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GECCO
2006
Springer

Comparing mathematical models on the problem of network inference

11 years 8 months ago
Comparing mathematical models on the problem of network inference
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. Categories and Subject Descriptors I.2 [Computing Methodologies]: ARTIFICIAL INTELLIGENCE--Miscellaneous; J.3 ...
Christian Spieth, Nadine Hassis, Felix Streichert
Added 23 Aug 2010
Updated 23 Aug 2010
Type Conference
Year 2006
Where GECCO
Authors Christian Spieth, Nadine Hassis, Felix Streichert
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