Statistical and Machine Learning Approaches for Network Analysis

5.4 Conclusion and Summary

Our study shows that the choice of the discretization method and MI estimator has a crucial influence on the inference performance of C3NET. In detail, the equal width and global equal width showed the best performance in combination with the Miller–Madow estimator. However, the major influence on the C3NET inference performance was observed for the discretization methods, where equal width and global equal width discretization markedly outperforms the equal frequency discretization.

In the study conducted by Olsen et al. [16], the influence of discretization, the mutual information estimator, sample size, and network size was studied for the ARACNE, CLR, and MRNET GRN inference algorithms. In contrast to our results, the equal frequency discretization was observed to outperform the equal width discretization for the used inference algorithms. In addition, the discrete estimators did not show a large difference as seen in our study, for example, for Miller–Madow.

The results suggest that the influence of the MI estimator on the global inference performance is highly dependent on the inference algorithm used. It is, therefore, a prerequisite to test GRN inference algorithms individually for different discretization and mutual information estimators.

Global error measures quantify the average inference performance for all edges in a network. Local measures allow to zoom-in the inference performance of individual parts of the network, down to individual ...