIZATION ABILITY; LEARNING;

LEARNING SYSTEMS;

EID: 25644453369     PISSN: 01678655     EISSN: None     Source Type: Journal    
DOI: 10.1016/j.patrec.2005.04.005     Document Type: Article

References (27)

1. H. Akaike A new look at the statistical model identification IEEE Trans. Automatic Control 19 1974 716 723
2. Bouckaert, R.R., 2003. Choosing between two learning algorithms based on calibrated tests. In: Proc. Twentieth Internat. Conf. on Machine Learning, pp. 51-58.
3. D.M. Chickering Optimal structure identification with greedy search J. Machine Learning Res. 3 2002 507 554
4. D.M. Chickering, and D. Heckerman A comparison of scientific and engineering criteria for Bayesian model selection Statist. Comput. 10 2000 55 62
5. R.G. Cowell, A.P. Dawid, S.L. Lauritzen, and D.J. Spiegelhalter Probabilistic Networks and Expert Systems 1999 Springer-Verlag
6. P. D'haeseleer, S. Liang, and R. Somogyi Genetic network inference: From co-expression clustering to reverse engineering Bioinformatics 16 2000 707 726
7. Friedman, N., Murphy, K., Russell, S., 1998. Learning the structure of dynamic probabilistic networks. In: Proc. Fourteenth Conf. on Uncertainty in Artificial Intelligence, pp. 139-147.
8. D. Heckerman, D. Geiger, and D.M. Chickering Learning Bayesian networks: The combination of knowledge and statistical data Machine Learning 20 1995 197 243
9. R. Hofmann, and V. Tresp Nonlinear Markov networks for continuous variables Advances in Neural Information Processing Systems 10 1998 521 527
10. D. Husmeier Sensitivity and specificity of inferring genetic regulatory interactions from microarray experiments with dynamic Bayesian networks Bioinformatics 19 2003 2271 2282
11. Kauffman, S., Peterson, C., Samuelsson, B., Troein, C., 2003. Random Boolean network models and the yeast transcriptional network. In: Proc. National Academy of Sciences of the USA, vol. 100, 14796-14799.
12. E. Keogh, and M. Pazzani Learning the structure of augmented Bayesian classifiers Internat. J. Artificial Intelligence Tools 11 2002 587 601
13. Kim, S., Imoto, S., Miyano, S., 2003. Dynamic Bayesian network and nonparametric regression for nonlinear modeling of gene networks from time series gene expression data. In: Proc. First Internat. Workshop on Comput. Methods in Systems Biology, pp. 104-113.
14. Kohavi, R., 1995. A study of cross-validation and bootstrap for accuracy estimation and model selection. In: Proc. Fourteenth Internat. Joint Conf. on Artificial Intelligence, pp. 1137-1143.
15. T.I. Lee, N.J. Rinaldi, F. Robert, D.T. Odom, Z. Bar-Joseph, G.K. Gerber, N.M. Hannett, C.T. Harbison, C.M. Thompson, I. Simon, J. Zeitlinger, E.G. Jennings, H.L. Murray, D.B. Gordon, B. Ren, J.J. Wyrick, J.B. Tagne, T.L. Volkert, E. Fraenkel, D.K. Gifford, and R.A. Young Transcriptional regulatory networks in Saccharomyces cerevisiae Science 298 2002 799 804
16. Moral, S., 2004. An empirical comparison of score measures for independence. In: Proc. Tenth Internat. Conf. on Information Process. and Management of Uncertainty in Knowledge-Based Systems, pp. 1307-1314.
17. Murphy, K., Mian, S., 1999. Modelling gene expression data using dynamic Bayesian networks. Technical Report, Computer Science Division, University of California, Berkeley.
18. R.E. Neapolitan Learning Bayesian Networks 2003 Prentice Hall
19. Ng, A., 1997. Preventing "Overfitting" of cross-validation data. In: Proc. Fourteenth Internat. Conf. on Machine Learning, pp. 245-253.
20. I.M. Ong, J.D. Glasner, and D. Page Modelling regulatory pathways in E. coli from time series expression profiles Bioinformatics 18 2002 S241 S248
21. Pazzani, M., 1995. Searching for dependencies in Bayesian classifiers. In: Proc. Fifth Internat. Workshop on Artificial Intell. Statist., pp. 239-248.
22. B.E. Perrin, L. Ralaivola, A. Mazurie, S. Bottani, J. Mallet, and F. d'Alché-Buc Gene networks inference using dynamic Bayesian networks Bioinformatics 19 2003 ii138 ii148
23. G. Schwarz Estimating the dimension of a model Annals of Statistics 6 1978 461 464
24. Steck, H., Jaakkola, T., 2003. On the Dirichlet prior and Bayesian regularization. In: Advances in Neural Information Process. Systems, vol. 15, pp. 697-704.
25. Van Allen, T., Greiner, R., 2000. Model selection criteria for learning belief nets: An empirical comparison. In: Proc. Seventeenth Internat. Conf. on Machine Learning, pp. 1047-1054.
26. Wessels, L.F.A., Van Someren, E.P., Reinders, M.J.T., 2001. A comparison of genetic network models. In: Proc. Pacific Symposium on Biocomputing, pp. 508-519.
27. M. Zou, and S.D. Conzen A new dynamic Bayesian network (DBN) approach for identifying gene regulatory networks from time course microarray data Bioinformatics 21 2005 71 79