SGNNMD: signed graph neural network for predicting deregulation types of miRNA-disease associations. Issue 1 (8th December 2021)

• Record Type: Journal Article
• Authors: Zhang, Guangzhan; Li, Menglu; Deng, Huan; Xu, Xinran; Liu, Xuan; Zhang, Wen
• Is Part Of: Briefings in bioinformatics. Volume 23:Issue 1(2022)
• Publication Date: 2021-12-08
• Subjects: miRNA-disease associations -- subgraph -- graph convolutional network -- signed network; Genetics -- Data processing -- Periodicals; Molecular biology -- Data processing -- Periodicals; Genomes -- Data processing -- Periodicals; 572.80285
• DOI: 10.1093/bib/bbab464
• Languages: English
• ISSNs: 1467-5463

Abstract

Abstract: MiRNAs are a class of small non-coding RNA molecules that play an important role in many biological processes, and determining miRNA-disease associations can benefit drug development and clinical diagnosis. Although great efforts have been made to develop miRNA-disease association prediction methods, few attention has been paid to in-depth classification of miRNA-disease associations, e.g. up/down-regulation of miRNAs in diseases. In this paper, we regard known miRNA-disease associations as a signed bipartite network, which has miRNA nodes, disease nodes and two types of edges representing up/down-regulation of miRNAs in diseases, and propose a s igned g raph n eural n etwork method (SGNNMD) for predicting deregulation types of m iRNA-d isease associations. 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. In the computational experiments, SGNNMD achieves highly competitive performance when compared with several baselines, including the signed graph link prediction methods, multi-relation prediction methods and one existing deregulation type prediction method. Moreover, SGNNMD has good inductive capability and can generalize to miRNAs/diseases unseen during the training.