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. 2020 Nov 3;10:18951. doi: 10.1038/s41598-020-76025-1

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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/.

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Predictive performance for DFS and OS using various features. The performance of machine learning models for predicting (a) OS and (b) DFS based on various features were measured. The y-axis represents the accuracy or AUC values. Clinical, nine clinical data; KG, known cancer driver genes (KRAS, CDKN2A, TP53, and SMAD4); HR, a high-risk group of patients harboring mutations in five genes with high CP values; Sub, a feature representing subgroups generated by integrating mRNA, miRNA, and DNA methylation subtypes; AUC, area under the curve values from fivefold cross-validation.