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. 2020 Feb 5;10:1861. doi: 10.1038/s41598-020-58821-x

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© The Author(s) 2020

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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Evaluation of drug-resistance prediction performance of RefDNN on GDSC and CCLE datasets. (a) Results on GDSC dataset and (b) Results on CCLE dataset. The predictive power is computed by 5 metrics, accuracy, AUCROC, precision, recall, and F1score. A value on each bar is the average of accuracy values in the nested 5-fold cross-validation. An error bar represents the standard deviation in the cross validation. The significance of predictive performance differences between RefDNN and others was calculated using the Welch’s t-test and Benjamini-Hochberg procedure. Single and double asterisk symbols mean p < 0.05 and p < 0.01, respectively.