MoGraphDRP: Multi-omics and graph fusion with bilinear attention for predicting drug sensitivity
Fig 5
a. Comparison of the performance of different models based on MAE and RMSE metrics on the benchmark dataset.
The MoGraphDRP model achieved lower error values in both metrics compared to other methods, indicating higher accuracy in predicting IC50 values. b. Performance comparison of different models based on SCC, PCC, and R² metrics shows that the MoGraphDRP model not only achieved the highest linear correlation coefficient (PCC = 0.9689) but also recorded the highest explained variance (R² = 0.9388) among all methods.