Fig. 5

Performance comparison of network inference approaches using realistic simulated data. a This subfigure displays the distribution of obtained performance (AUPR) for the three different classes of network inference methods, over all model settings listed in Table 1. There are four different network inference aims shown in four different shades. The blue distribution relates to the performance of the ODE method without prior at inferring a directed GRN including information about interaction types (activation/repression) (T). The orange distributions depict the performance of the ODE-based method and the GP-based method at predicting a directed GRN without type information (D). The green distributions show the performance of the same three methods at inferring an undirected GRN (U). The performance of a recently developed algorithm [10] based on partial information decomposition for the same settings and data is shown as the last distribution in grey (“PIDC”). b This subfigure shows the impact of different settings choices on network inference performance. Summing the two halves of each of the first three asymmetric distributions in the figure (and the four parts of the distributions labeled “4”) gives rise to the same distribution of model performance (constituted by the two main approaches discussed earlier (GP and ODE) - i.e. the sum of distributions 1 and 5 in Fig. 5a).The dashed line represents baseline (random) performance in all charts