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Table 1 Performance comparison among various methods.

From: Identifying network biomarkers based on protein-protein interactions and expression data

A

     
 

Data sets

Method

Predicting accuracy

Redundancyscore

Number of genes

Two-class case

GSE10797

DEG+ellipsoidFN

93.94% (62/66)

0.2090

22

  

PPIA+ellipsoidFN

96.97% (64/66)

0.1839

14

 

GSE7904

DEG+ellipsoidFN

98.39% (61/62)

0.3698

18

  

PPIA+ellipsoidFN

98.39% (61/62)

0.2968

19

Multiple class

GSE18229

DEG+ellipsoidFN

80.00% (248/310)

0.1532

170

  

PPIA+ellipsoidFN

79.03% (245/310)

0.1652

135

 

GSE10797

DEG+ellipsoidFN

78.79% (52/66)

0.1663

161

  

PPIA+ellipsoidFN

83.33% (55/66)

0.2150

56

B

     
 

Data sets

Method

Redundancy score

Number of common genes

Two-class case

GSE10797

t-test

0.2715

13

  

PPIA

0.2144

  
 

GSE7904

t-test

0.3077

27

  

PPIA

0.2623

  

Multiple class

GSE18229

F-test

0.3031

15

  

PPIA

0.1693

  
 

GSE10797

F-test

0.3280

11

  

PPIA

0.2209

  
  1. (A) Comparing PPIA + ellipsoidFN method with DEG + ellipsoidFN in predicting accuracy, redundancy score, and the number of genes identified. (B) Comparisons on PPIA + ellipsoidFN and t-test for two-class case and PPIA + ellipsoidFN versus F-test for multiple-class case based on 50 top PPIs and proteins.
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BMC Medical Genomics

ISSN: 1755-8794

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