The Scientific World Journal / 2014 / Article / Tab 2

Research Article

Rule-Based Knowledge Acquisition Method for Promoter Prediction in Human and Drosophila Species

Table 2

Some representative prediction methods and classifiers with their used features. The informative features are explained in Table 1.

MethodsClassifierFeatures

ARTS [16]SVMB2, E5, E13
CorePromoter [20]Stepwise strategyB1, B6, C
CoreBoost [23]LogitBoost algorithm with decision treesA1, B1, B9, B10, C, D, E2
CoreBoost_HM [22]Hidden Markov modelA1, B1, B9, B10, C, D, E2, F
CpGcluster [13]Distance-based algorithm D
CpGProD [14]A generalized linear modelD
DragonGSF [12]Artificial neural networkB9
DragonPF [15]Artificial neural networkD
EP3 [28]Analysis approachE3–18
Eponine [34]Relevance vector machineB1
FSPP [41]SVME4–6, E10–17
FirstEF [18]Decision treeB4, D
Fuzzy-AIRS [40]Artificial immune recognition systemA1
GDZE [6]Fisher's linear discriminant algorithmA1–5, E7
GSD-FLD [6]Fisher's linear discriminant algorithmA1–4
HMM-SA [33]Hidden Markov model, simulated annealingF
McPromoter [51]Artificial neural network,
hidden Markov model
E3–6, E8–17
NNPP2.2 [37]Artificial neural networkB1, B4
Nscan [52]Hidden Markov model,
Bayesian networks
B2–5
Prom-Machine [39]SVMA1 (128 top-ranked 4-mer motifs)
PromPredict [53]A scoring function and threshold valuesA10, B12, E1, E7, E9, E17
Promoter 2.0 [19]Neural networks and genetic algorithmsB1, B4, B9, B10
PromoterExplorer [8]AdaBoost algorithmA1, A6, D
PromoterInspector [54]Context analysis approachA1
PromoterScan [55]Linear discriminant analysisB1, C
ProSOM [30]Artificial neural networkE5, E7
PSPA [9]Probabilistic modelA1, A7
TSSW [56]Linear discriminant functionB1
vw Z-curve [7]Partial least squaresA5
Wu method [10]Linear discriminant analysisA3–5, A7, A8

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