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. 2020 Apr 14;10:6394. doi: 10.1038/s41598-020-62636-1

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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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The effect of the horizon cut-off $h$ on the performance of Algorithm 1. We report the average significant ranks at $p = 0.05$ using different values of $h$ . In (a–c), we show the ranks based on the area under the PR curve (AUPR), the area under the ROC curve (AUROC) and top-precision (TPR) respectively. These results are obtained using 40 small networks. The average top-precision significant ranks on a set of 40 large networks are shown in (d).