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Table 4 CASP9 comparison on labelled data.

From: Learning by aggregating experts and filtering novices: a solution to crowdsourcing problems in bioinformatics

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
  1. Comparisons of AEFN vs. alternative multi-annotator methods (GMM-MAPML, MAP-ML and MV) and individual CASP9 protein disorder predictors.
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