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Figure 4 | BMC Bioinformatics

Figure 4

From: Dynamic probabilistic threshold networks to infer signaling pathways from time-course perturbation data

Figure 4

Hyperparameter sensitivity analysis ERBB network. The Figure shows sensitivity of the AUC ROC (panel A) and AUC PR (panel B) of the ERBB network (with literature prior) with respect to changes in model hyperparameters. Model parameters μ, s, q, γ, r0 and s0 where changed by up to ±50%, and inference was repeated for each parameter choice. Shown are resulting values for the area under the receiver operator characteristic (AUC ROC ) and the area under the precision-recall (AUC PR ) curves. The analysis indicates that inference results are reasonably robust to changes in model hyperparameters, with the stochasticity parameter γ being the most critical parameter for inference performance.

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