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. 2018 Feb 21;8:3401. doi: 10.1038/s41598-018-21604-6

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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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Classification results on real and permutated data sets. Density plots of statistical measures obtained by 100 iterations of our classification procedure on real (blue) and permutated (red) data sets. Real dataset included 35 PE and 40 control samples. Permutated dataset included the same samples after random shuffling of their conditions (i.e., PE/control). Means are indicated as well. Sensitivity: true positives out of all positives; Specificity: true negatives out of all negatives; Accuracy: true classifications out of all classifications; Matthews’s correlation coefficient (MCC): a correlation coefficient between the observed and predicted binary classifications; AUC: area under the ROC curve; F1 Score: the harmonic mean of precision and sensitivity; Positive Likelihood Ratio: sensitivity/(1-specificity); Negative Likelihood Ratio: (1-sensitivity)/specificity.