Table 10

ACT feature knock-out experiments for LR

Features

F1 score

Specificity

Sensitivity

Accuracy

Matthews Coef

AUC iP/R

B

72.33

91.61

68.28

84.84

62.14

78.97

N

50.05

94.10

38.20

77.88

40.75

60.12

C

69.38

89.39

66.91

82.87

57.58

76.30

M

69.61

90.83

65.37

83.44

58.53

75.06

BC

74.57

92.69

70.09

86.13

65.34

80.75

BCM

76.45

93.20

72.17

87.10

67.84

82.67

BNCM

76.78

93.49

72.23

87.33

68.37

82.89

Results of feature knock-out experiments on the combined ACT training and development datasets for the logistic regression (LR) model (%). B – bag of words; N – named entities; C – contextual words surrounding proteins; M – MeSH descriptors.

Wang et al. BMC Bioinformatics 2011 12(Suppl 8):S11   doi:10.1186/1471-2105-12-S8-S11