C. Spieth, N. Hassis, F. Streichert, J. Supper, N. Speer, K. Beyreuther, and A. Zell

**
Comparing Mathematical Models on the Problem of Network Inference**

*
Proceedings of the Genetic and Evolutionary Computation Conference (GECCO 2006)*

### Abstract

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.

**Download**

[pdf]

**BibTeX**

@INPROCEEDINGS{spieth06models,

author = {C. Spieth, N. Hassis, F. Streichert, J. Supper, N. Speer, K. Beyreuther, and A. Zell},

title = {Comparing Mathematical Models on the Problem of Network Inference},

booktitle = {Proceedings of the Genetic and Evolutionary Computation Conference (GECCO 2006)},

year = {2006},

pages = {},

}