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. 2020 Oct 27;22:113. doi: 10.1186/s13058-020-01351-1

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© The Author(s) 2020

Open AccessThis 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 licence, and indicate if changes were made. The images or other third party material in this article are included in the article's Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article's Creative Commons licence 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 licence, visit http://creativecommons.org/licenses/by/4.0/. 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 in a credit line to the data.

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Fig. 6 — Schematic representation of the C 5.0 decision tree that gave the best model to predict TN BrCa patients from other BrCa patients. The schematic flowchart of the C5 decision tree is being used as a classification model to predict BrCa subtypes. The C5 decision tree is applied to discriminate TN cases from non-TN cases (i.e., luminal and HER2-enriched). The predictors used to sort the cases into various groups were 3HAA, AA/KYN ratio, 3HK, K/T ratio, and 3HK/KYN ratio. This was done in a hierarchical order of importance, calculated by the analytical software (data not shown). The purpose of the model is to completely segregate all the TN cases from the non-TN cases using cutoff values from the computed predictors that are iterated by the C5 decision tree algorithm. For example, a BrCa patient with a AA/KYN ratio of less than or equal to 2540.6 (#), a 3HAA concentration was greater than 22.68 nM (#), a 3HK/KYN ratio was less than or equal to 7516.96 (#), a 3HK was less than or equal to 136.84 nM (#), and a K/T ratio greater than 55.47 (#), then the model predicted them to have non-triple-negative cancer with an accuracy of 95.2%