Table 2.
Model performance results for predicting a novel relation between a drug and an ADE. Test results of competing KG embedding methods, including TransE, RotatE, ComplEx, DistMult, DeepWalk, node2vec and additionally Random Forest for adverse drug event prediction in comparison to our MultiGML models. Best results are marked in bold. Both MultiGML-RGCN and -RGAT variants were tested with basic and multimodal input features. The model variant “multimodal” refers to the use of several modalities for each node type described in section 3.1.2. In the model variant “basic” all input features have been initialized with the Xavier-Glorot method, i.e. the model effectively learns from the topology only.
| Model | AUROC | AUPR |
|---|---|---|
| TransE | 0.293 | 0.389 |
| RotatE | 0.943 | 0.915 |
| ComplEx | 0.884 | 0.934 |
| DistMult | 0.963 | 0.966 |
| DeepWalk | 0.575 | 0.604 |
| Node2Vec | 0.504 | 0.505 |
| Random Forest | 0.512 | 0.164 |
| MultiGML-RGCN (basic) | 1.0 | 1.0 |
| MultiGML-RGAT (basic) | 1.0 | 1.0 |
| MultiGML-RGCN (multimodal) | 1.0 | 1.0 |
| MultiGML-RGAT (multimodal) | 0.980 | 0.982 |