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. 2020 Jun 12;10:9522. doi: 10.1038/s41598-020-66481-0

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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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True versus predicted test values from BUN level prediction. Scatter plots showing the true (x-axis) versus predicted (y-axis) test values using: (a) gene expression data as training data, (b) chemical structure information as training data and (c) both gene expression and chemical structure as training data. Scatter plots showing the true (x-axis) versus predicted (y-axis) test values using gene expression and chemical structure as training data after: (d) PCA using 57 components, (e) tSVD using 57 components and (d) tSVD h. All scatterplots show marginal histograms with regression and kernel density fits. The 95% confidence interval for the regression estimate is drawn using translucent bands around the regression line. Datapoint colour is according to the standard deviation of the predictive distribution per point; scales for colour vary between plots. Figure (f) shows our best model.