Skip to main content

Table 3 Performance of the inference algorithms on the SysGenSIM networks

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

 

Noise configuration

   

Inference algorithm

1 - LL

2 - LM

3 - LH

4 - ML

5 - MM

6 - MH

7 - HL

8 - HM

9 - HH

PGnew

0.7388

0.6835

0.5159

0.6735

0.6622

0.5850

0.5141

0.5218

0.4835

PGnew + TRANSWESDu,w,∞

0.7695

0.6906

0.5141

0.6921

0.6778

0.5921

0.5192

0.5269

0.4868

PGnew + TRANSWESDs,w,∞

0.7702

0.6910

0.5142

0.6923

0.6780

0.5922

0.5192

0.5269

0.4868

PGnew + TRANSWESDu,w,2

0.7335

0.5825

0.5041

0.6471

0.6410

0.5650

0.4963

0.5115

0.4737

PGnew + TRANSWESDs,w,2

0.7354

0.5929

0.5042

0.6478

0.6417

0.5653

0.4965

0.5116

0.4739

PGnew + LTRu,u

0.7561

0.6320

0.5114

0.6701

0.6583

0.5783

0.5093

0.5209

0.4816

PGnew + LTRs,u

0.7570

0.6390

0.5115

0.6705

0.6587

0.5784

0.5094

0.5210

0.4818

PGnew + LTRu,w

0.7737

0.7051

0.5285

0.6898

0.6751

0.5924

0.5196

0.5291

0.4880

PGnew + LTRS,W

0.7742

0.7057

0.5285

0.6900

0.6753

0.5925

0.5196

0.5291

0.4880

  1. Each score is the mean of the AUPR computed for the 10 networks with K ≃ 1.5 simulated according to the same noise configuration. Thresholds used by the inference algorithms are β = 2.0 and γ = 0.05 for generating PGnew, α = 0.95 for TRANSWESD·,w,∞, α = 1.50 for TRANSWESD·,w,2 and α = 0.15 for LTR·,w. Analogous performances for K ≃ 2 and K ≃ 2.5 are shown in Tables T1 and T2 in Additional file 1.
Back to article page

BMC Systems Biology

ISSN: 1752-0509

Contact us