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Table 1 The main ideas of ANMDA and 6 published methods

From: ANMDA: anti-noise based computational model for predicting potential miRNA-disease associations

Method Main idea
ANMDA Adopts subsampling for noise smoothing and light gradient boosting machine for prediction
BHCMDA Uses biased heat conduction-based method to pay attention to specific nodes for prediction
DBNMDA Constructs deep-belief network for prediction
EKRRMDA Applies ensemble learning and kernel ridge regression on various data subset created by random selection of features for prediction
FCGCNMDA Applies fully connected graph convolutional networks for prediction
MDACNN Uses auto-encoders for dimensionality reduction and then applies convolutional neural networks for prediction
WBSMDA Calculates within-scores and between scores for prediction