Abstract
We propose a dynamic Bayesian network and nonparametric regression model for constructing a gene network from time series microarray gene expression data.The proposed method can overcome a shortcoming of the Bayesian network model in the sense of the construction of cyclic regulations.The proposed method can analyze the microarray data as continuous data and can capture even nonlinear relations among genes. It can be expected that this model will give a deeper insight into the complicated biological systems.We also derive a new criterion for evaluating an estimated network from Bayes approach.We demonstrate the effectiveness of our method by analyzing Saccharomyces cerevisiae gene expression data.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Preview
Unable to display preview. Download preview PDF.
References
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
Berger, J.O.:Statistical Decision theory and Bayesian analysis. Springer-Verlag New York (1985)105
Bilmes, J.A.:Dynamic Bayesian multinets.Proc.16th Conf. on Uncertainty in Artificial Intelligence (2000) 38–45 104
Burnham, K.P., Anderson, D.R.:Model selection and inference, a practical information-theoretical approach.Springer-Verlag New York (1998)107
Chen, T., He, H.L., Church, G.M.:Modeling gene expression with differential equations. Proc.Pacific Symposium on Biocomputing 4 (1999) 29–40 105
Davison A.C.:Approximate predictive likelihood.Biometrika 73 (1986) 323–332 108
De Boor, C.:A practical guide to splines.Springer-Verlag Berlin (1978)108
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
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
Dierckx, P.:Curve and surface fitting with splines.Oxford (1993) 108
Eiler, P.H.C., Marx, B.:Flexible smoothing with B splines and penalties (with discussion). Statistical Science 11 (1996) 89–121 108
Friedman, N., Murphy, K., Russell, S.:Learning the structure of dynamic probabilistic networks. Proc.Conf. on Uncertainty in Artificial ntelligence (1998)139–147 104
Friedman, N., Linial, M., Nachman, I, Pe’er, D.:Using Bayesian network to analyze expression data. J.Comp.Biol.7 (2000) 601–620 104
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) 175–186104, 105, 108, 109, 112
Imoto, S., Kim, S., Goto, T., Aburatani, S., Tashiro, K., Kuhara, S., Miyano, S.: Bayesian network and nonparametric heteroscedastic regression for nonlinear modeling of genetic network. Proc.EEE Computer Society Bioinformatics Conference(2002) 219–227104, 105, 108, 109, 112
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
Konishi, S., Kitagawa, G.:Generalized information criteria in model selection. Biometrika 83 (1996) 875–890 107
Murphy, K., Mian, S.:Modelling Gene Expression Data using Dynamic Bayesian Networks.Technical report, Computer Science Division, University of California, Berkeley, CA (1999) 104
Pe’er, D., Regev, A., Elidan, G., Friedman, N.:Inferring subnetworks from perturbed expression profiles.Bioinformatics 17 (ISBM2001) 215–224 104
Rangel, C., Wild, D.L., Falciani, F.:Modelling biological responses using gene expression profiling and linear dynamical systems. Proc.2nd nternational Conference on Systems Biology (2001)104
Someren, E.V., Wessels, L., Reinders, M.:Linear modeling of genetic networks from experimental data. Bioinformatics 18 (SMB2002) 355–366 104
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
Tinerey, L., Kadane, J.B.:Accurate approximations for posterior moments and marginal densities. J.Amer.Statist.Assoc. 81 (1986) 82–86 108
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2003 Springer-Verlag Berlin Heidelberg
About this paper
Cite this paper
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: Priami, C. (eds) Computational Methods in Systems Biology. CMSB 2003. Lecture Notes in Computer Science, vol 2602. Springer, Berlin, Heidelberg. https://doi.org/10.1007/3-540-36481-1_9
Download citation
DOI: https://doi.org/10.1007/3-540-36481-1_9
Published:
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-00605-3
Online ISBN: 978-3-540-36481-8
eBook Packages: Springer Book Archive