Abstract
Bayesian Networks (BNs) are applied to a wide range of applications. In the past few years great interest is dedicated to the problem of inferring the structure of BNs solely from the data. In this work we explore a probabilistic method which enables the inclusion of extra knowledge in the inference of BNs. We briefly present the theory of BNs and introduce our probabilistic model. We also present the method of Markov Chain Monte Carlo (MCMC) which is used to sample network structures and hyper-parameters of our probabilistic model. Finally we present and discuss the results focusing on aspects related with the accuracy of the reconstructed networks and how the proposed method behaves when provided with sources of knowledge of different quality.
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References
Heckerman, D.: Learning Gaussian networks. Technical Report MSR-TR-94-10, Microsoft Research, Redmond, Washington (July 1994)
Heckerman, D.: A tutorial on learning with Bayesian networks. Technical Report MSR-TR-95-06, Microsoft Research, Redmond, Washington (1995)
Madigan, D., York, J.: Bayesian graphical models for discrete data. International Statistical Review 63, 215–232 (1995)
Hastings, W.K.: Monte Carlo sampling methods using Markov chains and their applications. Biometrika 57, 97–109 (1970)
Husmeier, D., Dybowski, R., Roberts, S.: Probabilistic Modeling in Bioinformatics and Medical Informatics. In: Advanced Information and Knowledge Processing. Springer, New York (2005)
Imoto, S., Higuchi, T., Goto, T., Tashiro, K., Kuhara, S., Miyano, S.: Combining microarrays and biological knowledge for estimating gene networksvia Bayesian networks. In: Proceedings IEEE Computer Society Bioinformatics Conference (CSB 2003), pp. 104–113 (2003)
Werhli, A.V., Husmeier, D.: Reconstructing gene regulatory networks with Bayesian networks by combiningexpression data with multiple sources of prior knowledge. Statistical Applications in Genetics and Molecular Biology, Article 15 6(1) (May 2007)
Friedman, N., Linial, M., Nachman, I., Pe’er, D.: Using Bayesian networks to analyze expression data. Journal of Computational Biology 7(3/4), 601–620 (2000)
Friedman, N., Koller, D.: Being Bayesian about network structure. Machine Learning 50, 95–126 (2003)
Husmeier, D.: Sensitivity and specificity of inferring genetic regulatory interactionsfrom microarray experiments with dynamic Bayesian networks. Bioinformatics 19, 2271–2282 (2003)
Werhli, A.V., Husmeier, D.: Gene regulatory network reconstruction by Bayesian integration of prior knowledge and/or different experimental conditions. Journal of Bioinformatics and Computational Biology 6, 543–572 (2008)
Heckerman, D.: A tutorial on learning with Bayesian networks. In: Jordan, M.I. (ed.) Learning in Graphical Models. Adaptive Computation and Machine Learning, pp. 301–354. MIT Press, Cambridge (1999)
Geiger, D., Heckerman, D.: Learning Gaussian networks. In: de Mantaras, R.L., Poole, D. (eds.) Uncertainty in Artificial Intelligence, pp. 235–243. Morgan Kaufmann, San Francisco (July 1994)
Yuh, C.H., Bolouri, H., Davidson, E.H.: Genomic cis-regulatory logic: experimental and computational analysis ofa sea urchin gene. Science 279, 1896–1902 (1998)
Yuh, C.H., Bolouri, H., Davidson, E.H.: Cis-regulatory logic in the endo16 gene: switching from a specificationto a differentiation mode of control. Development 128, 617–629 (2001)
Werhli, A.V., Grzegorczyk, M., Husmeier, D.: Comparative evaluation of reverse engineering gene regulatory networks withrelevance networks, graphical Gaussian models and Bayesian networks. Bioinformatics 22(20), 2523–2531 (2006)
Sachs, K., Perez, O., Pe’er, D., Lauffenburger, D., Nolan, G.P.: Causal protein-signaling networks derived from multiparameter single-celldata. Science 308(5721), 523–529 (2005)
Pearl, J.: Causality: Models, Reasoning and Intelligent Systems. Cambridge University Press, London (2000)
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Werhli, A.V. (2010). Bayesian Network Structure Inference with an Hierarchical Bayesian Model. In: da Rocha Costa, A.C., Vicari, R.M., Tonidandel, F. (eds) Advances in Artificial Intelligence – SBIA 2010. SBIA 2010. Lecture Notes in Computer Science(), vol 6404. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-16138-4_10
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DOI: https://doi.org/10.1007/978-3-642-16138-4_10
Publisher Name: Springer, Berlin, Heidelberg
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