License: Creative Commons Attribution 3.0 Unported license (CC BY 3.0)
When quoting this document, please refer to the following
DOI: 10.4230/OASIcs.GCB.2013.46
URN: urn:nbn:de:0030-drops-42273
URL: http://dagstuhl.sunsite.rwth-aachen.de/volltexte/2013/4227/
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Fröhlich, Holger ; Klau, Gunnar W.

Reconstructing Consensus Bayesian Network Structures with Application to Learning Molecular Interaction Networks

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p046-froehlich.pdf (0.5 MB)

Abstract

Bayesian Networks are an established computational approach for data driven network inference. However, experimental data is limited in its availability and corrupted by noise. This leads to an unavoidable uncertainty about the correct network structure. Thus sampling or bootstrap based strategies are applied to obtain edge frequencies. In a more general sense edge frequencies can also result from integrating networks learned on different datasets or via different inference algorithms. Subsequently one typically wants to derive a biological interpretation from the results in terms of a consensus network. We here propose a log odds based edge score on the basis of the expected false positive rate and thus avoid the selection of a subjective edge frequency cutoff. Computing a score optimal consensus network in our new model amounts to solving the maximum weight acyclic subdigraph problem. We use a branch-and-cut algorithm based on integer linear programming for this task. Our empirical studies on simulated and real data demonstrate a consistently improved network reconstruction accuracy compared to two threshold based strategies.

BibTeX - Entry

@InProceedings{frhlich_et_al:OASIcs:2013:4227,
  author =	{Holger Fr{\"o}hlich and Gunnar W. Klau},
  title =	{{Reconstructing Consensus Bayesian Network Structures with Application to Learning Molecular Interaction Networks}},
  booktitle =	{German Conference on Bioinformatics 2013},
  pages =	{46--55},
  series =	{OpenAccess Series in Informatics (OASIcs)},
  ISBN =	{978-3-939897-59-0},
  ISSN =	{2190-6807},
  year =	{2013},
  volume =	{34},
  editor =	{Tim Bei{\ss}barth and Martin Kollmar and Andreas Leha and Burkhard Morgenstern and Anne-Kathrin Schultz and Stephan Waack and Edgar Wingender},
  publisher =	{Schloss Dagstuhl--Leibniz-Zentrum fuer Informatik},
  address =	{Dagstuhl, Germany},
  URL =		{http://drops.dagstuhl.de/opus/volltexte/2013/4227},
  URN =		{urn:nbn:de:0030-drops-42273},
  doi =		{10.4230/OASIcs.GCB.2013.46},
  annote =	{Keywords: Bayesian Networks, Network Reverse Engineering, Minimum Feedback Arc Set, Maximum Acyclic Subgraph, Molecular Interaction Networks}
}

Keywords: Bayesian Networks, Network Reverse Engineering, Minimum Feedback Arc Set, Maximum Acyclic Subgraph, Molecular Interaction Networks
Collection: German Conference on Bioinformatics 2013
Issue Date: 2013
Date of publication: 09.09.2013

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