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. 2019 Feb 18;47(8):e45. doi: 10.1093/nar/gkz096

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© The Author(s) 2019. Published by Oxford University Press on behalf of Nucleic Acids Research.

This is an Open Access article distributed under the terms of the Creative Commons Attribution Non-Commercial License (http://creativecommons.org/licenses/by-nc/4.0/), which permits non-commercial re-use, distribution, and reproduction in any medium, provided the original work is properly cited. For commercial re-use, please contact journals.permissions@oup.com

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Figure 5. — Fraction of predicted driver genes for each method by consensus among the methods. The average fraction of predicted driver genes for each method was determined by consensus among the other methods for the 31 datasets. Tools were sorted by the fraction of uniquely predicted drivers (indicated in red) from small to large. OncodriveCLUST was removed because it did not predicted any unique driver genes on 31 data sets. MEMo was also removed because it predicted too few genes. DriverML, MutSigCV and MDPFinder had the smallest fractions of uniquely predicted driver genes (2.5%, 5.2% and 10.8%, respectively). iPAC, SCS and CoMDP had the highest fractions (73.8%, 66.7% and 61.6%, respectively).