Table 6 Comparison of TRL with baseline RL. AUCs when RL (baseline) and TRL are applied to build a classification rule model on three datasets, Petalidis (brain), KangA (IPF), and Lapointe (prostate). SRC means the source dataset (e.g., for target Petalidis, SRC1 is Freije, see Additional File 3). The mean and the standard error of the mean (SEM) for the AUC of a dataset was obtained by 10-fold cross-validation
Dataset | Petalidis | KangA | Lapointe |
|---|---|---|---|
AUC (SEM) | AUC (SEM) | AUC (SEM) | |
Baseline | 0.83 (0.06) | 0.86 (0.07) | 0.93 (0.03) |
SRC1 | 0.82 (0.05) | 0.86 (0.07) | 0.93 (0.03) |
SRC2 | 0.88 (0.07) | 0.86 (0.07) | 0.89 (0.05) |
SRC3 | 0.81 (0.07) | 0.93 (0.05) | 0.90 (0.03) |
SRC4 | 0.78 (0.06) | 0.86 (0.07) | 0.93 (0.03) |
SRC5 | 0.85 (0.05) | 0.86 (0.07) | 0.91 (0.04) |
SRC6 | 0.81 (0.07) | 0.86 (0.07) | 0.91 (0.04) |