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. 2022 Oct 25;11:e80150. doi: 10.7554/eLife.80150

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© 2022, Wang, Liu, Liang et al

This article is distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use and redistribution provided that the original author and source are credited.

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Figure 2. — (A) The C-indexes of 76 machine-learning algorithm combinations in the nine testing cohorts. (B) Discovery of 32 consensus prognosis genes from 10 independent multicenter cohorts. (C–J) The predictive performance of AIDPS was compared with common clinical and molecular variables in the PACA-AU-Array (C), TCGA-PAAD (D), PACA-AU-Seq (E), PACA-CA-Seq (F), E-MTAB-6134 (G), GSE62452 (H), GSE78229 (I), and GSE79668 (J). Z-score test: *p<0.05, **p<0.01, ***p<0.001, ****p<0.0001.

Figure 2—source data 1. The nine genes included in the artificial intelligence-derived prognostic signature (AIDPS).

elife-80150-fig2-data1.xlsx^{(12.3KB, xlsx)}