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Table 1 Comparison of the evaluation metrics between MAGCNSE and its four variants

From: MAGCNSE: predicting lncRNA-disease associations using multi-view attention graph convolutional network and stacking ensemble model

Method Accuracy Sensitivity Specificity Precision \(F1\text{- }score\) MCC
MAGCNSE-fgl 0.9029 0.9013 0.9043 0.8984 0.8998 0.8056
MAGCNSE-natt 0.9013 0.9068 0.8959 0.8952 0.901 0.8026
MAGCNSE-nattcnn 0.8885 0.9003 0.8783 0.8647 0.8822 0.7771
MAGCNSE-ncnn 0.9013 0.896 0.907 0.9128 0.9043 0.8025
MAGCNSE 0.9395 0.9192 0.9626 0.9654 0.9417 0.88
  1. The bold number is the highest value of each column and its clarifies the superiority of our model