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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
  1. (Each result is reported in format: m ± d, where m is the mean and d is the standard deviation across the ten runs)