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Table 5 Performances of LNSM-MSE and benchmark methods evaluated by 5-CV

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

Dataset Methods AUPR AUC SN SP Precision Accuracy F
Pauwels’s dataset Liu’s method 0.345 0.920 0.643 0.950 0.400 0.934 0.493
Cheng’s method 0.588 0.922 0.587 0.975 0.547 0.955 0.566
RBMBM 0.612 0.941 0.605 0.977 0.579 0.958 0.592
INBM 0.641 0.934 0.608 0.979 0.605 0.961 0.607
LNSM-MSE 0.671 0.948 0.629 0.980 0.625 0.963 0.627
Mizutani’s dataset Liu’s method 0.366 0.918 0.637 0.948 0.418 0.930 0.505
Cheng’s method 0.599 0.923 0.593 0.973 0.560 0.951 0.576
RBMBM 0.619 0.939 0.614 0.974 0.581 0.954 0.597
INBM 0.646 0.932 0.616 0.976 0.605 0.956 0.611
LNSM-MSE 0.676 0.944 0.627 0.979 0.635 0.959 0.631
Liu’s dataset Liu’s method 0.278 0.907 0.669 0.930 0.341 0.917 0.452
Cheng’s method 0.592 0.922 0.589 0.974 0.550 0.954 0.569
RBMBM 0.616 0.941 0.608 0.976 0.581 0.957 0.594
INBM 0.641 0.934 0.607 0.979 0.606 0.959 0.606
LNSM-MSE 0.673 0.948 0.631 0.979 0.624 0.962 0.628