Table 4 Comparison of chemotherapy response prediction performance (AUPRC) for XGBoost models trained with original transcriptome data (“Raw data”) or transcriptome data encoded with PCA, ICA, or VAE. This analysis was carried out across five cancers (BRCA, COAD, BLCA, PAAD, and SARC)
From: Predicting chemotherapy response using a variational autoencoder approach
AUPRC (mean) | p (Welch’s t-test) | ||||||
|---|---|---|---|---|---|---|---|
Cancer type | VAE | Raw data | PCA | ICA | VAE versus Raw data | VAE versus PCA | VAE versus ICA |
BRCA | 0.192 | 0.157 | 0.145 | 0.150 | \(4.21 \times 10^{-6}\) | \(7.42 \times 10^{-10}\) | \(5.01 \times 10^{-8}\) |
PAAD | 0.764 | 0.729 | 0.746 | 0.713 | \(3.38 \times 10^{-4}\) | \(9.12 \times 10^{-2}\) | \(2.02 \times 10^{-6}\) |
COAD | 0.593 | 0.535 | 0.579 | 0.545 | \(6.52 \times 10^{-4}\) | \(3.91 \times 10^{-1}\) | \(1.27 \times 10^{-3}\) |
BLCA | 0.649 | 0.623 | 0.587 | 0.654 | \(4.30 \times 10^{-2}\) | \(1.60 \times 10^{-7}\) | \(6.13 \times 10^{-1}\) |
SARC | 0.736 | 0.713 | 0.714 | 0.729 | \(6.15 \times 10^{-2}\) | \(1.61 \times 10^{-1}\) | \(5.96 \times 10^{-1}\) |