Influence of Noise on the Inference of Dynamic Bayesian Networks from Short Time Series

Authors: Frank Emmert Streib, Matthias Dehmer, Gökhan H. Bakır, Max Mühlhauser

Abstract:

In this paper we investigate the influence of external noise on the inference of network structures. The purpose of our simulations is to gain insights in the experimental design of microarray experiments to infer, e.g., transcription regulatory networks from microarray experiments. Here external noise means, that the dynamics of the system under investigation, e.g., temporal changes of mRNA concentration, is affected by measurement errors. Additionally to external noise another problem occurs in the context of microarray experiments. Practically, it is not possible to monitor the mRNA concentration over an arbitrary long time period as demanded by the statistical methods used to learn the underlying network structure. For this reason, we use only short time series to make our simulations more biologically plausible.

Keywords: Dynamic Bayesian networks, structure learning, gene networks, Markov chain Monte Carlo, microarray data.

Digital Object Identifier (DOI): doi.org/10.5281/zenodo.1332678

References:

[1] Buntine, W.L.: Operations for Learning with Graphical Models. Journal of Artificial Intelligence Research 2 (1994) 159-225.
[2] Chen, T., He., H.L., Church, G.M.: Modeling gene expression with differential equations. Pac. Symp. Biocomput. 4 (1999) 29-40.
[3] Friedman, N., Murphy, K., Russel, S.: Learning the Structure of Dynamic Probabilistic Networks. In Cooper, G.F. and Moral, S. (eds), Proceedings of the Fourteenth Conference on Uncertainty in Artifical Intelligence (UAI). Morgan Kaufmann Publishers, San Francisco, CA (1998).
[4] Friedman, N., Linial, M., Nachman, I., Pe-er, D.: Using Bayesian Networks to Analyze Expression Data. Journal of Computational Biology 7:3/4 (2000) 601-620.
[5] Gardner, T.S., di Bernardo, D., Lorenz, D., Collins, J.J.: Inferring Genetic Networks and Identifying Compound Mode of Action via Expression Profiling. Science 301 (2003) 102-105.
[6] Hartemink, A.J., Gifford, D., Jaakkola, T., Young, R.: Using graphical models and genomic expression data to statistically validate models of genetic regulatory networks. Pac. Symp. Biocomp. 6 (2001) 422-433.
[7] Hartemink, A.J.: Reverse engineering gene regulatory networks. Nature 23:5 (2005) 554-555.
[8] Husmeier, D.: Sensitivity and specificity of inferring genetic regulatory interactions from microarray experiments with dynamic Bayesian networks. Bioinformatics 19:17 (2003) 2271-2282.
[9] Musmeier, D.: Inferring Dynamic Bayesian Networks with MCMC (DBmcmc). www.bioss.sari.ac.uk/Ôê╝dirk/software/DBmcmc/ (2003).
[10] Jordan, M.I.: Learning in Graphical Models. MIT Press (1998).
[11] Lee, T.I. et al.: Transcriptional Regulatory Networks in Saccharomyces cerevisiae. Science 298 (2002) 799-804.
[12] Liu, J.S.: Monte Carlo Strategies in Scientific Computing. Springer- Verlag, New York (2001).
[13] Murphy, K.P., Mian, S.: Modelling Gene Expression Data using Dynamic Bayesian Networks, Technical Report (1999).
[14] Murphy, K.P.: Bayes Net Toolbox. Technical Report, MIT Artificial Intelligence Laboratory (2002).
[15] Perl, J.: Probabilistic Reasoning in Intelligent Systems: Networks of Plausible Inference. Morgan Kaufmann, San Francisco, CA, USA (1988).
[16] Schlitt, T., Brazma, A.: Modelling gene networks at different organizational levels. FEBS Letters 579 (2005) 1859-1866.
[17] Smith, V.A., Jarvis, E.D., Hartemink, A.J.: Evaluating functional network inference using simulations of complex biological systems. Bioinformatics 18 (2002) 164-175.
[18] Yu, J., Smith, V.A., Wang, P.P., Hartemink, A.J., Jarvis, E.D.: Advances to Bayesian network inference for generating causal networks from observational biological data. Bioinformatics 20:18 (2004) 3594-3603.