Association prediction of lncRNAs and diseases using multiview graph convolution neural network
Long noncoding RNAs (lncRNAs) regulate physiological processes via interactions with macromolecules such as miRNAs, proteins, and genes, forming disease-associated regulatory networks. However, predicting lncRNA-disease associations remains challenging due to network complexity and isolated entities...
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| Format: | Article |
| Language: | English |
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Frontiers Media S.A.
2025-04-01
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| Series: | Frontiers in Genetics |
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| Online Access: | https://www.frontiersin.org/articles/10.3389/fgene.2025.1568270/full |
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| author | Wei Zhang Yifu Zeng Xiaowen Xiang Bihai Zhao Sai Hu Limiao Li Xiaoyu Zhu Lei Wang |
| author_facet | Wei Zhang Yifu Zeng Xiaowen Xiang Bihai Zhao Sai Hu Limiao Li Xiaoyu Zhu Lei Wang |
| author_sort | Wei Zhang |
| collection | DOAJ |
| description | Long noncoding RNAs (lncRNAs) regulate physiological processes via interactions with macromolecules such as miRNAs, proteins, and genes, forming disease-associated regulatory networks. However, predicting lncRNA-disease associations remains challenging due to network complexity and isolated entities. Here, we propose MVIGCN, a graph convolutional network (GCN)-based method integrating multimodal data to predict these associations. Our framework constructs a heterogeneous network combining disease semantics, lncRNA similarity, and miRNA-lncRNA-disease interactions to address isolation issues. By modeling topological features and multiscale relationships through deep learning with attention mechanisms, MVIGCN prioritizes critical nodes and edges, enhancing prediction accuracy. Cross-validation demonstrated improved reliability over single-view methods, highlighting its potential to identify disease-related lncRNA biomarkers. This work advances network-based computational strategies for decoding lncRNA functions in disease biology and provides a scalable tool for prioritizing therapeutic targets. |
| format | Article |
| id | doaj-art-c73d1805d04549b5bebdb113672edd52 |
| institution | OA Journals |
| issn | 1664-8021 |
| language | English |
| publishDate | 2025-04-01 |
| publisher | Frontiers Media S.A. |
| record_format | Article |
| series | Frontiers in Genetics |
| spelling | doaj-art-c73d1805d04549b5bebdb113672edd522025-08-20T02:26:31ZengFrontiers Media S.A.Frontiers in Genetics1664-80212025-04-011610.3389/fgene.2025.15682701568270Association prediction of lncRNAs and diseases using multiview graph convolution neural networkWei Zhang0Yifu Zeng1Xiaowen Xiang2Bihai Zhao3Sai Hu4Limiao Li5Xiaoyu Zhu6Lei Wang7College of Computer Science and Engineering, Changsha University, Changsha, Hunan, ChinaCollege of Computer Science and Engineering, Changsha University, Changsha, Hunan, ChinaCollege of Computer Science and Engineering, Changsha University, Changsha, Hunan, ChinaCollege of Computer Science and Engineering, Changsha University, Changsha, Hunan, ChinaDepartment of Information and Computing Science, College of Mathematics, Changsha University, Changsha, ChinaCollege of Computer Science and Engineering, Changsha University, Changsha, Hunan, ChinaCollege of Computer Science and Engineering, Changsha University, Changsha, Hunan, ChinaCollege of Computer Science and Engineering, Changsha University, Changsha, Hunan, ChinaLong noncoding RNAs (lncRNAs) regulate physiological processes via interactions with macromolecules such as miRNAs, proteins, and genes, forming disease-associated regulatory networks. However, predicting lncRNA-disease associations remains challenging due to network complexity and isolated entities. Here, we propose MVIGCN, a graph convolutional network (GCN)-based method integrating multimodal data to predict these associations. Our framework constructs a heterogeneous network combining disease semantics, lncRNA similarity, and miRNA-lncRNA-disease interactions to address isolation issues. By modeling topological features and multiscale relationships through deep learning with attention mechanisms, MVIGCN prioritizes critical nodes and edges, enhancing prediction accuracy. Cross-validation demonstrated improved reliability over single-view methods, highlighting its potential to identify disease-related lncRNA biomarkers. This work advances network-based computational strategies for decoding lncRNA functions in disease biology and provides a scalable tool for prioritizing therapeutic targets.https://www.frontiersin.org/articles/10.3389/fgene.2025.1568270/fullgraph convolutional networklncRNA-miRNAmultiview datadeep learningsimilarity network |
| spellingShingle | Wei Zhang Yifu Zeng Xiaowen Xiang Bihai Zhao Sai Hu Limiao Li Xiaoyu Zhu Lei Wang Association prediction of lncRNAs and diseases using multiview graph convolution neural network Frontiers in Genetics graph convolutional network lncRNA-miRNA multiview data deep learning similarity network |
| title | Association prediction of lncRNAs and diseases using multiview graph convolution neural network |
| title_full | Association prediction of lncRNAs and diseases using multiview graph convolution neural network |
| title_fullStr | Association prediction of lncRNAs and diseases using multiview graph convolution neural network |
| title_full_unstemmed | Association prediction of lncRNAs and diseases using multiview graph convolution neural network |
| title_short | Association prediction of lncRNAs and diseases using multiview graph convolution neural network |
| title_sort | association prediction of lncrnas and diseases using multiview graph convolution neural network |
| topic | graph convolutional network lncRNA-miRNA multiview data deep learning similarity network |
| url | https://www.frontiersin.org/articles/10.3389/fgene.2025.1568270/full |
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