A memetic inference method for gene regulatory networks based on S-Systems
In this paper, we address the problem of finding gene regulatory networks from experimental DNA microarray data. As underlying mathematical model we used S-Systems, a quantitative model, which recently has found increased attention in the literature. Due to the complexity of the inference problem so...
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Published in | Proceedings of the 2004 Congress on Evolutionary Computation (IEEE Cat. No.04TH8753) Vol. 1; pp. 152 - 157 Vol.1 |
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Main Authors | , , , |
Format | Conference Proceeding |
Language | English |
Published |
Piscataway NJ
IEEE
2004
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Abstract | In this paper, we address the problem of finding gene regulatory networks from experimental DNA microarray data. As underlying mathematical model we used S-Systems, a quantitative model, which recently has found increased attention in the literature. Due to the complexity of the inference problem some researchers suggested evolutionary algorithms for this purpose. We introduce enhancements to this optimization process to infer the parameters of sparsely connected non-linear systems given by the observed data more reliably and precisely. Due to the limited number of available data the inferring problem is under-determined and ambiguous. Further on, the problem often is multi-modal and therefore appropriate optimization strategies become necessary. In this paper, we propose a new method, which evolves the topology as well as the parameters of the mathematical model to find the correct network. This method is compared to standard algorithms found in the literature. |
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AbstractList | In this paper, we address the problem of finding gene regulatory networks from experimental DNA microarray data. As underlying mathematical model we used S-Systems, a quantitative model, which recently has found increased attention in the literature. Due to the complexity of the inference problem some researchers suggested evolutionary algorithms for this purpose. We introduce enhancements to this optimization process to infer the parameters of sparsely connected non-linear systems given by the observed data more reliably and precisely. Due to the limited number of available data the inferring problem is under-determined and ambiguous. Further on, the problem often is multi-modal and therefore appropriate optimization strategies become necessary. In this paper, we propose a new method, which evolves the topology as well as the parameters of the mathematical model to find the correct network. This method is compared to standard algorithms found in the literature. |
Author | Streichert, F. Speer, N. Zell, A. Spieth, C. |
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Keywords | Genetic algorithm Process planning Evolutionary algorithm DNA chip Observation data Inference Topology Gene expression Bioinformatics Modeling Optimization Quantitative analysis |
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Snippet | In this paper, we address the problem of finding gene regulatory networks from experimental DNA microarray data. As underlying mathematical model we used... |
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SubjectTerms | Applied sciences Artificial intelligence Bioinformatics Biological system modeling Biological systems Computer science; control theory; systems DNA Exact sciences and technology Gene expression Mathematical model Probes Proteins Semiconductor device measurement Systems biology |
Title | A memetic inference method for gene regulatory networks based on S-Systems |
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