Table 5

Summary of the performance of other state-of-the-art classifiers of mutations, either general or kinase-specific

Method

Scope

Accuracy (%)

Precision (%)

Recall (%)

MCC


KinMut

Kinase†

83.3

60.0

75.2

0.6

SNPs&GO [18]

Kinase†

82.3

62.8

77.5

0.6

Torkamani [19]

Kinase

77.0

-

-

0.5

MutationAssessor [9]

Kinase†

53.8

41.6

95.6

0.5

SNAP [16]

Kinase†

49.4

34.0

93.1

0.4

SIFT [7]

Kinase†

77.6

37.8

27.9

0.2

SNPs&GO [18]

Genome-wide

82.0

83.0

78.0

0.6

MutationAssessor [9]

Genome-wide

79.0

-

-

-

SNAP [16]

Genome-wide

78.2

76.7

80.2

-

SIFT [7]

Genome-wide

68.3

66.1

56.5

0.3


Summary of the performance of other state-of-the-art classifiers of mutations, either general or kinase-specific. Performance was measured in terms of overall accuracy recall and the Matthews correlation coefficient. General methods with which the prediction corresponds to our dataset are marked with †. The remaining results for the classifiers displayed here were taken directly from their original publications

Izarzugaza et al. BMC Genomics 2012 13(Suppl 4):S3   doi:10.1186/1471-2164-13-S4-S3

Open Data