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Table 6 Statistics about the comparative performance of GEST and our label similarity-incorporated kNN classifiers, measured in terms of the number of classes for which AUC scores are improved by the latter over the former, and the average and maximum improvement in AUC scores.

From: Incorporating functional inter-relationships into protein function prediction algorithms

Dataset

Total # classes

# Classes improved

Average improvement over all classes

Maximum improvement

Mnaimneh

137

87

0.0116 (1.86%)

0.1788 (37.83%)

Rosetta

137

65

0.0004 (0.06%)

0.1854 (36.6%)

Krogan

108

60

0.0059 (0.84%)

0.1307 (23.53%)

Combined

136

75

0.0081 (1.04%)

0.2117 (48.85%)