Table 3

Performance comparison on the prediction of MDA and LMI in 5-fold cross-validation

Prediction taskMethodAverage AUC
Prediction of microRNA-disease associationsIMCMDA [17]0.6233+/−0.032
MDHGI [18]0.6932+/−0.027
Zeng et al.’s work [19]0.7883+/−0.012
MDA-SKF [20]0.8192+/−0.010
The proposed method0.8512+/−0.012
Prediction of lncRNA–microRNA interactionsNeighbor-based CF [21]0.7301+/−0.026
LFM CF [22]0.7692+/−0.025
EPLMI [23]0.8126+/−0.012
Katz [24]0.8737+/−0.008
The proposed method0.9014+/−0.012
Prediction taskMethodAverage AUC
Prediction of microRNA-disease associationsIMCMDA [17]0.6233+/−0.032
MDHGI [18]0.6932+/−0.027
Zeng et al.’s work [19]0.7883+/−0.012
MDA-SKF [20]0.8192+/−0.010
The proposed method0.8512+/−0.012
Prediction of lncRNA–microRNA interactionsNeighbor-based CF [21]0.7301+/−0.026
LFM CF [22]0.7692+/−0.025
EPLMI [23]0.8126+/−0.012
Katz [24]0.8737+/−0.008
The proposed method0.9014+/−0.012
Table 3

Performance comparison on the prediction of MDA and LMI in 5-fold cross-validation

Prediction taskMethodAverage AUC
Prediction of microRNA-disease associationsIMCMDA [17]0.6233+/−0.032
MDHGI [18]0.6932+/−0.027
Zeng et al.’s work [19]0.7883+/−0.012
MDA-SKF [20]0.8192+/−0.010
The proposed method0.8512+/−0.012
Prediction of lncRNA–microRNA interactionsNeighbor-based CF [21]0.7301+/−0.026
LFM CF [22]0.7692+/−0.025
EPLMI [23]0.8126+/−0.012
Katz [24]0.8737+/−0.008
The proposed method0.9014+/−0.012
Prediction taskMethodAverage AUC
Prediction of microRNA-disease associationsIMCMDA [17]0.6233+/−0.032
MDHGI [18]0.6932+/−0.027
Zeng et al.’s work [19]0.7883+/−0.012
MDA-SKF [20]0.8192+/−0.010
The proposed method0.8512+/−0.012
Prediction of lncRNA–microRNA interactionsNeighbor-based CF [21]0.7301+/−0.026
LFM CF [22]0.7692+/−0.025
EPLMI [23]0.8126+/−0.012
Katz [24]0.8737+/−0.008
The proposed method0.9014+/−0.012
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