Table 3

(i) Impact of core-attachment refinement on MCL; (ii) Role of affinity scoring in reducing the impact of natural noise on MCL and MCL-CAw

Benchmark

Method

PPI Network

#Predicted complexes

#Matched prediction

Precision

#Derivable benchmarks

#Derived benchmarks

Recall


Wodak

MCL

G+K

242

55

0.226

182

62

0.338

ICD(G+K)

136

68

0.500

153

76

0.497

FSW(G+K)

120

69

0.575

153

78

0.510

Consol3.19

116

70

0.603

145

79

0.545

Boot0.094

203

76

0.374

172

85

0.494


MCL-CAw

G+K

310

77

0.248

182

77

0.423

ICD(G+K)

129

80

0.620

153

80

0.523

FSW(G+K)

117

72

0.615

153

83

0.542

Consol3.19

122

82

0.672

145

82

0.566

Boot0.094

199

79

0.397

172

88

0.512


MIPS

MCL

G+K

242

35

0.143

177

40

0.226

ICD(G+K)

136

47

0.346

151

60

0.397

FSW(G+K)

120

46

0.383

151

61

0.404

Consol3.19

116

48

0.414

157

63

0.401

Boot0.094

203

44

0.271

168

56

0.333


MCL-CAw

G+K

310

53

0.171

177

53

0.300

ICD(G+K)

129

63

0.488

151

63

0.417

FSW(G+K)

117

48

0.410

151

66

0.437

Consol3.19

122

68

0.557

157

68

0.433

Boot0.094

199

47

0.236

168

59

0.351


Aloy

MCL

G+K

242

43

0.179

76

42

0.556

ICD(G+K)

136

58

0.426

75

56

0.747

FSW(G+K)

120

57

0.475

75

57

0.760

Consol3.19

116

54

0.466

76

55

0.724

Boot0.094

203

56

0.276

76

55

0.724


MCL-CAw

G+K

310

52

0.168

76

52

0.684

ICD(G+K)

129

59

0.457

75

59

0.787

FSW(G+K)

117

60

0.513

75

60

0.800

Consol3.19

122

57

0.467

76

57

0.750

Boot0.094

199

57

0.286

76

58

0.763


Affinity scoring of PPI networks improved the performance of MCL and MCL-CAw. Affinity scoring followed by CA refinement had a compounded effect in improving the performance of MCL.

Srihari et al. BMC Bioinformatics 2010 11:504   doi:10.1186/1471-2105-11-504

Open Data