The objective of the <i>in silico</i> network inference challenge was to infer networks of various sizes (10, 50, and 100 nodes) from steady-state and time-series “measurements” of simulated gene regulation networks.

<p>Predicted networks were evaluated on the basis of two scoring metrics, (<b>a</b>) area under the ROC curve and (<b>b</b>) area under the precision-recall curve. ROC and precision-recall curves of the five best teams in the 100-node sub-challenge. (<b>a</b>) Dotted diagonal line is the expected value of a random prediction. (<b>b</b>) Note that the best and second-best performers have different precision-recall characteristics. (<b>c</b>) Histograms (log scale) of the AUROC scoring metric for 100,000 random predictions was approximately Gaussian (fitted blue points) whereas the histogram of the AUPR metric was not (inset). Significance of the predictions of the teams (black points) was assessed with respect to the empirical probability densities embodied by these histograms. Scores of the best-performer team are denoted with arrows. All plots are analyses of the gold standard network called <i>InSilico_Size100_Yeast2</i>.