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. 2010 Feb 19;38(11):3523–3532. doi: 10.1093/nar/gkq045

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© The Author(s) 2010. Published by Oxford University Press.

This is an Open Access article distributed under the terms of the Creative Commons Attribution Non-Commercial License (http://creativecommons.org/licenses/by-nc/2.5), which permits unrestricted non-commercial use, distribution, and reproduction in any medium, provided the original work is properly cited.

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Figure 2. — Benchmarking on simulated data set. Performance of the TfT, PCU, TopW, GenGO, MGSA and MGSA algorithms on simulated data set with different settings of false positive () and false negative () rates. In each row, the leftmost panel shows the precision for a recall of 0.2 (A, B), the middle panel precision as a function of recall (C, D) and the rightmost panel the ROC curve (E, F).

Inline graphic — Benchmarking on simulated data set. Performance of the TfT, PCU, TopW, GenGO, MGSA and MGSA algorithms on simulated data set with different settings of false positive () and false negative () rates. In each row, the leftmost panel shows the precision for a recall of 0.2 (A, B), the middle panel precision as a function of recall (C, D) and the rightmost panel the ROC curve (E, F).