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. 2020 Jan 27;10:1233. doi: 10.1038/s41598-019-56444-5

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© The Author(s) 2020

Open Access This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made. The images or other third party material in this article are included in the article’s Creative Commons license, unless indicated otherwise in a credit line to the material. If material is not included in the article’s Creative Commons license and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this license, visit http://creativecommons.org/licenses/by/4.0/.

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Model performance evaluation using ROC curves (in silico study). The mean ROC curve corresponding to DynGLasso (red line) is closer to the upper left corner than the model GLasso (blue line), indicating a higher accuracy in the network estimation. The mean ROC curves are estimated using 20 synthetic time-series datasets, and the error bars correspond to their standard deviation. The estimated AUROC (in the legend) shows that the performance of DynGLasso is higher than GLasso and the low standard deviation of AUROC indicates a higher robustness of DynGLasso predictions.