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. 2022 Sep 27;13:5680. doi: 10.1038/s41467-022-33291-z

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© The Author(s) 2022

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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Fig. 5 — a Precision-recall curve representing the performance of the best AutoML model on the TCGA test dataset. b Survival analysis of a test dataset spanning multiple TCGA cancer types. Patients were classified into two groups based on the median value of the predicted probability of response with a P-value = 1.634292e-05 (log-rank test). We estimated the 5-years survival probability of group 1 as 0.72 and group 2 as 0.28. c–e Bar plots showing the probability of predicted response for drugs dabrafenib and trametinib in patient 1, patient 2, and patient 3, respectively. A red bar indicates the probability of response for dabrafenib and a purple bar indicates the probability of response for trametinib. Source data are provided in the Source Data file.