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. 2017 Oct 18;7:13421. doi: 10.1038/s41598-017-13644-1

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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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Derivation of a minimal, multiply-redundant, methylation signature for medulloblastoma subgroup identification. Using a three-class (a) and two-class (b) subgrouping model, a minimal multiply-redundant methylation signature was identified by selecting a subset of signature candidates with the highest ranking in a classifier fusion model (support vector machine (SVM), artificial neural network (ANN), decision tree (DT) and Bayesian network (BN). (c) Heatmap of the most discriminatory signature candidates (n = 17) recapitulates the four known medulloblastoma subgroups. Chromosomal locations (hg19 assembly) and identifiers of the 17 CpG loci are reported in the adjacent table.