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Table 2 Statistics about the comparative performance of the base k-NN classifiers and their label similarity-incorporated versions, 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 over all classes.

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

Dataset Total # classes # Classes improved Average improvement over all classes Maximum improvement
Mnaimneh 137 74 0.0219 (3.57%) 0.1882 (39.92%)
Rosetta 137 47 0.0083 (1.33%) 0.2091 (38.66%)
Krogan 108 30 0.0045 (0.63%) 0.1982 (31.82%)
Combined 136 59 0.0079 (1.02%) 0.1129 (20.39%)