Comparing Mathematical Models on the Problem of Network Inference

Abstract

In this paper we address the problem of finding gene regulatory networks from experimental DNA microarray data. We focus on the evaluation of the performance of different mathematical models on the inference problem. They are used to model the underlying dynamic system of artificial regulatory networks. The dynamics of the artificial systems represent different basic types of behavior, dimensionality and mathematical properties. They are all created with three commonly used approaches, namely linear weight matrices, H-systems, and S-systems. Due to the complexity of the inference problem, some researchers suggested evolutionary algorithms for this purpose. However, in many publications only one algorithm is used without any comparison to other optimization methods. Thus, we introduce a framework to systematically apply evolutionary algorithms for further comparative analysis.

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BibTeX

@inproceedings{2006_spieth06comparing,
  author = {Spieth, Christian and Hassis, Nadine and Streichert, Felix and Supper,
	Jochen and Speer, Nora and Beyreuther, Klaus and Zell, Andreas},
  title = {Comparing Mathematical Models on the Problem of Network Inference},
  booktitle = {Genetic and Evolutionary Computation Conference (GECCO 2006)},
  year = {2006},
  series = {Lecture Notes in Computer Science},
  pages = {305-306},
  address = {Seattle, USA},
  publisher = {Springer},
  abstract = {In this paper we address the problem of finding gene regulatory networks
	from experimental DNA microarray data. We focus on the evaluation
	of the performance of different mathematical models on the inference
	problem. They are used to model the underlying dynamic system of
	artificial regulatory networks. The dynamics of the artificial systems
	represent different basic types of behavior, dimensionality and mathematical
	properties. They are all created with three commonly used approaches,
	namely linear weight matrices, H-systems, and S-systems. Due to the
	complexity of the inference problem, some researchers suggested evolutionary
	algorithms for this purpose. However, in many publications only one
	algorithm is used without any comparison to other optimization methods.
	Thus, we introduce a framework to systematically apply evolutionary
	algorithms for further comparative analysis.},
  url = {http://www.cogsys.cs.uni-tuebingen.de/publikationen/2006/spieth06models.pdf}
}