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. 2023 Jun 29;29(7):1804–1813. doi: 10.1038/s41591-023-02396-3

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© The Author(s) 2023

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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Extended Data Fig. 8 — This figure compares the area under the receiver operator characteristics curves (95% confidence interval) of 10 classifiers during training (left) and testing (right) on the derivation cohort. RF: random forest; KNN: K-nearest neighbors; GBM: gradient boosting machine; XGB: extreme gradient boosting; SVM: support vector machine; ANN: artificial neural networks; LogReg: regularized logistic regression; LDA: linear discriminant analysis; SGD_LogReg: stochastic gradient descent logistic regression; G_NB: Gaussian Naïve Bayes.