Identification of microbe-disease signed associations via multi-scale variational graph autoencoder based on signed message propagation.

Huan ZhuHongxia HaoLiang Yu

Published in: BMC biology (2024)

MSignVGAE achieves AUROC and AUPR values of 0.9742 and 0.9601, respectively. Case studies on three diseases demonstrate that MSignVGAE can effectively capture a comprehensive distribution of associations by leveraging signed information.

Keyphrases

healthcare
convolutional neural network
bioinformatics analysis
machine learning
deep learning