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. 2017 Jan 11;7:40019. doi: 10.1038/srep40019

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Copyright © 2017, The Author(s)

This work is licensed under a Creative Commons Attribution 4.0 International License. The images or other third party material in this article are included in the article’s Creative Commons license, unless indicated otherwise in the credit line; if the material is not included under the Creative Commons license, users will need to obtain permission from the license holder to reproduce the material. To view a copy of this license, visit http://creativecommons.org/licenses/by/4.0/

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A RBF kernel-based SVM was designed and 5-fold cross-validation strategy was utilized to evaluate the performance of SVM classifier. The outcome of prediction was then assessed by ROC curve analysis. (a,b) The ROC curves of SVM classifier of early CP. (a) Every DEmiR as a classification feature; (b) The combined DEmiRs were classification features based on the correlation. The diagonal line is the baseline; (c,d) Comparing the classification effect of biomarkers. c, prediction effect of early CP biomarkers in late CP (GEO CP, n = 38; GEO healthy controls, n = 38); (d) prediction effect of late CP biomarkers in early CP (early CP, n = 10; healthy control, n = 9).