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. 2018 Jun 11;8:8857. doi: 10.1038/s41598-018-27214-6

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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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Cell line- and drug-centric correlation analyses. (a) Prediction accuracy assessment for each cell line. Scatter plots show the correlation between observed and CDRscan-predicted ln(IC₅₀) values for the cell lines that showed the strongest (BFTC-909, left) and the lowest agreement (COR-L32, right). The COSMIC IDs of the two cell lines and the corresponding cancer types are indicated above the scatter plots, and the R² values, Pearson correlation coefficient (r), p values, and the number of instances (n) are shown in the upper left corner of each plot. Histograms on the right show the overall distribution of prediction accuracy assessed for individual cell lines using indicated metrics. (b) Scatter plots showing the strongest and weakest agreement between observed and CDRscan-predicted ln(IC₅₀) in drug-centric correlation analysis. The drug name and its PubCHEM ID are indicated in each plot. The R² values, Pearson correlation coefficient (r), p values, and the instance counts (n) are also indicated. Histograms on the right show the overall distribution of prediction accuracy (R²) assessed for individual drugs using indicated metrics.