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Table 1 Performance of the inference algorithms on the DREAM4 networks (100 nodes)

From: Reconstruction of large-scale regulatory networks based on perturbation graphs and transitive reduction: improved methods and their evaluation

DREAM4 best performers

   

Score

     

Team 395 (PG 1 + DR-FFL)

   

71.5889

     

Team 296

   

71.2970

     

Team 515 (PG 2∗ + TRANSWESD s,w,∞ )

   

64.7150

     

Team 466

   

63.4060

     

Team 549

   

63.1050

     

Inference algorithm

β

γ

α

Score

Edges

TPs

FPs

FNs

TP100

PG1

2.00

-

-

70.3495

349.4

103.4

246.0

101.4

58.0

PG1 + DR-FFL

-

-

-

71.5889

267.4

83.6

183.8

121.2

62.4

PG 1 + TRANSWESD u,w,∞

-

-

0.95

73.0444

225.0

97.4

127.6

107.4

60.8

PG1 + TRANSWESDu,w,2

-

-

1.50

79.6644

153.8

83.0

70.8

121.8

70.4

PG 1 + LTR u,u

-

-

-

79.7428

261.2

92.4

168.8

112.4

72.2

PG1 + LTRu,w

-

-

0.15

79.1609

262.0

92.8

169.2

112.0

72.2

PG2

2.60

0.05

-

65.8012

398.2

98.0

300.2

106.8

58.2

PG2 + DR-FFL

-

-

-

64.2614

372.8

94.4

278.4

110.4

58.0

PG2 + TRANSWESDu,w,∞

-

-

0.95

65.6504

256.6

86.0

170.6

118.8

64.4

PG2 + TRANSWESDs,w,∞

-

-

0.95

66.0970

260.8

86.8

174.0

118.0

66.6

PG2 + TRANSWESDu,w,2

-

-

1.50

66.5562

224.0

81.0

143.0

123.8

64.2

PG 2 + TRANSWESD s,w,2

-

-

1.50

68.1534

249.2

84.4

164.8

120.4

67.0

PG2 + LTRu,u

-

-

-

65.4214

253.0

82.2

170.8

122.6

64.6

PG2 + LTRs,u

-

-

-

67.7407

274.0

86.4

187.6

118.4

66.6

PG2 + LTRu,w

-

-

0.15

67.2567

271.4

85.6

185.8

119.2

66.0

PG 2 + LTR s,w

-

-

0.15

68.5959

288.2

88.4

199.8

116.4

67.2

PG new

2.00

0.00

-

81.7594

250.2

99.6

150.6

105.2

66.6

PGnew + DR-FFL

-

-

-

80.3085

179.8

82.0

97.8

122.8

66.2

PGnew + TRANSWESDu,w,∞

-

-

0.95

85.3288

179.2

90.2

89.0

114.6

72.0

PGnew + TRANSWESDs,w,∞

-

-

0.95

85.7898

183.0

92.0

91.0

112.8

72.8

PGnew + TRANSWESDu,w,2

-

-

1.50

88.0570

147.6

86.0

61.6

118.8

72.6

PG new + TRANSWESD s,w,2

-

-

1.50

88.5728

150.4

87.8

62.6

117.0

72.8

PGnew + LTRu,u

-

-

-

88.2217

166.8

91.8

75.0

113.0

74.2

PGnew + LTRs,u

-

-

-

88.6350

169.4

93.4

76.0

111.4

75.2

PGnew + LTRu,w

-

-

0.15

88.5203

168.4

92.8

75.6

112.0

75.0

PG new + LTR s,w

-

-

0.15

88.8005

169.8

93.8

76.0

111.0

75.8

  1. The table summarizes the performance (overall score, true positives (TPs), false positives (FPs) and false negatives (FNs)) of the different PG generation and TR algorithms when applied to the DREAM4 networks together with the displayed (optimal) parameters. The last column TP100 shows the average number of TPs within the first 100 top-ranked (reconstructed) edges. As a comparison, the scores of the 5 best-performing algorithms within the challenge are shown. PG2∗ denotes PG2 computed with a minor bug in the original implementation.
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BMC Systems Biology

ISSN: 1752-0509

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