Fig. 4

Network inference results using ground truth data (without downsampling) from 100 different simulated random one regulation networks with 20 genes for 3 different network sparsities. Each network was simulated over 500 hours using parameters sampled as described in Methods sect. "Parameters for mammalian cells" A shows a barplot of the AUROC score for the 4 different network inference algorithms considered as well as a random classifier (RAND). B shows a barplot of the AUPR score for the 4 different network inference algorithms considered as well as a random classifier. Confidence intervals for barplots were computed by subsampling 35 out of 100 networks 100 times. C shows a boxplot of the true positives found for each network inference algorithm and random classifier for 3 different sparsity levels. The horizontal lines depict the actual number of true positives for reference. D shows a boxplot of the true negatives found for each network inference algorithm and random classifier for 3 different sparsity levels. Again, the horizontal lines depict the actual number of true negatives for reference. E shows a boxplot of the false positives found for each network inference algorithm and random classifier for 3 different sparsity levels. F shows a boxplot of the false negatives found for each network inference algorithm and random classifier for 3 different sparsity levels