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Table 1 Performance of HM-SVM versus other methods on all data sets

From: Prediction of protein binding sites in protein structures using hidden Markov support vector machine

Data set

Method

Specificity+ (random)a

Sensitivity+ (random)b

F1

Accuracy

MCC

AUC

Time (s)c

Hetero-complex Id

ANN

37.6% (28.1%)

59.4% (16.7%)

46.0%

60.9%

18.9%

64.5%

326

 

SVM

38.4% (28.1%)

59.8% (16.8%)

46.8%

61.8%

20.2%

65.4%

179461

 

CRF

42.6% (28.1%)

55.2% (15.5%)

48.0%

66.5%

24.4%

65.3%

12151

 

HM-SVM

44.9% (28.1%)

56.0% (15.7%)

49.8%

68.3%

27.4%

69.5%

356

Homo-complex I

ANN

39.0% (27.0%)

58.4% (15.8%)

46.6%

63.9%

22.1%

67.0%

586

 

SVM

39.6% (27.0%)

61.9% (16.7%)

48.3%

64.2%

24.2%

68.6%

224979

 

CRF

45.1% (27.0%)

59.2% (16.0%)

51.2%

69.5%

30.2%

67.6%

16961

 

HM-SVM

45.4% (27.0%)

60.0% (16.2%)

51.7%

69.7%

30.9%

72.2%

588

MixeI

ANN

40.3% (27.5%)

51.4% (14.1%)

44.7%

65.4%

20.8%

65.8%

1242

 

SVM

39.5% (27.5%)

61.5% (16.9%)

48.1%

63.6%

23.3%

67.6%

831579

 

CRF

44.3% (27.5%)

57.5% (15.8%)

49.9%

68.4%

28.0%

66.8%

28364

 

HM-SVM

45.5% (27.5%)

58.0% (15.9%)

51.0%

69.4%

29.7%

71.2%

891

Hetero-complex IIf

ANN

45.9% (34.9%)

60.5% (21.1%)

52.1%

61.3%

21.3%

65.8%

604

 

SVM

47.9% (34.9%)

61.6% (21.5%)

53.9%

63.2%

24.6%

67.7%

160625

 

CRF

51.6% (34.9%)

57.6% (20.1%)

54.3%

66.3%

28.0%

67.3%

13441

 

HM-SVM

54.0% (34.9%)

56.7% (19.8%)

55.3%

68.0%

30.5%

70.7%

464

Homo-complex II

ANN

43.9% (32.3%)

66.7% (21.5%)

52.8%

61.5%

24.1%

68.1

856

 

SVM

47.1% (32.3%)

63.1% (20.4%)

54.0%

65.2%

27.7%

70.2%

554054

 

CRF

52.5% (32.3%)

59.7% (19.3%)

55.9%

69.5%

32.9%

68.7%

18124

 

HM-SVM

53.3% (32.3%)

60.1% (19.4%)

56.5%

70.1%

34.0%

73.4%

851

Mix II

ANN

46.5% (33.3%)

53.4% (17.9%)

49.4%

63.7%

21.7%

65.8%

1260

 

SVM

47.5% (33.3%)

62.3% (20.8%)

53.9%

64.5%

26.5%

69.2%

1316103

 

CRF

52.2% (33.3%)

58.6% (19.5%)

55.2%

68.3%

30.9%

68.1%

856765

 

HM-SVM

53.6% (33.3%)

58.6% (19.6%)

56.0%

69.3%

32.6%

72.4%

1320

  1. Specificity+ = TP/(TP+FP); Sensitivity+ = TP/(TP+FN); F1 = 2 × Specificity+ × Sensitivity+/(Specificity++Sensitivity+); Accuracy = (TP+TN)/(TP+TN+FP+FN); MCC = (TP × TN-FP × FN)/; AUC: Area Under ROC Curve [61]. Where TP is the number of true positives (residues predicted to be interface residues that actually are interface residues); FP the number of false positives (residues predicted to be interface residues that are in fact not interface residues); TN the number of true negatives; FN the number of false negatives.
  2. aValues in parentheses are randomly predicted values. The specificity+ of random prediction is calculated as: the total number of interaction sites residues/the total number of residues.
  3. bValues in parentheses are randomly predicted values. The sensitivity+ of random prediction is calculated as: the total number of predicted residues as interaction sites by each method/the total number of residues.
  4. cThe total running time (second) for 5-fold cross-validation, including training and testing.
  5. dType I data set with minor interface as negative samples.
  6. eThe mixed data set of hetero-complexes and homo-complexes.
  7. fType II data set with minor interface as positive samples.