Table 1 Summary of ML models and features were used for training PVPs
From: Application of machine learning in bacteriophage research
No. | Predictor | Method | Number dataset (TR/TS) | Performance |
|---|---|---|---|---|
1 | ANN | “ACC, protein isoelectric Points” + ANN | 307 (307/NA) | 85% |
2 | Naïve Bayes | “ACC, DPC” + CFS + Naïve Bayes | 401 (307/94) | 79% |
3 | PVPred | g-gap DPC + ANOVA+SVM | 307 (307/NA) | 85% |
4 | PhagePred | g-gap DPC + ANOVA + Multinomial Naïve Bayes | 307 (307/NA) | 98% |
5 | PVP-SVM | “AAC, ATC, CTD, DPC, PCP” + RF-based feature selection + SVM | 401 (307/94) | 87% |
6 | SVM-based | g-gap DPC + “ANOVA, mRMR” + SVM | 401 (307/94) | 86% |
7 | Ensemble RF | “CTD, bi-profile Bayes, PseAAC, PSSM” + Relief + RF | 501 (253/248) | 85% |
8 | Pred-BVP-Unb | CT, SAAC, bi-PSSM+SVM | 401 (307/94) | 92% |
9 | PVPred-SCM | DPC + SCM | 401 (307/94) | 77% |
10 | Meta-iPVP | Probabilistic feature+SVM | 626 (313/313) | 82% |