SGNNMD extracts subgraphs around miRNA-disease pairs from the signed bipartite network and learns structural features of subgraphs via a labeling algorithm and a neural network, and then combines them with biological features (i.e. miRNA-miRNA functional similarity and disease-disease semantic similarity) to build the prediction model.
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Zhang G, Li M, Deng H, Xu X, Liu X, Zhang W.
SGNNMD: signed graph neural network for predicting deregulation types of miRNA-disease associations.
Brief Bioinform. 2021 Dec 8:bbab464. doi: 10.1093/bib/bbab464. Epub ahead of print. PMID: 34875683.