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. 2018 Jul 24;8:11173. doi: 10.1038/s41598-018-29433-3

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© The Author(s) 2018

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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Edges appearing in more than 50% of 1000 Bayesian Network reconstructions based on random sub-samples of the data. Line thickness is proportional to the relative frequency of observing an edge in the 1000 network reconstrucions, and the corresponding number is shown as edge label. The node size is proportional to the relative influence of the variable in the final GBM model, and the color reflects the selection frequency in the repeated cross-validation procedure (more black = higher stability). Sub-figures (A) and (B) depict two examples zooms into the overall network.