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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