We compare the inference performance of the C3NET  for four mutual information estimators. We simulated gene expression datasets for random graphs for different edge densities. We studied the global inference performance and structural characteristics of the inferred networks.
We study the influence of four different mutual information estimators on the C3NET network inference performance. We use the F-score measure to measure the performance for C3NET network inference from simulated gene expression data. First, we compared the impact of three discretization methods for each estimator. For the three network types the equal width and global equal width discretization showed the highest inference accuracy for C3NET compared to the equal frequency discretization.
The equal and global equal width discretization favor the Miller–Madow estimator followed by the empirical estimator to be most beneficial for the C3NET inference performance. The Schürmann–Grassberger and Shrink estimator perform worse. However, the Schürmann–Grassberger performs better than the Shrink estimator (Fig. 5.9). For the equal frequency discretization, we do not observe a substantial difference of the inference performance for the empirical, Miller–Madow, Shrink, and Schürmann–Grassberger estimator (Fig. 5.9).