Predicting the potential human lncRNA–miRNA interactions based on graph convolution network with conditional random field

• Published in: Briefings in bioinformatics Vol. 23; no. 6
• Main Authors: Wang, Wenya, Zhang, Li, Sun, Jianqiang, Zhao, Qi, Shuai, Jianwei
• Format: Journal Article
• Language: English
• Published: England 19.11.2022
• Subjects: Algorithms; Computational Biology; Humans; MicroRNAs - genetics; MicroRNAs - metabolism; Neural Networks, Computer; RNA, Long Noncoding - genetics; RNA, Long Noncoding - metabolism; conditional random field; graph convolutional network; computational model; random walk with restart; lncRNA–miRNA interactions
• DOI: 10.1093/bib/bbac463
• ISSN: 1467-5463; 1477-4054

Long non-coding RNA (lncRNA) and microRNA (miRNA) are two typical types of non-coding RNAs (ncRNAs), their interaction plays an important regulatory role in many biological processes. Exploring the interactions between unknown lncRNA and miRNA can help us better understand the functional expression between lncRNA and miRNA. At present, the interactions between lncRNA and miRNA are mainly obtained through biological experiments, but such experiments are often time-consuming and labor-intensive, it is necessary to design a computational method that can predict the interactions between lncRNA and miRNA. In this paper, we propose a method based on graph convolutional neural (GCN) network and conditional random field (CRF) for predicting human lncRNA–miRNA interactions, named GCNCRF. First, we construct a heterogeneous network using the known interactions of lncRNA and miRNA in the LncRNASNP2 database, the lncRNA/miRNA integration similarity network, and the lncRNA/miRNA feature matrix. Second, the initial embedding of nodes is obtained using a GCN network. A CRF set in the GCN hidden layer can update the obtained preliminary embeddings so that similar nodes have similar embeddings. At the same time, an attention mechanism is added to the CRF layer to reassign weights to nodes to better grasp the feature information of important nodes and ignore some nodes with less influence. Finally, the final embedding is decoded and scored through the decoding layer. Through a 5-fold cross-validation experiment, GCNCRF has an area under the receiver operating characteristic curve value of 0.947 on the main dataset, which has higher prediction accuracy than the other six state-of-the-art methods.