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Table 2 5-CV performances of prediction models on Liu’s dataset

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

Data Methods AUC AUPR Hamming Loss Ranking Loss One Error Coverage Average Precision
Enzyme LNSM 0.8898 0.4187 0.0473 0.0821 0.1659 846.3846 0.4696
Pathway LNSM 0.8886 0.4273 0.0470 0.0776 0.1647 814.6298 0.4932
Target LNSM 0.8991 0.4708 0.0452 0.0690 0.1538 792.3726 0.5216
Transporter LNSM 0.8896 0.4147 0.0477 0.0817 0.1611 849.3161 0.4762
Treatment LNSM 0.9013 0.4836 0.0446 0.0710 0.1262 806.8558 0.5232
Substructure LNSM 0.8944 0.4538 0.0459 0.0714 0.1490 803.5228 0.5184
All data LNSM-SMI 0.8986 0.5053 0.0435 0.0670 0.1154 789.8486 0.5476
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