Skip to main content

Table 2 Predicted performances of classifiers trained with 1,742 examples, presented as the mean and standard deviation (Mean ± SD)

From: The discriminant power of RNA features for pre-miRNA recognition

ALG

FS

Acc

Se

Sp

Fm

Mcc

SVM

FS4

E 85.6 ± 1.2 a

D 83.0 ± 1.9 a

D 88.4 ± 1.5 a

E 85.2 ± 1.3 a

E 71.4 ± 2.3 a

 

FS5

D 87.4 ± 0.9 a

C 84.3 ± 1.5 a

C 90.5 ± 1.4 a

D 86.9 ± 0.9 a

D 74.9 ± 1.7 a

 

FS6

C 89.8 ± 1.1 a

B 87.5 ± 1.5 a

C 93.0 ± 1.7 a

C 89.5 ± 1.1 a

C 79.8 ± 2.2 a

 

FS3

B 90.6 ± 0.8 a

B 88.0 ± 1.3 a

B 93.3 ± 1.3 a

B 90.4 ± 0.9 a

B 81.4 ± 1.7 a

 

FS1

A 92.2 ± 0.9 a

A 89.7 ± 1.8 a

A 94.7 ± 0.8 a

A 92.0 ± 1.0 a

A 84.6 ± 1.8 a

 

FS2

A 92.4 ± 0.9 a

A 90.1 ± 1.6 a

A 94.7 ± 0.6 a

A 92.2 ± 1.0 a

A 84.9 ± 1.8 a

 

FS7

A 92.3 ± 1.0 a

A 89.9 ± 1.1 a

A 94.7 ± 0.9 a

A 92.1 ± 0.9 a

A 84.7 ± 1.6 a

 

SELECT

A 92.3 ± 0.9 a

A 90.0 ± 1.3 a

A 94.6 ± 1.0 a

A 92.1 ± 0.9 a

A 84.6 ± 1.7 a

RF

FS4

E 84.8 ± 1.1 b

D 81.2 ± 1.8 b

C 88.3 ± 1.3 a

E 84.2 ± 1.2 b

E 69.8 ± 2.1 b

 

FS5

D 85.7 ± 0.7 b

D 81.2 ± 0.8 b

B 90.3 ± 1.4 a

D 85.1 ± 0.6 b

D 71.8 ± 1.5 b

 

FS6

C 88.7 ± 1.4 b

C 86.6 ± 1.5 b

A 89.8 ± 1.6 b

C 88.5 ± 1.4 b

C 77.4 ± 2.8 b

 

FS3

C 90.0 ± 1.0 b

C 86.9 ± 1.4 b

A 93.0 ± 1.1 a

C 89.6 ± 1.0 b

C 80.1 ± 1.9 b

 

FS1

A 91.5 ± 1.0 b

A 89.1 ± 1.1 a

A 93.9 ± 1.2 a

A 91.3 ± 1.0 b

A 83.1 ± 1.9 b

 

FS2

A 90.9 ± 1.0 b

B 88.1 ± 1.2 b

A 93.8 ± 1.3 b

A 90.7 ± 1.1 b

A 82.0 ± 2.1 b

 

FS7

A 91.1 ± 0.8 b

B 88.5 ± 1.3 b

A 93.7 ± 1.3 b

A 90.9 ± 1.0 b

A 82.3 ± 2.0 b

 

SELECT

B 90.5 ± 0.9 b

C 87.4 ± 1.0 b

A 93.6 ± 1.4 b

B 90.2 ± 0.9 b

B 81.2 ± 1.9 b

G 2DE

FS3

90.2 ± 0.9

87.4 ± 1.5

93.1 ± 0.9

89.9 ± 0.9

80.6 ± 1.8

  1. Predicted accuracies (Acc), sensitivities (Se), specificities (Sp), F-measures (Fm) and Mathew Correlation Coefficients (Mcc) of classifiers trained with 1,742 examples, presented as the mean and standard deviation (mean ± sd). Capital letters in columns indicate the performance cluster of each feature set, within algorithm (ALG). Lower case letters in columns indicate the cluster of each algorithms, within feature sets. Bold numbers represents the highest performances, which were not significantly different according to the clustering criteria in [42].