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. 2017 Jun 29;9:59. doi: 10.1186/s13073-017-0446-9

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© The Author(s). 2017

Open AccessThis article is distributed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided 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 Creative Commons Public Domain Dedication waiver (http://creativecommons.org/publicdomain/zero/1.0/) applies to the data made available in this article, unless otherwise stated.

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Fig. 2 — Tenfold cross-validation. We generated 1000 training sets, each containing 700 randomly selected somatic and 700 germline variants from each cancer set. ISOWN validation was done using different machine learners (shown with different colors). Plot shows average F1-measure (upper panel), false positive rate (middle panel) and AUC (lower panel) from 1000 training sets