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. 2019 Nov 11;9:16526. doi: 10.1038/s41598-019-52937-5

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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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Confusion matrices of tissue, disease, and disease sub-type classification. (a) Confusion matrix of tissue classification, in which the columns and rows represent the real and predicted tissue types, respectively. Each cell contains zero or some positive number of samples, and the diagonal and other cells represent correct and incorrect classifications, respectively. (b) Confusion matrix for classification of the disease type. (c) Confusion matrices for classification of cancer types/subtypes that originate from the same tissue and should be distinguished for appropriate clinical management. All tissues of the dataset having more than one cancer type are presented.