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

Abstract

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.

Analysis and verification of the circRNA regulatory network RNO_CIRCpedia_ 4214/RNO-miR-667-5p/Msr1 axis as a potential ceRNA promoting macrophage M2-like polarization in spinal cord injury

“…Advances in the prediction of molecular interactions using computational biology has led to the development of prediction models such as GCNCRF [ 31 ], NDALMA [ 32 ], and GCNAT [ 33 ]. These allow the efficient prediction of miRNA-circRNA/lncRNA interactions and contribute significantly to our understanding of circRNA/lncRNA functions in many diseases.…”

“…Advances in the prediction of molecular interactions using computational biology has led to the development of prediction models such as GCNCRF [31], NDALMA [32], and GCNAT [33]. These allow the Fig.…”

Novel m7G-related lncRNA signature for predicting overall survival in patients with gastric cancer

“…With the development of bioinformatics analysis tools such as machine learning, deep learning and convolutional neural networks, bioinformatics analysis of lncRNA-miRNA will bring great reference value to experiments. Several methods have been proposed for predicting lncRNA-miRNA interactions, such as lncRNA-miRNA interactions prediction by logistic matrix factorization with neighborhood regularized (LMFNRLMI), graph convolutional neural network and conditional random field (GCNCRA), and network distance analysis model for lncRNA-miRNA association prediction (NDALMA), all of which have been shown to be reliable [49][50][51]. In addition, several bioinformatics tools provide an important contribution to tumor metabolism analysis and drug development.…”

Development and validation of an interpretable radiomic nomogram for severe radiation proctitis prediction in postoperative cervical cancer patients

“…In data-driven disease research, a graph neuro network was used to predict the potential associations of disease-related metabolites ( Sun et al, 2022 ). Deep learning can also be used to explore the identification of circRNA-disease associations ( Wang et al, 2021 ) and predict the potential human lncRNA interactions ( Zhang et al, 2021 ; Jingxuan et al, 2022 ; Wang et al, 2022 ). In drug metabolism research, deep learning can be used to predict the ability of a compound to permeate across the blood–brain barrier ( Tang et al, 2022 ) and drug response ( Kuenzi et al, 2020 ).…”