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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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Schematic diagram of the workflow for PE/control samples classification. Data is randomly divided into a training set and a test set. 5-fold cross validation procedure is used on the training set to obtain a logistic regression model that best classifies training-set samples, and then it is tested on the blind test set. This process is repeated a 100 times, each time with a random partition to training and test set, in order to increase the stability and generalization of the results, and to estimate the goodness of the procedure on a new blind data set. The classification accuracy in the blind test set and related statistics are calculated in each of the iterations, and are summarized for overall evaluation of the pipeline.