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Table 2 Comparison of the evaluation metrics between MAGCNSE and six traditional machine learning classifiers

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
RandonForest 0.8945 0.877 0.9120 0.9089 0.8926 0.7896
ExtraTrees 0.8958 0.8859 0.9057 0.9042 0.8948 0.7921
XGBoost 0.9076 0.9101 0.9050 0.9056 0.9078 0.8153
LightGBM 0.9037 0.9031 0.9044 0.9052 0.9036 0.8085
CatBoost 0.9108 0.9146 0.9070 0.9079 0.9111 0.8218
LogisticRegression 0.8652 0.8470 0.8834 0.8792 0.8627 0.7312
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