KGDCMI: A New Approach for Predicting circRNA–miRNA Interactions From Multi-Source Information Extraction and Deep Learning

Abstract

Emerging evidence has revealed that circular RNA (circRNA) is widely distributed in mammalian cells and functions as microRNA (miRNA) sponges involved in transcriptional and posttranscriptional regulation of gene expression. Recognizing the circRNA–miRNA interaction provides a new perspective for the detection and treatment of human complex diseases. Compared with the traditional biological experimental methods used to predict the association of molecules, which are limited to the small-scale and are time-consuming and laborious, computing models can provide a basis for biological experiments at low cost. Considering that the proposed calculation model is limited, it is necessary to develop an effective computational method to predict the circRNA–miRNA interaction. This study thus proposed a novel computing method, named KGDCMI, to predict the interactions between circRNA and miRNA based on multi-source information extraction and fusion. The KGDCMI obtains RNA attribute information from sequence and similarity, capturing the behavior information in RNA association through a graph-embedding algorithm. Then, the obtained feature vector is extracted further by principal component analysis and sent to the deep neural network for information fusion and prediction. At last, KGDCMI obtains the prediction accuracy (area under the curve [AUC] = 89.30% and area under the precision–recall curve [AUPR] = 87.67%). Meanwhile, with the same dataset, KGDCMI is 2.37% and 3.08%, respectively, higher than the only existing model, and we conducted three groups of comparative experiments, obtaining the best classification strategy, feature extraction parameters, and dimensions. In addition, in the performed case study, 7 of the top 10 interaction pairs were confirmed in PubMed. These results suggest that KGDCMI is a feasible and useful method to predict the circRNA–miRNA interaction and can act as a reliable candidate for related RNA biological experiments.

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

“…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 . GCNCMI aggregates the semantic and interaction information in the bipartite graph for CMI prediction .…”

“…The second dataset was CMI-9905, compiled by Wang et al, including 9905 sets of relationships between 2346 circRNAs and 926 miRNAs. 18 The dataset we mainly use in this article is CMI-9905; see Table 1 for details.…”

“…After filtering out invalid data, we finally got 9589 sets of relationships between 2115 circRNAs and 821 miRNAs. The second dataset was CMI-9905, compiled by Wang et al, including 9905 sets of relationships between 2346 circRNAs and 926 miRNAs . The dataset we mainly use in this article is CMI-9905; see Table for details.…”

“…40 KGDCMI indicates CMI by fusing attribute features and behavioral features. 18 GCNCMI aggregates the semantic and interaction information in the bipartite graph for CMI prediction. 14 JSNDCMI predicts CMI by extracting local topological structural features and functional similarity features of molecules.…”

Identifying circRNA-miRNA interaction based on multi-biological interaction fusion

“… Yu et al (2022) proposed a computational model (SGCNCMI) to identify circRNA-miRNA interactions by combining multimodal information and graph convolutional neural network. Wang et al (2022) presented a computing method (KGDCMI) to predict the interactions between circRNA and miRNA based on multi-source information fusion. It exacts RNA attribute information from sequence and similarity and captures the behavior information in RNA association based on graph-embedding algorithm.…”