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 ...

• Type: Conference
• Year: 2006
• Where: GECCO
• Authors: Christian Spieth, Nadine Hassis, Felix Streichert
• Keywords: Evolutionary Algorithms, GECCO 2006, Inference Problem, Optimization, Regulatory Networks