| | 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 |
|
|
