Table 2 Comparison with Other methods
From: A multi-omics supervised autoencoder for pan-cancer clinical outcome endpoints prediction
| Methods | OS | DSS | PFI | DFI |
|---|---|---|---|---|
| SVM |
0.6905 (±0.0108) |
0.6927 (±0.0154) |
0.6416 (±0.0119) |
0.5950 (±0.0174) |
| DecisionTree |
0.6973 (±0.0082) |
0.6877 (±0.0199) |
0.6503 (±0.0093) |
0.5736 (±0.0276) |
| Naïve Bayes |
0.6825 (±0.0110) |
0.7139 (±0.0277) |
0.6672 (±0.0074) |
0.6631 (±0.0304) |
| kNN |
0.7189 (±0.0086) |
0.7134 (±0.0146) |
0.6788 (±0.0095) |
0.6488 (±0.0474) |
| RandomForest |
0.7355 (±0.0082) |
0.7449 (±0.0160) |
0.6999 (±0.0134) |
0.6621 (±0.0299) |
| AdaBoost |
0.7297 (±0.0042) |
0.7369 (±0.0219) |
0.6831 (±0.0155) |
0.6454 (±0.0254) |
|
Multi-view Factorization AutoEncoder [3] |
0.766 (−) | – |
0.724 (−) | – |
| MOSAE |
0.7830 (±0.0081) |
0.7870 (±0.0293) |
0.7325 (±0.0123) |
0.7061 (±0.0393) |