Abstract
In this paper we address the problem of finding gene regulatory networks from experimental DNA microarray data. We introduce enhancements to an Evolutionary Algorithm optimization process to infer the parameters of the non-linear system 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. Therefore, we propose a new method, which will suggest necessary additional biological experiments to remove the ambiguities.
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Spieth, C., Streichert, F., Speer, N., Zell, A. (2004). Iteratively Inferring Gene Regulatory Networks with Virtual Knockout Experiments. In: Raidl, G.R., et al. Applications of Evolutionary Computing. EvoWorkshops 2004. Lecture Notes in Computer Science, vol 3005. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-24653-4_11
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DOI: https://doi.org/10.1007/978-3-540-24653-4_11
Publisher Name: Springer, Berlin, Heidelberg
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