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Fig. 3 | BMC Systems Biology

Fig. 3

From: A tree-like Bayesian structure learning algorithm for small-sample datasets from complex biological model systems

Fig. 3

Sensitivity analysis of ffc shows less significant impact than random variability. The Child network was analyzed with 100 samples, 10 times each for ffc parameter values ranging from 0.2 to 0.4. The variability induced by changing ffc (range of TPR and FPR across all parameter values) is smaller than the variability from different random datasets being used for structure learning (error bars for any given ffc value). This suggests that there is a broad optimum of ffc values and that the value used in this work is a reasonable one (and perhaps not even optimal). TPR: true positive rate; FPR: false positive rate. Error bars represent one standard deviation

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