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. 2015 Nov 1;22(11):1005–1024. doi: 10.1089/cmb.2015.0051

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© The Author(s) 2015; Published by Mary Ann Liebert, Inc.

This Open Access article is distributed under the terms of the Creative Commons Attribution Noncommercial License (http://creativecommons.org/licenses/by-nc/4.0/) which permits any noncommercial use, distribution, and reproduction in any medium, provided the original author(s) and the source are credited.

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FIG. 5. — Effect of noise on (a) mutual information, (b) , and (c) . The points of the curves are computed by applying the measures to a set of selected tuples of Example 1 (see legend). The first point of each curve corresponds to a measurement value computed on the data without noise. All other points are the values of corresponding measures computed on data with noise averaged across 100 repeats, and the error bars represent standard deviation. The x-axis shows the amount of noise for corresponding values of the measures and ranges from 0 to 50% with 2.5% incremental increases. Additionally, the black curve is an average across all tuples that were not selected (the background) and the error bars are maximal standard deviation across these tuples.

Inline graphic — Effect of noise on (a) mutual information, (b) , and (c) . The points of the curves are computed by applying the measures to a set of selected tuples of Example 1 (see legend). The first point of each curve corresponds to a measurement value computed on the data without noise. All other points are the values of corresponding measures computed on data with noise averaged across 100 repeats, and the error bars represent standard deviation. The x-axis shows the amount of noise for corresponding values of the measures and ranges from 0 to 50% with 2.5% incremental increases. Additionally, the black curve is an average across all tuples that were not selected (the background) and the error bars are maximal standard deviation across these tuples.