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. 2019 Apr 2;9:5479. doi: 10.1038/s41598-019-41918-3

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© The Author(s) 2019

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 average differential networks estimated by each method based on 20 generated samples of size $n = 250$ . The cells are shaded based on how frequently the edge was identified as differentially connected in the 20 samples; darker cells indicate higher frequency. The true differential network is based on the absolute difference in the precision matrices of the two populations.