Association prediction of lncRNAs and diseases using multiview graph convolution neural network
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| Titel: | Association prediction of lncRNAs and diseases using multiview graph convolution neural network |
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| Autoren: | Wei Zhang, Yifu Zeng, Xiaowen Xiang, Bihai Zhao, Sai Hu, Limiao Li, Xiaoyu Zhu, Lei Wang |
| Quelle: | Front Genet Frontiers in Genetics, Vol 16 (2025) |
| Verlagsinformationen: | Frontiers Media SA, 2025. |
| Publikationsjahr: | 2025 |
| Schlagwörter: | similarity network, graph convolutional network, multiview data, Genetics, deep learning, QH426-470, lncRNA-miRNA |
| Beschreibung: | 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. |
| Publikationsart: | Article Other literature type |
| ISSN: | 1664-8021 |
| DOI: | 10.3389/fgene.2025.1568270 |
| Zugangs-URL: | https://pubmed.ncbi.nlm.nih.gov/40303981 https://doaj.org/article/c73d1805d04549b5bebdb113672edd52 |
| Rights: | CC BY |
| Dokumentencode: | edsair.doi.dedup.....f424c2edd38c7da9ea32998a552cdba2 |
| Datenbank: | OpenAIRE |
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Nájsť tento článok vo Web of Science | Abstract: | 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. |
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| ISSN: | 16648021 |
| DOI: | 10.3389/fgene.2025.1568270 |