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Table 7 Calculated p-values for Wilcoxon signed rank sum tests (GDT)

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   0.14 3.59E-02 5.99E-03 1.09E-03 9.83E-06 3.88E-06 4.69E-08 1.37E-11
ProQ-MX 0.87   4.77E-02 7.52E-03 3.85E-05 3.58E-07 7.98E-07 3.24E-08 3.38E-12
ProQ-LG 0.96 0.95   0.07 3.48E-03 1.05E-04 2.99E-07 6.41E-08 2.06E-13
ProQ* 0.99 0.99 0.93   0.14 1.03E-02 3.40E-05 8.83E-06 2.91E-09
ModSSEA 1.00 1.00 1.00 0.86   0.06 5.80E-03 2.53E-03 7.13E-08
ModFOLD 1.00 1.00 1.00 0.99 0.94   4.64E-02 0.06 2.90E-06
Pcons* 1.00 1.00 1.00 1.00 0.99 0.95   0.43 1.47E-03
LEE* 1.00 1.00 1.00 1.00 1.00 0.94 0.57   1.01E-03
3D-Jury 1.00 1.00 1.00 1.00 1.00 1.00 1.00 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 GDT 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.