Dynamic bayesian network and nonparametric regression for nonlinear modeling of gene networks from time series gene expression data

Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)

Volumn 2602, Issue , 2003, Pages 104-113

  Kim, Sun Yong ^a   Imoto, Seiya ^a   Miyano, Satoru ^a  

^a UNIVERSITY OF TOKYO   (Japan)

Author keywords

[No Author keywords available]

Indexed keywords

BIOLOGICAL SYSTEMS; GENE EXPRESSION; GENES; KNOWLEDGE BASED SYSTEMS; REGRESSION ANALYSIS; TIME SERIES; YEAST;

BAYESIAN NETWORK MODELS; DYNAMIC BAYESIAN NETWORKS; GENE EXPRESSION DATA; NON-LINEAR MODEL; NON-PARAMETRIC REGRESSION; NONLINEAR RELATIONS; TIME SERIES MICROARRAYS; TIME-SERIES GENE EXPRESSION DATA;

BAYESIAN NETWORKS;

EID: 84958595689     PISSN: 03029743     EISSN: 16113349     Source Type: Book Series    
DOI: 10.1007/3-540-36481-1_9     Document Type: Conference Paper

References (24)

1. Akaike, H.: Information theory and an extension of the maximum likelihood principle. In: Petrov, B.N., Csaki, F. (eds.): 2nd International Symposium on Information Theory. Akademiai Kiado, Budapest (1973) 267–281 107
2. Berger, J.O.: Statistical Decision theory and Bayesian analysis. Springer-Verlag New York (1985) 105
3. Bilmes, J.A.: Dynamic Bayesian multinets. Proc. 16th Conf on Uncertainty in Artificial Intelligence (2000) 38–45 104
4. Burnham, K.P., Anderson, D.R.: Model selection and inference, a practical information-theoretical approach. Springer-Verlag New York (1998) 107
5. Chen, T., He, H. L., Church, G.M.: Modeling gene expression with differential equations. Proc. Pacific Symposium on Biocomputing 4 (1999) 29–40 105
6. Davison A.C.: Approximate predictive likelihood. Biometrika 73 (1986) 323–332 108
7. De Boor, C.: A practical guide to splines. Springer-Verlag Berlin (1978) 108
8. De Hoon, M. J. L., Imoto, S., Kobayashi, K., Ogasawara, N., Miyano, S.: Inferring gene regulatory networks from time-ordered gene expression data of Bacillussubtilis using differential equations. Proc. Pacific Symposium on Biocomputing 8 (2003) in press 105
9. DeRisi, J., Lyer, V.R., Brown, P.O.: Exploring the metabolic and gene control of gene expression on a genomic scale. Science 278 (1997) 680–686 111
10. Dierckx, P.: Curve and surface fitting with splines. Oxford (1993) 108
11. Eiler, P.H. C., Marx, B.: Flexible smoothing with B-splines and penalties (with discussion). Statistical Science 11 (1996) 89–121 108
12. Friedman, N., Murphy, K., Russell, S.: Learning the structure of dynamic probabilistic networks. Proc. Conf. on Uncertainty in Artificial Intelligence (1998) 139–147 104
13. Friedman, N., Linial, M., Nachman, I., Pe’er, D.: Using Bayesian network to analyze expression data. J. Comp. Biol. 7 (2000) 601–620 104
14. Imoto, S., Goto, T., Miyano, S.: Estimation of genetic networks and functional structures between genes by using Bayesian network and nonparametric regression. Proc. Pacific Symposium on Biocomputing 7 (2002) 175–186 104, 105, 108, 109, 112
15. Imoto, S., Kim, S., Goto, T., Aburatani, S., Tashiro, K., Kuhara, S., Miyano, S.: Bayesian network and nonparametric heteroscedastic regression for nonlinear modelling of genetic network. Proc. IEEE Computer Society Bioinformatics Conference (2002) 219–227 104, 105, 108, 109, 112
16. Konishi, S.: Statistical model evaluation and information criteria. In: Ghosh, S. (ed.): Multivaliate Analysis, Design of Experiments and Survey Sampling. Marcel Dekker, New York (1999) 369–399 107
17. Konishi, S., Kitagawa, G.: Generalized information criteria in model selection. Biometrika 83 (1996) 875–890 107
18. Murphy, K., Mian, S.: Modelling Gene Expression Data using Dynamic Bayesian Networks. Technical report, Computer Science Division, University of California, Berkeley, CA (1999) 104
19. Pe’er, D., Regev, A., Elidan, G., Friedman, N.: Inferring subnetworks from perturbed expression profiles. Bioinformatics 17 (ISBM2001) 215–224 104
20. Rangel, C., Wild, D.L., Falciani, F.:Modelling biological responses using gene expression profiling and linear dynamical systems. Proc. 2nd International Conference on Systems Biology (2001) 104
21. Someren, E. V., Wessels, L., Reinders, M.: Linear modeling of genetic networks from experimental data. Bioinformatics 18 (ISMB2002) 355–366 104
22. Spellman, P.T., Sherlock, G., Zhang, M. Q., Iyer, V.R., Anders, K., Eisen, M.B., Brown, P. O., Botstein, D., Futcher, B.: Comprehensive identification of cell cycleregulated genes of the yeast Saccharomyces cerevisiae by microarray hybridization. Molecular Biology of the Cell 9 (1998) 3273–3297 110
23. Tinerey, L., Kadane, J. B.: Accurate approximations for posterior moments and marginal densities. J. Amer. Statist. Assoc. 81 (1986) 82–86 108
24. http://www.genome.ad.jp/kegg/ 111