Table 2.
Some representative prediction methods and classifiers with their used features. The informative features are explained in Table 1.
| Methods | Classifier | Features |
|---|---|---|
| ARTS [16] | SVM | B2, E5, E13 |
| CorePromoter [20] | Stepwise strategy | B1, B6, C |
| CoreBoost [23] | LogitBoost algorithm with decision trees | A1, B1, B9, B10, C, D, E2 |
| CoreBoost_HM [22] | Hidden Markov model | A1, B1, B9, B10, C, D, E2, F |
| CpGcluster [13] | Distance-based algorithm | D |
| CpGProD [14] | A generalized linear model | D |
| DragonGSF [12] | Artificial neural network | B9 |
| DragonPF [15] | Artificial neural network | D |
| EP3 [28] | Analysis approach | E3–18 |
| Eponine [34] | Relevance vector machine | B1 |
| FSPP [41] | SVM | E4–6, E10–17 |
| FirstEF [18] | Decision tree | B4, D |
| Fuzzy-AIRS [40] | Artificial immune recognition system | A1 |
| GDZE [6] | Fisher's linear discriminant algorithm | A1–5, E7 |
| GSD-FLD [6] | Fisher's linear discriminant algorithm | A1–4 |
| HMM-SA [33] | Hidden Markov model, simulated annealing | F |
| McPromoter [51] | Artificial neural network, hidden Markov model |
E3–6, E8–17 |
| NNPP2.2 [37] | Artificial neural network | B1, B4 |
| Nscan [52] | Hidden Markov model, Bayesian networks |
B2–5 |
| Prom-Machine [39] | SVM | A1 (128 top-ranked 4-mer motifs) |
| PromPredict [53] | A scoring function and threshold values | A10, B12, E1, E7, E9, E17 |
| Promoter 2.0 [19] | Neural networks and genetic algorithms | B1, B4, B9, B10 |
| PromoterExplorer [8] | AdaBoost algorithm | A1, A6, D |
| PromoterInspector [54] | Context analysis approach | A1 |
| PromoterScan [55] | Linear discriminant analysis | B1, C |
| ProSOM [30] | Artificial neural network | E5, E7 |
| PSPA [9] | Probabilistic model | A1, A7 |
| TSSW [56] | Linear discriminant function | B1 |
| vw Z-curve [7] | Partial least squares | A5 |
| Wu method [10] | Linear discriminant analysis | A3–5, A7, A8 |