※ Advanced Services of GPS-SUMO 2.0 
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' before the selected one.
1. GPS-SUMO 2.0 (PLR + GSEA): Prediction based on penalized logistic regression + GSEA with the group-based prediction system feature (Also available at the HOME page). 
Speed: 
2. GPS-SUMO 2.0 (Transformer): Prediction based on Transformer with the contextual information. Speed:
3. GPS-SUMO 2.0 (All): Prediction based on all models with all features. Speed:
4. GPS-SUMO 2.0 (Species-specific): Species-specific prediction based on all models with all features. Speed:
5. GPS-SUMO 2.0 (Comprehensive): Prediction based on all models with all features and additional annotations of secondary structure and surface accessibility. Speed:
6. GPS-SUMO 2.0 (Stress conditions): Prediction based on penalized logistic regression, using 39,938 non-redundant SUMOylation sites identified under various stress conditions, such as SUMO protease inhibition, proteasome inhibition and heat shock. Speed:
According to
Sequence(s) in FASTA format UniProt accession number(s)
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After GPS-SUMO 2.0 predictor model was well-trained, we performed an evaluation on this model. From the evaluation, three thresholds with High, Medium and Low stringency were chosen for GPS-SUMO 2.0. The performance under these three thresholds was presented as follow:
| Threshold | SUMOylation | SUMO interaction | ||||||||
| Ac | Sn | Sp | MCC | Pr | Ac | Sn | Sp | MCC | Pr | |
| High | 88.63% | 57.45% | 95.17% | 0.5749 | 71.39% | 94.64% | 90.06% | 95.17% | 0.7551 | 68.08% |
| Medium | 86.60% | 68.24% | 90.46% | 0.5585 | 60.00% | 90.88% | 98.14% | 90.05% | 0.6823 | 53.02% |
| Low | 84.33% | 75.98% | 85.01% | 0.5293 | 51.54% | 86.80% | 99.38% | 85.36% | 0.6081 | 43.72% |