Synthetic network reconstruction errors for varying Gaussian kernel widths. The total number of inferred errors (NFP + NFN) in reconstructing the Mendes networks is stable with respect to choice of estimator kernel width, validating the observation that rankings of MIs are more stable than the MI estimates with respect to changes in this parameter (Figure 1). The choice of kernel width for each number of samples that minimizes the mean absolute MI estimation error for bivariate Gaussian densities (indicated with diamonds) yields optimal or near optimal reconstruction of this network for all samples sizes. Results are calculated for a statistical significance threshold of 10-4 for the scale-free network topology.
Margolin et al. BMC Bioinformatics 2006 7(Suppl 1):S7 doi:10.1186/1471-2105-7-S1-S7