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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