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Adding Hidden Nodes to Gene Networks

  • Benny Chor
  • Tamir Tuller
Part of the Lecture Notes in Computer Science book series (LNCS, volume 3240)

Abstract

Bayesian networks are widely used for modelling gene networks. We investigate the problem of expanding a given Bayesian network by adding a hidden node – a node on which no experimental data are given. Finding a good expansion (a new hidden node and its neighborhood) can point to regions where the model is not rich enough, and help locate new, unknown variables that are important for understanding the network. We study the computational complexity of this expansion, show it is hard, and describe an EM based heuristic algorithm for solving it. The algorithm was applied to synthetic datasets and to yeast gene expression datasets, and produces good, encouraging results.

Keywords

Bayesian networks minimum description length network expansion gene network EM maximum likelihood compression 

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References

  1. 1.
    Chickering, D.M.: Learning bayesian network is NP-complete. In: Fisher, D., Le, H.J. (eds.) Learning from data: AI and statistic V, pp. 121–130. Springer, New York (1996)Google Scholar
  2. 2.
    Friedman, N.: Inferring cellular networks using probabilistic graphical models. Science, 799–805 (2004)Google Scholar
  3. 3.
    Friedman, N., Linial, M., Nachman, I., Pe’er, D.: Using bayesian network to analyze expression data. Journal of Computational Biology (7), 601–620 (2000)Google Scholar
  4. 4.
    Friedman, N., Yakhini, Z.: On the sample complexity of learning bayesian networks. In: Proc. 12th Conf. on Uncertainty in Artificial Intelligence, Portland, OR, pp. 274–282 (1996)Google Scholar
  5. 5.
    Höffgen, K.L.: Learning and robust learning of product distributions. In: COLT, pp. 77 – 83 (1993)Google Scholar
  6. 6.
    Hugehes, T.R., Marton, M.J.: Functional discovery via a compendium of expression profiles. Cell 102(1), 109–126 (2000)CrossRefGoogle Scholar
  7. 7.
    Kwoh, C.K., Gillies, D.F.: Using hidden nodes in byesian networks. Artificial Intelligence 88, 1–38 (1996)zbMATHCrossRefGoogle Scholar
  8. 8.
    Pe’er, D., Regev, A., Elidan, G., Friedman, N.: Inferring subnetworks from perturbed expression profiles. Bioinformatics 1(1), 1–9 (2001)Google Scholar
  9. 9.
    Rissanen, J.: Modeling by shortest data description. Automatica 14, 465–471 (1978)zbMATHCrossRefGoogle Scholar
  10. 10.
    Segal, E., Shapira, M., Pe’er, D., Botstein, D., Koller, D., Friedman, N.: Module networks: identifying regulatory modules and their condition-specific regulators from gene expression data. Nature Genetics 34, 166–176 (2003)CrossRefGoogle Scholar
  11. 11.
    Tanay, A., Shamir, R.: Computational expansion of genetic networks. In: ISMB, pp. 1–9 (2001)Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2004

Authors and Affiliations

  • Benny Chor
    • 1
  • Tamir Tuller
    • 1
  1. 1.School of Computer ScienceTel-Aviv UniversityTel-AvivIsrael

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