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Table 3 Performance of models based on different classifiers

From: A network embedding-based multiple information integration method for the MiRNA-disease association prediction

Classifiers AUPR AUC F1 ACC REC SPEC PRE
RF 0.6104 ± 0.0012 0.9293 ± 0.0017 0.6147 ± 0.0025 0.9956 ± 0.0001 0.4893 ± 0.006 0.9993 ± 0.0001 0.8289 ± 0.0164
NB 0.1846 ± 0.0008 0.9103 ± 0.0089 0.2528 ± 0.0028 0.9892 ± 0.0004 0.2572 ± 0.0124 0.9944 ± 0.0005 0.2532 ± 0.0056
LR 0.2129 ± 0.0008 0.9023 ± 0.0008 0.2734 ± 0.0017 0.9884 ± 0.0004 0.3078 ± 0.0094 0.9933 ± 0.0005 0.2480 ± 0.0096
SVM 0.0968 ± 0.0034 0.9021 ± 0.0010 0.1718 ± 0.0036 0.9740 ± 0.0012 0.3761 ± 0.0144 0.9783 ± 0.0013 0.1121 ± 0.0037
weighted RF 0.5944 ± 0.0014 0.9336 ± 0.0014 0.5920 ± 0.0025 0.9953 ± 0.0001 0.4741 ± 0.0085 0.9991 ± 0.0001 0.7913 ± 0.0233