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Conference Paper

Evaluating the effect of perturbations in reconstructing network topologies

MPS-Authors

Markowetz,  Florian
Max Planck Society;

http://pubman.mpdl.mpg.de/cone/persons/resource/persons50564

Spang,  Rainer
Dept. of Computational Molecular Biology (Head: Martin Vingron), Max Planck Institute for Molecular Genetics, Max Planck Society;

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Fulltext (public)

MarkowetzSpang.pdf
(Any fulltext), 524KB

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Citation

Markowetz, F., & Spang, R. (2003). Evaluating the effect of perturbations in reconstructing network topologies.


Cite as: http://hdl.handle.net/11858/00-001M-0000-0010-8B27-0
Abstract
Many different Bayesian network models have been suggested to reconstruct gene expression networks from microarray data. However, little attention has been payed to the effects of small sample size and the stability of the solution. We engage in a systematic investigation of these issues. As a starting point for further research we introduce the kappa-network. It is a small Bayesian network model (5 nodes with three states) in which a parameter kappa controls the conditional probability distributions of the nodes. With data sampled from this model, we evaluate the effects of different sample sizes and of data being derived from active perturbations on the reconstruction of the origninal network topology.