KGDCMI: A New Approach for Predicting circRNA–miRNA Interactions From Multi-Source Information Extraction and Deep Learning
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 th...
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| Format: | Article |
| Language: | English |
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Frontiers Media S.A.
2022-08-01
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| Series: | Frontiers in Genetics |
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| Online Access: | https://www.frontiersin.org/articles/10.3389/fgene.2022.958096/full |
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| author | Xin-Fei Wang Chang-Qing Yu Li-Ping Li Li-Ping Li Zhu-Hong You Wen-Zhun Huang Yue-Chao Li Zhong-Hao Ren Yong-Jian Guan |
| author_facet | Xin-Fei Wang Chang-Qing Yu Li-Ping Li Li-Ping Li Zhu-Hong You Wen-Zhun Huang Yue-Chao Li Zhong-Hao Ren Yong-Jian Guan |
| author_sort | Xin-Fei Wang |
| collection | DOAJ |
| description | 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. |
| first_indexed | 2024-04-13T13:16:01Z |
| format | Article |
| id | doaj.art-3950b2d6539f42e687414dec88e8500d |
| institution | Directory Open Access Journal |
| issn | 1664-8021 |
| language | English |
| last_indexed | 2024-04-13T13:16:01Z |
| publishDate | 2022-08-01 |
| publisher | Frontiers Media S.A. |
| record_format | Article |
| series | Frontiers in Genetics |
| spelling | doaj.art-3950b2d6539f42e687414dec88e8500d2022-12-22T02:45:27ZengFrontiers Media S.A.Frontiers in Genetics1664-80212022-08-011310.3389/fgene.2022.958096958096KGDCMI: A New Approach for Predicting circRNA–miRNA Interactions From Multi-Source Information Extraction and Deep LearningXin-Fei Wang0Chang-Qing Yu1Li-Ping Li2Li-Ping Li3Zhu-Hong You4Wen-Zhun Huang5Yue-Chao Li6Zhong-Hao Ren7Yong-Jian Guan8School of Information Engineering, Xijing University, Xi’an, ChinaSchool of Information Engineering, Xijing University, Xi’an, ChinaSchool of Information Engineering, Xijing University, Xi’an, ChinaCollege of Grassland and Environment Sciences, Xinjiang Agricultural University, Urumqi, ChinaSchool of Computer Science, Northwestern Polytechnical University, Xi’an, ChinaSchool of Information Engineering, Xijing University, Xi’an, ChinaSchool of Information Engineering, Xijing University, Xi’an, ChinaSchool of Information Engineering, Xijing University, Xi’an, ChinaSchool of Information Engineering, Xijing University, Xi’an, ChinaEmerging 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.https://www.frontiersin.org/articles/10.3389/fgene.2022.958096/fullcircRNA–miRNA interactioncircRNAdeep neural networkgraph embeddingK-mer |
| spellingShingle | Xin-Fei Wang Chang-Qing Yu Li-Ping Li Li-Ping Li Zhu-Hong You Wen-Zhun Huang Yue-Chao Li Zhong-Hao Ren Yong-Jian Guan KGDCMI: A New Approach for Predicting circRNA–miRNA Interactions From Multi-Source Information Extraction and Deep Learning Frontiers in Genetics circRNA–miRNA interaction circRNA deep neural network graph embedding K-mer |
| title | KGDCMI: A New Approach for Predicting circRNA–miRNA Interactions From Multi-Source Information Extraction and Deep Learning |
| title_full | KGDCMI: A New Approach for Predicting circRNA–miRNA Interactions From Multi-Source Information Extraction and Deep Learning |
| title_fullStr | KGDCMI: A New Approach for Predicting circRNA–miRNA Interactions From Multi-Source Information Extraction and Deep Learning |
| title_full_unstemmed | KGDCMI: A New Approach for Predicting circRNA–miRNA Interactions From Multi-Source Information Extraction and Deep Learning |
| title_short | KGDCMI: A New Approach for Predicting circRNA–miRNA Interactions From Multi-Source Information Extraction and Deep Learning |
| title_sort | kgdcmi a new approach for predicting circrna mirna interactions from multi source information extraction and deep learning |
| topic | circRNA–miRNA interaction circRNA deep neural network graph embedding K-mer |
| url | https://www.frontiersin.org/articles/10.3389/fgene.2022.958096/full |
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