Comparing Mathematical Models on the Problem of Network Inference

C. Spieth, N. Hassis, F. Streichert, J. Supper, N. Speer, K. Beyreuther, and A. Zell

Comparing Mathematical Models on the Problem of Network Inference

Proceedings of the Genetic and Evolutionary Computation Conference (GECCO 2006)

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{spieth06models,
    author = {C. Spieth, N. Hassis, F. Streichert, J. Supper, N. Speer, K. Beyreuther, and A. Zell},
    title = {Comparing Mathematical Models on the Problem of Network Inference},
    booktitle = {Proceedings of the Genetic and Evolutionary Computation Conference (GECCO 2006)},
    year = {2006},
    pages = {},
}