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Table 1 Overall prediction performance of the 15 RF classifiers and that of the ensemble with different votes on the CASP9 data set.

From: LigandRFs: random forest ensemble to identify ligand-binding residues from sequence information alone

Individual

Ensemble

No.

Sen(%)

MCC

Prec(%)

F1(%)

No.

Sen(%)

MCC

Prec(%)

F1(%)

1

57.15

0.30

25.25

29.83

1

87.86

0.19

9.98

16.85

2

59.40

0.35

31.13

33.59

2

84.48

0.22

12.58

19.95

3

63.24

0.31

24.35

29.38

3

81.86

0.24

14.05

21.49

4

65.76

0.33

25.20

31.14

4

80.28

0.25

15.39

22.67

5

44.50

0.32

34.65

31.24

5

78.84

0.27

16.65

24.09

6

57.83

0.31

26.22

30.47

6

75.99

0.27

17.38

24.58

7

59.12

0.33

29.19

31.62

7

74.81

0.28

18.86

26.00

8

59.23

0.32

27.18

31.16

8

73.32

0.29

20.21

27.03

9

67.88

0.30

22.80

22.56

9

72.63

0.30

21.17

28.04

10

51.21

0.31

28.62

31.21

10

71.21

0.31

23.42

29.34

11

46.99

0.31

30.96

30.53

11

69.51

0.32

24.69

30.19

12

64.50

0.30

23.61

28.96

12

67.31

0.33

25.72

30.64

13

61.25

0.31

25.28

29.20

13

64.93

0.33

26.68

31.07

14

40.51

0.31

38.38

30.41

14

62.01

0.34

28.78

32.46

15

59.98

0.31

26.08

30.50

15

56.96

0.37

34.97

35.99

  1. The left part shows the performance of each individual classifier, and the right shows the performance of the ensemble of the 15 classifiers with different votes, i.e., the ensemble predicts a residue to be ligand binding site if a number of RF classifiers predict it to be a ligand binding site residue. Here the ensemble with majority vote predicts a residue to be ligand binding site if all of the 15 RF classifiers predict it to be a binding residue.
  2. The italic number denotes the best performance by the measure of MCC.
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BMC Bioinformatics

ISSN: 1471-2105

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