Clustering Based Approach to Identify Solutions for the Inference of Regulatory Networks

Christian Spieth, Felix Streichert, Nora Speer, and Andreas Zell

Clustering Based Approach to Identify Solutions for the Inference of Regulatory Networks

Proceedings of the IEEE Congress on Evolutionary Computation (CEC 2005)

Abstract

In this paper we address the problem of finding valid solutions for the problem of inferring gene regulatory networks. Different approaches to directly infer the dependencies of gene regulatory networks by identifying parameters of mathematical models can be found in literature. The problem of reconstructing regulatory systems from experimental data is often multi-modal and thus appropriate optimization strategies become necessary. Thus, we propose to use a clustering based niching evolutionary algorithm to maintain diversity in the optimization population to prevent premature convergence and to raise the probability of finding the global optimum by identifying multiple alternative networks. With this set of alternatives, the identification of the true solution has then to be addressed in a second post-processing step.

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BibTeX

@INPROCEEDINGS{spieth05clustering,
    author = {Christian Spieth and Felix Streichert and Nora Speer and Andreas Zell},
    title = {Clustering Based Approach to Identify Solutions for the Inference of Regulatory Networks},
    booktitle = {Proceedings of the IEEE Congress on Evolutionary Computation (CEC 2005)},
    year = {2005},
}