From: Predicting drug side effects by multi-label learning and ensemble learning
Features | AUC | AUPR | Hamming loss | Ranking loss | One error | Coverage | Average precision |
---|---|---|---|---|---|---|---|
Enzyme | 0.8861 ± 0.0006 | 0.3989 ± 0.0011 | 0.0483 ± 0.0001 | 0.0839 ± 0.0002 | 0.1695 ± 0.0053 | 837.7197 ± 1.6124 | 0.4551 ± 0.0005 |
Pathway | 0.8884 ± 0.0006 | 0.4105 ± 0.0010 | 0.0477 ± 0.0001 | 0.0802 ± 0.0001 | 0.1865 ± 0.0076 | 827.1183 ± 2.9986 | 0.4721 ± 0.0007 |
Target | 0.8947 ± 0.0009 | 0.4424 ± 0.0017 | 0.0464 ± 0.0001 | 0.0745 ± 0.0003 | 0.1695 ± 0.0061 | 812.6752 ± 2.9022 | 0.4919 ± 0.0010 |
Transporter | 0.8863 ± 0.0006 | 0.4010 ± 0.0013 | 0.0482 ± 0.0001 | 0.0826 ± 0.0002 | 0.1661 ± 0.0041 | 836.2058 ± 2.8593 | 0.4644 ± 0.0007 |
Indication | 0.8948 ± 0.0004 | 0.4566 ± 0.0020 | 0.0456 ± 0.0001 | 0.0762 ± 0.0003 | 0.1363 ± 0.0034 | 818.3745 ± 3.6611 | 0.4950 ± 0.0012 |
Substructure | 0.8912 ± 0.0005 | 0.4255 ± 0.0015 | 0.0472 ± 0.0001 | 0.0754 ± 0.0004 | 0.1760 ± 0.0040 | 808.9192 ± 2.4440 | 0.4888 ± 0.0014 |