 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]  Distancebased 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  FuzzyAIRS [40]  Artificial immune recognition system  A1  GDZE [6]  Fisher's linear discriminant algorithm  A1–5, E7  GSDFLD [6]  Fisher's linear discriminant algorithm  A1–4  HMMSA [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  PromMachine [39]  SVM  A1 (128 topranked 4mer 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 Zcurve [7]  Partial least squares  A5  Wu method [10]  Linear discriminant analysis  A3–5, A7, A8 

