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. 2017 Dec 21;11(Suppl 7):135. doi: 10.1186/s12918-017-0504-3

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© The Author(s) 2017

Open Access This 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. 4 — Performance comparison with eight existing protein complex prediction algorithms in terms of recall, precision, and F1-measure. Our method CPreditor3.0 achieves the highest F1-measure in five of the six experimental settings. (a) Results with Krogan as PPI dataset and MIPS as complex reference set, (b) Results with Krogan as PPI dataset and CYC2008 as complex reference set, (c) Results with Collins as PPI dataset and MIPS as complex reference set, (d) Results with Collins as PPI dataset and CYC2008 as complex reference set, (e) Results with WI-PHI as PPI dataset and MIPS as complex reference set, (f) Results with WI-PHI as PPI dataset and CYC2008 as complex reference set