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Table 8 Performance comparison of the biclustering network using new evaluation criteria.

From: Construction of gene regulatory networks using biclustering and bayesian networks

Methods

EdgeC ount

TP

FP

TN

FN

FP to TP

TN to FN

AURO C

AUPR

Gold

2194

2194

0

400396

0

0

0

1

1

ALL

5440

94

5346

395050

2100

4623

2150

0.7530

0.4614

SAMBA

1611

46

1565

398831

2148

1340

2501

0.6958

0.3644

ISA

2558

56

2502

397894

2138

2141

1151

0.8451

0.6306

OPSM

220

12

208

400188

2182

190

2453

0.5423

0.089

Friedman

947

22

925

399471

2172

794

2700

0.6364

0.2491

CMSBE

735

20

715

399681

2174

653

2750

0.6181

0.2333

K-means

380

13

367

400029

2181

323

3100

0.5667

0.1307

Bivisu

1515

13

1502

398894

2181

1265

2610

0.6845

0.3326

CC

590

3

587

399809

2191

507

2800

0.5943

0.1788

  1. The performances of the various biclustering algorithms improved when false positive edges could be considered true positive edges on the basis of strong evidence in the gold network. Column 7 shows the number of false positive edges in each algorithm that could be considered true positive edges (i.e. have evidence in the gold standard network). Column 8 shows the number of true negative edges that are considered as false negative.