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. 2018 Mar 15;9:1090. doi: 10.1038/s41467-018-03424-4

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© The Author(s) 2018

Open Access This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as 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 images or other third party material in this article are included in the article’s Creative Commons license, unless indicated otherwise in a credit line to the material. If material is not included in the article’s Creative Commons license and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this license, visit http://creativecommons.org/licenses/by/4.0/.

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Fig. 3 — Effect of automatic parameter estimation using four different cluster validity indices and two measures based on functional enrichment on the performance of top module detection methods. Shown are changes in test scores after parameter estimation (either using measures based on functional enrichment in blue or cluster validity indices in red–orange), averaged over datasets and module definitions, of the top module detection methods in every category