|
Statistical CGP
|
Inductive CGP
|
|---|
|
Scoring function
|
AUC
|
( /η
max
)
|
Algorithm
|
AUC
|
|
sens
|
0.634
|
(1.2/1.8)
|
NB
|
0.695
|
|
spec
|
0.464
|
(0.8/1.5)
|
LR
|
0.796
|
|
ppv
|
0.519
|
(1.1/2.0)
|
ADTree
|
0.780
|
|
npv
|
0.594
|
(1.8/11.0)
|
IBk
|
0.860
|
|
amss
|
0.578
|
(2.4/96.6)
|
J48
|
0.663
|
|
hmss
|
0.628
|
(2.4/95.1)
|
SMO/Poly
|
0.848
|
|
OR
|
0.537
|
(1.2/2.3)
|
SMO/RBF
|
0.782
|
|
chisq
|
0.767
|
(3.2/109)
| | |
|
bchisq
|
0.585
|
(2.5/109)
| | |
|
F
|
0.698
|
(2.5/69.9)
| | |
- Thirty-eight known genes were labelled as known (out of 4131 genes of the EC-K12 genome). The AUC in inductive CGP were calculated using stratified 10-fold cross-validation. Abbreviations: sens: sensitivity; spec: specificity; ppv: positive predictive value; npv: negative predictive value; amss: arithmetic mean of sensitivity and specificity; hmss: harmonic mean of sensitivity and specificity; OR: odds ratio; chisq: chi-square; bchisq: signed chi-square; F: F-measure; NB: naïve Bayes classifier; LR: logistic regression; ADTree: alternating decision tree; IBk: k-nearest neighbour classifier; J48: J48 decision tree; SMO: support vector machine trained by sequential minimal optimisation algorithm; Poly: polynomial kernel; RBF: radial basis function kernel.
/η
max
)