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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.
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BMC Bioinformatics

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