Numerical Recipes in C: The Art of Scientific Computing
Numerical Recipes in C: The Art of Scientific Computing
Combining Microarrays and Biological Knowledge for Estimating Gene Networks via Bayesian Networks
CSB '03 Proceedings of the IEEE Computer Society Conference on Bioinformatics
Comparing evolutionary algorithms on the problem of network inference
Proceedings of the 8th annual conference on Genetic and evolutionary computation
Comparing evolutionary algorithms on the problem of network inference
Proceedings of the 8th annual conference on Genetic and evolutionary computation
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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.