Table 6

The results of our meta-predictors and top-scoring primary methods in CASP8 and CASP9
CASP8
Method Sw AUC Sensitivity Specificity
FloatCons 0.662 ± 0.048 0.908 ± 0.017 0.758 ± 0.048 0.904 ± 0.004
BinCons 0.661 ± 0.050 0.897 ± 0.021 0.741 ± 0.050 0.920 ± 0.003
DisoClust 0.644 ± 0.047 0.908 ± 0.018 0.727 ±0.047 0.917 ± 0.004
MULTICOM 0.660 ± 0.039 0.896 ± 0.019 0.796 ± 0.039 0.864 ± 0.004
Mahmood-Torda 0.619 ± 0.061 0.918 ± 0.015 0.641 ± 0.061 0.978 ± 0.001
POODLE-L 0.588 ± 0.066 0.895 ± 0.021 0.646 ± 0.066 0.942 ± 0.004
CASP9
Method Sw AUC Sensitivity Specificity
FloatCons 0.427 ± 0.009 0.795 ± 0.011 0.574 ± 0.020 0.854 ± 0.009
GSmetaDisorder3D 0.391 ± 0.007 0.784 ± 0.012 0.411 ± 0.016 0.948 ± 0.008
GSmetaDisorderMD 0.476 ± 0.006 0.818 ± 0.008 0.654 ± 0.012 0.821 ± 0.010
GSmetaDisorderMD2 0.516 ± 0.010 0.841 ± 0.014 0.653 ± 0.013 0.860 ± 0.012
PrDOS2 0.509 ± 0.002 0.855 ± 0.010 0.609 ± 0.008 0.857 ± 0.003
MULTICOM-REFINE 0.500 ± 0.003 0.821 ± 0.008 0.651 ± 0.003 0.851 ± 0.004

The highest value for each score is shown in bold.

Kozlowski and Bujnicki

Kozlowski and Bujnicki BMC Bioinformatics 2012 13:111   doi:10.1186/1471-2105-13-111

Open Data