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. 2019 May 21;9:7635. doi: 10.1038/s41598-019-44129-y

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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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Project development workflow. The full dataset of peptidomics profiles of BC patients (n = 98) was split into training (n = 48) used for model development and test set (n = 50) retained for validation. Feature selection was performed through Cox regression analysis (10 resampling permutations) with 36 peptides found significantly predictive of the relapse (p-value < 0.1). Those were further used in the development of Random Forest-based predictive model of BC relapse. Performance of the model was evaluated on the test set and further optimized.