Supper, Jochen and Fröhlich, Holger and Spieth, Christian and Dräger, Andreas and Zell, Andreas

Inferring Gene Regulatory Networks by Machine Learning Methods

Proceedings of the 5th Asia-Pacific Bioinformatics Conference (APBC 2007), 57 Shelton Street, Govent Garden, London WC2H 9HE, UK, Series on Advances in Bioinformatics and Computational Biology vol. 5, Imperial College Press, 2007, pp. 247-256


Abstract

The ability to measure the transcriptional response after a stimulus has drawn much attention to the underlying gene regulatory networks. Several machine learning related methods, such as Bayesian networks and decision trees, have been proposed to deal with this difficult problem, but rarely a systematic comparison between different algorithms has been performed. In this work, we critically evaluate the application of multiple linear regression, SVMs, decision trees and Bayesian networks to reconstruct the budding yeast cell cycle network. The performance of these methods is assessed by comparing the topology of the reconstructed models to a validation network. This validation network is defined a priori and each interaction is specified by at least one publication. We also investigate the quality of the network reconstruction if a varying amount of gene regulatory dependencies is provided a priori.


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BibTeX

@inproceedings{Supper2007,
  author = {Supper, Jochen and Fr\"ohlich, Holger and Spieth, Christian and Dr\"ager,
	Andreas and Zell, Andreas},
  title = {Inferring Gene Regulatory Networks by Machine Learning Methods},
  booktitle = {Proceedings of the 5\textsuperscript{th} Asia-Pacific Bioinformatics
	Conference (APBC 2007)},
  year = {2007},
  editor = {Sankoff, David and Wang, Lusheng and Chin, Francis},
  volume = {5},
  series = {Series on Advances in Bioinformatics and Computational Biology},
  pages = {247--256},
  address = {57 Shelton Street, Govent Garden, London WC2H 9HE, UK},
  month = jan,
  publisher = {Imperial College Press},
  abstract = {The ability to measure the transcriptional response after a stimulus
	has drawn much attention to the underlying gene regulatory networks.
	Several machine learning related methods, such as Bayesian networks
	and decision trees, have been proposed to deal with this difficult
	problem, but rarely a systematic comparison between different algorithms
	has been performed. In this work, we critically evaluate the application
	of multiple linear regression, SVMs, decision trees and Bayesian
	networks to reconstruct the budding yeast cell cycle network. The
	performance of these methods is assessed by comparing the topology
	of the reconstructed models to a validation network. This validation
	network is defined \emph{a priori} and each interaction is specified
	by at least one publication. We also investigate the quality of the
	network reconstruction if a varying amount of gene regulatory dependencies
	is provided \emph{a priori}.},
  doi = {10.1142/9781860947995_0027},
  pdf = {http://www.cogsys.cs.uni-tuebingen.de/publikationen/2007/SupperAPBC2007_GRNs.pdf},
  url = {http://dx.doi.org/10.1142/9781860947995_0027}
}