Fig. 5From: GSAMDA: a computational model for predicting potential microbe–drug associations based on graph attention network and sparse autoencoderAnalysis of the impact of hyperparameters on performance of GSAMDA. The subfigures from (a) to (d) show the AUC values of the related values of the dimension of node topological representation and node attribute representation, the learning rate of GAE and SAE, respectivelyBack to article page