Comparing mathematical models on the problem of network inference
Proceedings of the 8th annual conference on Genetic and evolutionary computation
Comparing mathematical models on the problem of network inference
Proceedings of the 8th annual conference on Genetic and evolutionary computation
Automatic reverse engineering algorithm for drug gene regulating networks
ASC '07 Proceedings of The Eleventh IASTED International Conference on Artificial Intelligence and Soft Computing
IJCNN'09 Proceedings of the 2009 international joint conference on Neural Networks
EA'09 Proceedings of the 9th international conference on Artificial evolution
Parameter Estimation Using Metaheuristics in Systems Biology: A Comprehensive Review
IEEE/ACM Transactions on Computational Biology and Bioinformatics (TCBB)
IEEE/ACM Transactions on Computational Biology and Bioinformatics (TCBB)
Proceedings of the 15th 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 evolutionary algorithms on the inference problem. These algorithms are used to evolve an underlying quantitative mathematical model. The dynamics of the regulatory system are modeled with two commonly used approaches, namely linear weight matrices and S-systems and a novel formulation, namely H-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 and different types of mutation and crossover operators to the inference problem for further comparative analysis.