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. 2023 Sep 28;2(10):917–936. doi: 10.1038/s44161-023-00338-3

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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 — ROC curves (associated AUCs and accuracy) indicating Random forest model performance in classifying patients with compensated or decompensated RV in each cohort independently. P-value has been calculated using one-sided Mann-Whitney (Wilcoxon-based) test for the H0: AUC = 0.5, and not corrected for multiple comparison as not applicable here. (a–p) Different combinations of proteins from panel of five has been tested to find the most effective features in prediction of RV states. Combination of NID1 + C1QTNF1 + CRTAC1 in German cohort (i) and NID1 + C1QTNF1 + MEG9 in validation cohort (o) are the most significant classifying features (highlighted in green).