Influence of Noise on the Inference of Dynamic Bayesian Networks from Short Time Series
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.
[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.
[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.
@article{"International Journal of Medical, Medicine and Health Sciences:55907", author = "Frank Emmert Streib and Matthias Dehmer and Gökhan H. Bakır and Max Mühlhauser", title = "Influence of Noise on the Inference of Dynamic Bayesian Networks from Short Time Series", 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.", volume = "1", number = "10", pages = "536-5", }