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. 2021 Dec 10;7:154. doi: 10.1038/s41523-021-00356-z

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© The Author(s) 2021, corrected publication 2022

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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Fig. 3 — a, b ROC for the performance of the exLR d-signature in the training (n = 120, a) and validation (n = 52, b) cohorts. c, d The diagnosis effects of the d-signature in the training (c) and validation (d) cohorts. e, f Unsupervised hierarchical clustering of eleven exLRs selected for use in the d-signature in the training (e) and validation (f) cohorts. Each column represents an individual sample, and each row represents an exLR. The scale represents the expression values. ROC receiver operating characteristic, AUC area under the curve, SD standard deviation, CI confidence interval.