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. 2019 Jun 11;17:198. doi: 10.1186/s12967-019-1951-y

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© The Author(s) 2019

Open AccessThis article is distributed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided 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 Creative Commons Public Domain Dedication waiver (http://creativecommons.org/publicdomain/zero/1.0/) applies to the data made available in this article, unless otherwise stated.

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Fig. 2 — Survival analysis comparison of IHC-decision tree and machine learning algorithms. Overall survival of patients classified by Hans and the best five machine learning algorithms is shown. GCB (black) overall survival was significatively better than non-GCB (grey) cases for both VY subset and clinical sample set, except for Hans when used in clinical sample set. Numeric Tags 1 = CD10, 2 = BCL6, 3 = FOXP1, 4 = GCTE1, and 5 = MUM1. PV: Perfecto–Villela; BS: Bayesian simple; ANN: artificial neural networks; SVM: support vector machines