Table 2.
Comparison on the drug response prediction performance of different data representations and prediction models.
| Dataset | Prediction model | Data representation | R2 | P-value |
|---|---|---|---|---|
| CTRP | LightGBM | Tabular data | 0.825 (0.003) | 8.19E−20 |
| Random forest | 0.786 (0.003) | 5.97E−26 | ||
| tDNN | 0.834 (0.004) | 7.90E−18 | ||
| sDNN | 0.832 (0.005) | 1.09E−16 | ||
| CNN | IGTD images | 0.856 (0.003) | ||
| REFINED images | 0.855 (0.003) | 8.77E−01 | ||
| DeepInsight images | 0.846 (0.004) | 7.02E−10 | ||
| GDSC | LightGBM | Tabular data | 0.718 (0.006) | 2.06E−13 |
| Random forest | 0.682 (0.006) | 4.53E−19 | ||
| tDNN | 0.734 (0.009) | 1.79E−03 | ||
| sDNN | 0.723 (0.008) | 6.04E−10 | ||
| CNN | IGTD images | 0.74 (0.006) | ||
| REFINED images | 0.739 (0.007) | 5.93E−01 | ||
| DeepInsight images | 0.731 (0.008) | 2.96E−06 |
In the R2 column, the number before parenthesis is the average R2 across 20 cross-validation trials, and the number in the parenthesis is the standard deviation. Bold indicates the highest average R2 obtained on each dataset. P-value is obtained via the two-tail pairwise t-test to compare the performance of CNNs trained on IGTD images with those of other combinations of prediction models and data representations.