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. 2019 Feb 7;19:127. doi: 10.1186/s12885-019-5340-y

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© The Author(s). 2019

Open AccessThis 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. 2 — Analysis of GA/SVM-derived optimal feature sets for 100 runs generated by GA/SVM. a The average sensitivity for 100 generated predictor sets. b The average MCC (Matthew’s Correlation Coefficient) for 100 generated predictor sets [43]. c The prediction accuracies for 32 tumor classifications. d The average sensitivity of test-set samples predicted to be each of the 32 tumor types. X-axis and Y-axis list the actual and the predicted cancer type, respectively. The color of each cell in the heatmap is the average sensitivity of the test-set samples originally as the cancer type in X-axis to be predicted as the cancer type in Y-axis