SGCNCMI: A New Model Combining Multi-Modal Information to Predict circRNA-Related miRNAs, Diseases and Genes

Abstract

Computational prediction of miRNAs, diseases, and genes associated with circRNAs has important implications for circRNA research, as well as provides a reference for wet experiments to save costs and time. In this study, SGCNCMI, a computational model combining multimodal information and graph convolutional neural networks, combines node similarity to form node information and then predicts associated nodes using GCN with a distributive contribution mechanism. The model can be used not only to predict the molecular level of circRNA–miRNA interactions but also to predict circRNA–cancer and circRNA–gene associations. The AUCs of circRNA—miRNA, circRNA–disease, and circRNA–gene associations in the five-fold cross-validation experiment of SGCNCMI is 89.42%, 84.18%, and 82.44%, respectively. SGCNCMI is one of the few models in this field and achieved the best results. In addition, in our case study, six of the top ten relationship pairs with the highest prediction scores were verified in PubMed.

Biolinguistic graph fusion model for circRNA–miRNA association prediction

“…Compared with our model, other models in this research field do not have the advantages of this model’s integration of the above algorithms, so they cannot show good competitive results. Because there is a precise comparison here, we counted the \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{upgreek} \usepackage{mathrsfs} \setlength{\oddsidemargin}{-69pt} \begin{document} $\text{AUC}$\end{document} and \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{upgreek} \usepackage{mathrsfs} \setlength{\oddsidemargin}{-69pt} \begin{document} $\text{AUPR}$\end{document} scores generated by the prior models, and these listed results in Table 4 , containing our model and only several newly published papers in the new research field of CMAs prediction with CMIVGSD, WSCD, KGDCMI [ 23 ] and SGCNCMI [ 20 ]. The Table 4 shows BGF-CMAP realized the highest \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{upgreek} \usepackage{mathrsfs} \setlength{\oddsidemargin}{-69pt} \begin{document} $\text{AUC}$\end{document} and \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{upgreek} \usepackage{mathrsfs} \setlength{\oddsidemargin}{-69pt} \begin{document} $\text{AUPR}$\end{document} scores, which were 0.0133 and 0.0233 times superior to the second-best SGCNCMI model and exceeded the mean value of the other three methods by about 0.0198 and 0.0372 times.…”

“…Despite this fact, exploring circRNA–miRNA interactions through some wet-lab experiments is commonly labor-intensive. To alleviate this trouble, plenty of simulation methods have been employed to speed up the identification of circRNA–miRNA associations (CMAs) [ 19 , 20 ].…”

BCMCMI: A Fusion Model for Predicting circRNA-miRNA Interactions Combining Semantic and Meta-path

“…CMIVGSD uses singular value decomposition to extract linear features from the matrix representations of circRNAs and miRNAs and uses variational graph self-encoders to obtain nonlinear features . SGCNCMI introduces a sparse autoencoder and predicts CMI using a multilayer graph convolutional neural network . KGDCMI indicates CMI by fusing attribute features and behavioral features .…”

Likelihood-based feature representation learning combined with neighborhood information for predicting circRNA–miRNA associations

“…In comparison, Wang et al . [ 20–22 ] proposed KGDCMI, SGCNCMI and JSNDCMI models, which achieved higher accuracy with prediction AUCs of 0.8930, 0.8942 and 0.9003, respectively. Based on the data collection presented above, it provides a strong basis for predicting CMAs using computer algorithm models.…”