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. 2018 Sep 3;8:13149. doi: 10.1038/s41598-018-31573-5

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© The Author(s) 2018

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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KBCP ER+ and ER− classification using the BC risk-predictive SNPs identified by the proposed and the baseline methods. SNPs are sorted on the x-axis based on their importance score from the highest to the lowest provided by an XGBoost model in discriminating ER+ and ER− BC cases for all the methods. XGBoost ranking discards SNPs, which do not contribute to the ER subtype classification. Increasing the number of top-ranked SNPs improves the ER+ and ER− classification accuracy. The improvement is more prominent for the SNPs identified from the proposed method. Overall accuracy denotes the percentage of correctly classified instances. L1, L2 and elastic net SNPs relate to the identified SNPs from the penalized logistic regression respectively with L1, L2 and elastic net penalties.