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. 2017 Dec 5;7:16954. doi: 10.1038/s41598-017-17031-8

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© The Author(s) 2017

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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Comparing kernel prediction with the established prognostic signatures. (a) Bar plots of C-index for the kernel machine learning method, an immune infiltration signature, a Metagene signature (capturing chromosome instability, mesenchymal transition, and lymphocyte immune recruitment), and data-driven selection of mRNA transcripts using Lasso-regression across cancer types. (b) Extended analysis of low grade glioma (LGG) including the IDH1, chromosome 1p/19q co-deletion prognostic subtypes. (c) Extended analysis of BRCA including the PAM50 classification and the MammaPrint signature.