Fig. 6: Application to 16 non-linear gene regulatory networks: including noise.

a Classification scheme for a distribution of parameter estimates. Going from left to right panels, the same parameter distribution with an actual (ground truth) value of positive (+), negative (−), or null (0), respectively, is estimated using different percentile windows centered on the median. The percentile “window” is the median value for the leftmost panel (rigorous classification), between 40th and 60^th percentile in the second panel, and between 10^th and 90^th percentile in the third panel (conservative classification). Going from rigorous to conservative (left to right), an intermediate between the two gives a good classification performance. b ROC curves across all parameters for all 16 FFL models. Different color lines are different noise levels. c Fraction of correctly classified model parameters for different noise levels broken down by FFL model type. d Fraction of each model parameter correctly classified for different noise levels broken down by parameter type.