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Table 6 Calculated p-values for Wilcoxon signed rank sum tests (MaxSub)

From: Benchmarking consensus model quality assessment for protein fold recognition

 

Method x

Method y

MODCHECK

ProQ-MX

ProQ-LG

ProQ*†

ModSSEA

ModFOLD

Pcons*†

LEE*†

3D-Jury†

MODCHECK

 

0.05

9.47E-03

2.99E-03

1.56E-03

2.61E-05

1.92E-06

4.09E-08

4.02E-11

ProQ-MX

0.95

 

2.74E-02

1.36E-02

2.70E-03

1.54E-05

1.80E-06

7.16E-07

5.21E-11

ProQ-LG

0.99

0.97

 

0.18

0.12

1.10E-02

9.48E-06

8.15E-06

2.95E-11

ProQ*

1.00

0.99

0.82

 

0.28

0.06

7.67E-05

3.74E-05

3.67E-08

ModSSEA

1.00

1.00

0.88

0.72

 

0.08

7.84E-04

1.01E-03

5.11E-07

ModFOLD

1.00

1.00

0.99

0.94

0.93

 

1.41E-02

3.80E-02

1.45E-05

Pcons*†

1.00

1.00

1.00

1.00

1.00

0.99

 

0.57

5.30E-03

LEE*†

1.00

1.00

1.00

1.00

1.00

0.96

0.43

 

2.28E-03

3D-Jury†

1.00

1.00

1.00

1.00

1.00

1.00

0.99

1.00

 
  1. The different MQAP methods are compared in terms of the observed model quality of the top ranked models for each target. H0 = m x ≤ m y , H1 = m x > m y , where H0 is the null hypothesis; H1 is the alternative hypothesis; m x is the observed model quality of models selected by Method x and m y is the observed model quality of models selected by Method y according to the MaxSub score. * The MQAP scores for these methods were downloaded from CASP7 website; all other MQAP methods were run in house during the CASP7 experiment. † MQAP methods which rely on the comparison of multiple models or additional information from multiple servers; all other methods are capable of producing a single score based on a single model.