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. 2017 Jun 6;7:2855. doi: 10.1038/s41598-017-03141-w

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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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Rank cutoff curves of top 100 candidates in mCGfinder (red line with circle markers), HotNet2 (green line with square markers) and ReMIC (blue line with triangle markers) results, describing the relation between various cutoffs and the fraction of known CGC cancer genes ranked above this cutoff in BRCA (A), BLCA (B), GBM (C) and HNSC (D). Cumulative fractions of known CGC cancer genes annotated by CGC within the top 100, 300, 500, 700 and 1000 genes in BRCA (E), BLCA (F), GBM (G) and HNSC (H). Results from all the assessments indicate the generally improved performance of mCGfinder over the competing methods.