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Fig. 4 | BMC Bioinformatics

Fig. 4

From: Parametric and non-parametric gradient matching for network inference: a comparison

Fig. 4

Performance comparison of network inference approaches using noise-free 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 distributions relate to the performance of the ODE methods with and without prior at inferring a directed GRN including information about interaction types (activation/repression) (T). The orange distributions depict the performance of the two ODE-based methods 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 four asymmetric distributions in the figure gives rise to the same distribution of model performance (constituted by the three approaches discussed earlier, i.e. the sum of distributions 1, 2 and 5 in Fig. 4A). The dashed line represents baseline (random) performance in all charts

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