|
Predictor Name
|
Institution
|
ACC
|
AUC
|
|---|
|
AEFN
| |
0.801
|
0.887
|
|
GMM-MAPML
| |
0.785
|
0.874
|
|
MAP-ML
| |
0.764
|
0.859
|
|
MV
| |
0.735
|
0.776
|
|
PRDOS2
|
Tokyo Tech
|
0.754
|
0.855
|
|
MULTICOM-REFINE
|
U of Missouri
|
0.750
|
0.822
|
|
BIOMINE_DR_PDB
|
U of Alberta
|
0.741
|
0.821
|
|
GSMETADISORDERMD
|
IIMCB in Warsaw
|
0.738
|
0.816
|
|
MASON
|
George Mason U
|
0.736
|
0.743
|
|
ZHOU-SPINE-D
|
Indiana University
|
0.731
|
0.832
|
|
DISTILL-PUNCH1
|
UCD Dublin
|
0.726
|
0.800
|
|
OND-CRF
|
Umea University
|
0.706
|
0.759
|
|
UNITED3D
|
Kitasato University
|
0.704
|
0.780
|
|
CBRC_POODLE
|
CBRC
|
0.694
|
0.830
|
|
MCGUFFIN
|
University of Reading
|
0.688
|
0.817
|
|
ISUNSTRUCT
|
IPR RAS
|
0.676
|
0.739
|
|
DISOPRED3C
|
UCL
|
0.670
|
0.853
|
|
ULG-GIGA
|
University of Liege
|
0.588
|
0.726
|
|
MEDOR
|
Aix-Marseille U
|
0.579
|
0.679
|
- Comparisons of AEFN vs. alternative multi-annotator methods (GMM-MAPML, MAP-ML and MV) and individual CASP9 protein disorder predictors.
