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. 2020 Sep 23;11:4809. doi: 10.1038/s41467-020-18396-7

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© The Author(s) 2020

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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Fig. 4 — a Overview of processing and elastic net regression analysis of transcriptomic data in combination with FAERS-derived clinical risk scores. b Transcriptomic signature genes selected to predict cardiotoxicity risk score indicating their variable importance. c Observed and predicted risk scores from the elastic net cross- validation analysis (mean and standard deviation). d External validation of the signature for six kinase inhibitors: regorafenib (REG), sunitinib (SUN), ibrutinib (IBR), lenvatinib (LEN), nintendinib (NIN), and ceritinib (CER).