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Sensitivity Analysis for the Network Inference performance on synthetic data with respect to parameters

By Matthias Böck (323978), Soichi Ogishima (265727), Hiroshi Tanaka (3087), Stefan Kramer (323979) and Lars Kaderali (110095)


<p><b> and </b><b>.</b> Plots comparing distributions of AUC values for ROC graphs for different a and r settings (x- and y-axis), for the synthetic networks of sizes 11, 100 and 1000, using data sets with 20 and 200 time points, respectively. The plots show that results are relatively insensitive over a large range of parameters. Smaller values of the hyperparameter correspond to a more peaked prior distribution, resulting in “sparser” networks. Correspondingly, the figure shows that smaller values of should be chosen for larger networks. Although the effect of changing seems not as pronounced, larger values of correspond to a narrower prior distribution, and should therefore be used if fewer data are available to avoid overfitting.</p

Topics: Genetics, Biological Sciences, inference, synthetic, parameters
Year: 2013
DOI identifier: 10.1371/journal.pone.0035077.g008
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Provided by: FigShare
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