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Structure Learning for Bayesian Networks as Models of Biological Networks

  • Antti Larjo
  • Ilya Shmulevich
  • Harri Lähdesmäki
Protocol
Part of the Methods in Molecular Biology book series (MIMB, volume 939)

Abstract

Bayesian networks are probabilistic graphical models suitable for modeling several kinds of biological systems. In many cases, the structure of a Bayesian network represents causal molecular mechanisms or statistical associations of the underlying system. Bayesian networks have been applied, for example, for inferring the structure of many biological networks from experimental data. We present some recent progress in learning the structure of static and dynamic Bayesian networks from data.

Key words

Static Bayesian networks Dynamic Bayesian networks Structure learning Active learning 

Notes

Acknowledgements

This work was supported by the Academy of Finland (application numbers 135320 and 213462, Finnish Programme for Centres of Excellence in Research 2006–2011), and FP7 EU project SYBILLA.

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Copyright information

© Springer Science+Business Media New York 2013

Authors and Affiliations

  • Antti Larjo
    • 1
  • Ilya Shmulevich
    • 2
  • Harri Lähdesmäki
    • 3
  1. 1.Department of Signal ProcessingTampere University of TechnologyTampereFinland
  2. 2.Institute for Systems BiologySeattleUSA
  3. 3.Department of Information and Computer Science, School of ScienceAalto UniversityAaltoFinland

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