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. 2015 Jul 21;43(21):e147. doi: 10.1093/nar/gkv733

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© The Author(s) 2015. 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 License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted reuse, distribution, and reproduction in any medium, provided the original work is properly cited.

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Figure 7. — Distinguishing the 67 verified mutations from random ones. The results of three programs (i.e. BayesPI-BAR, CADD and FunSeq2) are compared. (A) The log10-transformed P-values of the rank-sum tests for the predicted scores of verified mutations versus those of randomly generated ones. Lower P-values indicate a more significant difference in predicted scores between the two sets of mutations. (B) The Z-values of the same tests as in Figure 7A. (C) The receiver operating characteristic (ROC) curves for the three programs show the performance of the programs as binary classifiers (i.e. functional versus random mutations) and the area under this curve (AUC) serves as a numeric measure. For a random-guessing classifier (‘Random’), the AUC of the theoretical ROC curve is 0.5.