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Table 2 Comparison of chemotherapy response prediction performance for XGBoost models trained with VAE-derived features versus autoencoder (AE)-derived features, for three cancer types (BRCA, BLCA, and PAAD)

From: Predicting chemotherapy response using a variational autoencoder approach

Cancer type Mean p (Welch’s t-test)
AUROC AUPRC AUROC AUPRC
VAE AE VAE AE VAE versus AE VAE versus AE
BRCA 0.674 0.575 0.192 0.137 \(1.61 \times 10^{-15}\) \(5.38 \times 10^{-10}\)
PAAD 0.738 0.660 0.764 0.695 \(3.46 \times 10^{-10}\) \(6.72 \times 10^{-7}\)
BLCA 0.659 0.573 0.649 0.577 \(7.97 \times 10^{-12}\) \(1.23 \times 10^{-7}\)
SARC 0.704 0.611 0.736 0.654 \(2.78 \times 10^{-7}\) \(1.75 \times 10^{-6}\)
  1. The p values are for row-wise difference of means tests for the two columns under “AUROC” and for the two columns under “AUPRC”, respectively. For each cancer type (row), the highest mean AUROC performance is shown in boldface