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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
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BMC Bioinformatics

ISSN: 1471-2105

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