Table 1.
DTIs Prediction Results on Luo’s Dataset and BioKG Dataset in the scenario of the warm start.
| Luo | BioKG | |||
|---|---|---|---|---|
| Methods | AUROC std |
AUPR std |
AUROC std |
AUPR std |
| SVM | 0.654 0.037 |
0.739 0.045 |
0.730  0.041 |
0.803 0.021 |
| DNN | 0.882 0.031 |
0.881 0.026 |
0.934 0.018 |
0.933 0.014 |
| TransE | 0.731 0.042 |
0.725 0.033 |
0.756 0.011 |
0.799 0.018 |
| DistMult | 0.752 0.025 |
0.745 0.038 |
0.903 0.049 |
0.889 0.031 |
| ConvKB | 0.823 0.010 |
0.826 0.036 |
0.911 0.029 |
0.913 0.015 |
| RGCN | 0.842 0.032 |
0.848 0.045 |
0.915 0.017 |
0.902 0.026 |
| ConvE | 0.795 0.033 |
0.828 0.041 |
0.922 0.032 |
0.925 0.029 |
| CrossE | 0.862 0.015 |
0.874 0.027 |
0.931 0.041 |
0.928 0.039 |
| RotatE | 0.869 0.025 |
0.872 0.013 |
0.946 0.019 |
0.952 0.032 |
| DistMA | 0.837 0.028 |
0.838 0.019 |
0.928 0.025 |
0.930 0.041 |
| MuRE | 0.839 0.028 |
0.839 0.053 |
0.931 0.052 |
0.937 0.015 |
| AutoSF | 0.733 0.040 |
0.731 0.058 |
0.877 0.034 |
0.885 0.049 |
| BoxE | 0.857 0.054 |
0.843 0.030 |
0.913 0.047 |
0.916 0.026 |
| PairRE | 0.831 0.017 |
0.828 0.044 |
0.915 0.027 |
0.911 0.062 |
| DeepDTI | 0.859 0.039 |
0.840 0.017 |
0.973 0.024 |
0.970 0.036 |
| KGE_NFM | 0.903 0.026 |
0.898 0.019 |
0.946 0.016 |
0.946 0.028 |
| KGE-UNIT | 0.942 0.025 |
0.930 0.034 |
0.987 0.035 |
0.980 0.042 |