Adding Hidden Nodes to Gene Networks

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


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.


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


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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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