Table 2 AUC performance of SVM classifier on embedding features with different biological word lengths
From: TNFPred: identifying tumor necrosis factors using hybrid features based on word embeddings
Feature | AUC | |
|---|---|---|
5-fold cross-validation | Independent | |
1-g | 0.856 ± 0.485 | 0.848 ± 0.525 |
2-g | 0.901 ± 0.416 | 0.883 ± 0.599 |
3-g | 0.934 ± 0.423 | 1 ± 0 |
4-g | 0.617 ± 0.63 | 0.563 ± 0.751 |
5-g | 0.543 ± 0.539 | 0.574 ± 0.686 |
1-g and 2-g combined | 0.952 ± 0.416 | 0.934 ± 0.48 |
1-g and 3-g combined | 0.96 ± 0.42 | 0.921 ± 0.497 |
2-g and 3-g combined | 0.984 ± 0.298 | 0.998 ± 0.277 |