For panels A,B,C we consider only the performance of MCZ, and use relative rank as an estimate of error. We compute relative rank in the following way. Denote by

the total number of possible regulatory interactions, and by

the rank that was given to each regulatory interaction,

. The relative rank of

is defined to be

. Error distributions of the predictions for the five networks are shown as black boxplots in panels A,B,C. Distributions of in-degree of the regulators, out-degree of the regulators, and median expression of the regulators are shown as gray boxplots in panels A,B,C, respectively.

**A**) There is no apparent relationship between relative rank (Error) and the in-degree of the regulators.

**B**) There is no apparent relationship between relative rank (Error) and the out-degree of the regulators.

**C**) Relative rank (Error) in network prediction increases as the median expression of the regulators decreases.

**D**) we show the relationship between median expression of the regulators and the performance in ranking regulatory interactions, in terms of AUPR, across all five networks. For MCZ a correlation of (

) exists between the TFs median expression and AUPR (shown in red), while for Resampling+MCZ there is a smaller correlation of (

) between the TFs median expression and AUPR (shown in purple).

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