GESOPGraphical Environment for Simulation and Optimization (Astos Solutions GmbH)
GESOPGlobal Employee Stock Ownership Plan
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Regarding the size of the networks, results show that the networks optimized by GeSOp have reduced their size from 81,81% to 98.25%, in terms of number of edges.
Once it has been shown that GeSOp is capable of carrying out a reduction in the size of gene networks, it is also important to check if these optimized networks keep the ratio of biological information that they originally contained.
GeSOp is able to reduce considerably the size of the networks (e.g., -85.68% for Kendall's network and -89.46% for Spearman's), but the case of SU's network is remarkable.
In conclusion, the results obtained by both experiments show how GeSOp is able to perform a pruning process on large networks, by reducing their size while keeping their ratio of biological information.
In this section, the ability of GeSOp to improve the topology of gene networks is analyzed.
Based on this assumption, we present a topological analysis of some of the networks optimized by GeSOp in the previous section.
The results presented in Table 7 show that the networks improve their topological indicators once they are processed by GeSOp. Moreover, it is possible to argue that these networks follow a biological pattern according to [36].
The results generated by this second experiment probes that GeSOp is a reliable method to improve the topological features of the gene networks, in terms of biological structure.
In this work, a new backward elimination method for optimization of large gene networks structure, namely, GeSOp, has been presented.
On the other hand, topological analyses carried out in the experiments show how networks optimized by GeSOp improve their biological indicators by acquiring a scale-free topology.
Caption: Figure 1: GeSOp method is composed of two different steps: 1.
On (b), the final network obtained with GeSOp is depicted.