Figure 11.

Comparison with unsupervised methods, ARACNE and CLR in simulated data. Average F-Measure at different percentage of known positives. The figure shows the difference between supervised and unsupervised methods obtained in the context of simulated data. The performance of supervised methods increases with the percentage of known positive examples. Instead, the performance of unsupervised information theoretic methods decreases with the number of genes in a regulatory network and is of course independent from the percentage of known positive examples. The intersection between supervised and unsupervised curves occur at different percentage of known positives and decreases with the number of genes composing the network.