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Table 3 Performances of our methods and other state-of-the-art methods

From: A unified frame of predicting side effects of drugs by using linear neighborhood similarity

Dataset Method AUC AUPR Hamming Loss Ranking Loss One Error Coverage Average Precision
Pauwels’s dataset Pauwels’s method 0.8827 0.3883 0.0577 0.0827 0.1779 832.7827 0.4616
LNSM 0.8941 0.4491 0.0444 0.0713 0.1633 790.9471 0.5126
Mizutani’s dataset Mizutani’s method 0.8665 0.4107 0.0557 0.0888 0.1854 862.9757 0.4795
LNSM 0.8946 0.4624 0.0499 0.0746 0.1581 805.8875 0.5170
Liu’s dataset Liu’s method 0.8850 0.2514 0.0721 0.0927 0.9291 837.4579 0.2610
FS-MLKNN 0.9034 0.4802 0.0524 0.0703 0.1202 795.9435 0.5134
LNSM-SMI 0.8986 0.5053 0.0435 0.0670 0.1154 789.8486 0.5476