Prediction of circRNA-miRNA Associations Based on Network Embedding
circRNA is a novel class of noncoding RNA with closed-loop structure. Increasing biological experiments have shown that circRNAs play an important role in many diseases by acting as a miRNA sponge to indirectly regulate the expression of miRNA target genes. Therefore, predicting associations between...
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| Main Authors: | , , , , , , , |
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| Format: | Article |
| Language: | English |
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Wiley
2021-01-01
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| Series: | Complexity |
| Online Access: | http://dx.doi.org/10.1155/2021/6659695 |
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| _version_ | 1832561464226873344 |
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| author | Wei Lan Mingrui Zhu Qingfeng Chen Jianwei Chen Jin Ye Jin Liu Wei Peng Shirui Pan |
| author_facet | Wei Lan Mingrui Zhu Qingfeng Chen Jianwei Chen Jin Ye Jin Liu Wei Peng Shirui Pan |
| author_sort | Wei Lan |
| collection | DOAJ |
| description | circRNA is a novel class of noncoding RNA with closed-loop structure. Increasing biological experiments have shown that circRNAs play an important role in many diseases by acting as a miRNA sponge to indirectly regulate the expression of miRNA target genes. Therefore, predicting associations between circRNAs and miRNAs can promote the understanding of pathogenesis of disease. In this paper, we propose a new computational method, NECMA, based on network embedding to predict potential associations between circRNAs and miRNAs. In our method, the Gaussian interaction profile (GIP) kernel similarities of circRNA and miRNA are calculated based on the known circRNA-miRNA associations, respectively. Then, the circRNA-miRNA association network, circRNA GIP kernel similarity network, and miRNA GIP kernel similarity network are utilized to construct the heterogeneous network. Furthermore, the network embedding algorithm is used to extract potential features of circRNA and miRNA from the heterogeneous network, respectively. Finally, the associations between circRNAs and miRNAs are predicted by using neighborhood regularization logic matrix decomposition and inner product. The performance of NECMA is evaluated by using ten-fold cross-validation. The results show that this method has better prediction accuracy than other state-of-the-art methods. |
| format | Article |
| id | doaj-art-e80b1e81fc2e4b6090b4fa1af8371c83 |
| institution | Kabale University |
| issn | 1076-2787 1099-0526 |
| language | English |
| publishDate | 2021-01-01 |
| publisher | Wiley |
| record_format | Article |
| series | Complexity |
| spelling | doaj-art-e80b1e81fc2e4b6090b4fa1af8371c832025-02-03T01:25:02ZengWileyComplexity1076-27871099-05262021-01-01202110.1155/2021/66596956659695Prediction of circRNA-miRNA Associations Based on Network EmbeddingWei Lan0Mingrui Zhu1Qingfeng Chen2Jianwei Chen3Jin Ye4Jin Liu5Wei Peng6Shirui Pan7School of Computer, Electronics and Information, Guangxi University, Nanning 530004, ChinaSchool of Computer, Electronics and Information, Guangxi University, Nanning 530004, ChinaSchool of Computer, Electronics and Information, Guangxi University, Nanning 530004, ChinaSchool of Computer, Electronics and Information, Guangxi University, Nanning 530004, ChinaSchool of Computer, Electronics and Information, Guangxi University, Nanning 530004, ChinaHunan Provincial Key Lab on Bioinformatics, School of Computer Science and Engineering, Central South University, Changsha 410083, ChinaFaculty of Information Engineering and Automation, Kunming University of Science and Technology, Kunming 650050, ChinaFaculty of Information Technology, Monash University, Melbourne 3800, AustraliacircRNA is a novel class of noncoding RNA with closed-loop structure. Increasing biological experiments have shown that circRNAs play an important role in many diseases by acting as a miRNA sponge to indirectly regulate the expression of miRNA target genes. Therefore, predicting associations between circRNAs and miRNAs can promote the understanding of pathogenesis of disease. In this paper, we propose a new computational method, NECMA, based on network embedding to predict potential associations between circRNAs and miRNAs. In our method, the Gaussian interaction profile (GIP) kernel similarities of circRNA and miRNA are calculated based on the known circRNA-miRNA associations, respectively. Then, the circRNA-miRNA association network, circRNA GIP kernel similarity network, and miRNA GIP kernel similarity network are utilized to construct the heterogeneous network. Furthermore, the network embedding algorithm is used to extract potential features of circRNA and miRNA from the heterogeneous network, respectively. Finally, the associations between circRNAs and miRNAs are predicted by using neighborhood regularization logic matrix decomposition and inner product. The performance of NECMA is evaluated by using ten-fold cross-validation. The results show that this method has better prediction accuracy than other state-of-the-art methods.http://dx.doi.org/10.1155/2021/6659695 |
| spellingShingle | Wei Lan Mingrui Zhu Qingfeng Chen Jianwei Chen Jin Ye Jin Liu Wei Peng Shirui Pan Prediction of circRNA-miRNA Associations Based on Network Embedding Complexity |
| title | Prediction of circRNA-miRNA Associations Based on Network Embedding |
| title_full | Prediction of circRNA-miRNA Associations Based on Network Embedding |
| title_fullStr | Prediction of circRNA-miRNA Associations Based on Network Embedding |
| title_full_unstemmed | Prediction of circRNA-miRNA Associations Based on Network Embedding |
| title_short | Prediction of circRNA-miRNA Associations Based on Network Embedding |
| title_sort | prediction of circrna mirna associations based on network embedding |
| url | http://dx.doi.org/10.1155/2021/6659695 |
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