Prediction of circRNA–Disease Associations via Graph Isomorphism Transformer and Dual-Stream Neural Predictor
Circular RNAs (circRNAs) have attracted increasing attention for their roles in human diseases, making the prediction of circRNA–disease associations (CDAs) a critical research area for advancing disease diagnosis and treatment. However, traditional experimental methods for exploring CDAs are time-c...
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| Format: | Article |
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MDPI AG
2025-02-01
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| Series: | Biomolecules |
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| Online Access: | https://www.mdpi.com/2218-273X/15/2/234 |
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| author | Hongchan Li Yuchao Qian Zhongchuan Sun Haodong Zhu |
| author_facet | Hongchan Li Yuchao Qian Zhongchuan Sun Haodong Zhu |
| author_sort | Hongchan Li |
| collection | DOAJ |
| description | Circular RNAs (circRNAs) have attracted increasing attention for their roles in human diseases, making the prediction of circRNA–disease associations (CDAs) a critical research area for advancing disease diagnosis and treatment. However, traditional experimental methods for exploring CDAs are time-consuming and resource-intensive, while existing computational models often struggle with the sparsity of CDA data and fail to uncover potential associations effectively. To address these challenges, we propose a novel CDA prediction method named the Graph Isomorphism Transformer with Dual-Stream Neural Predictor (GIT-DSP), which leverages knowledge graph technology to address data sparsity and predict CDAs more effectively. Specifically, the model incorporates multiple associations between circRNAs, diseases, and other non-coding RNAs (e.g., lncRNAs, and miRNAs) to construct a multi-source heterogeneous knowledge graph, thereby expanding the scope of CDA exploration. Subsequently, a Graph Isomorphism Transformer model is proposed to fully exploit both local and global association information within the knowledge graph, enabling deeper insights into potential CDAs. Furthermore, a Dual-Stream Neural Predictor is introduced to accurately predict complex circRNA–disease associations in the knowledge graph by integrating dual-stream predictive features. Experimental results demonstrate that GIT-DSP outperforms existing state-of-the-art models, offering valuable insights for precision medicine and disease-related research. |
| format | Article |
| id | doaj-art-c9fec2e65bb84236be8d40438bebe9eb |
| institution | DOAJ |
| issn | 2218-273X |
| language | English |
| publishDate | 2025-02-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Biomolecules |
| spelling | doaj-art-c9fec2e65bb84236be8d40438bebe9eb2025-08-20T02:44:59ZengMDPI AGBiomolecules2218-273X2025-02-0115223410.3390/biom15020234Prediction of circRNA–Disease Associations via Graph Isomorphism Transformer and Dual-Stream Neural PredictorHongchan Li0Yuchao Qian1Zhongchuan Sun2Haodong Zhu3School of Computer Science and Technology, Zhengzhou University of Light Industry, Zhengzhou 450000, ChinaSchool of Computer Science and Technology, Zhengzhou University of Light Industry, Zhengzhou 450000, ChinaSchool of Computer Science and Technology, Zhengzhou University of Light Industry, Zhengzhou 450000, ChinaSchool of Computer Science and Technology, Zhengzhou University of Light Industry, Zhengzhou 450000, ChinaCircular RNAs (circRNAs) have attracted increasing attention for their roles in human diseases, making the prediction of circRNA–disease associations (CDAs) a critical research area for advancing disease diagnosis and treatment. However, traditional experimental methods for exploring CDAs are time-consuming and resource-intensive, while existing computational models often struggle with the sparsity of CDA data and fail to uncover potential associations effectively. To address these challenges, we propose a novel CDA prediction method named the Graph Isomorphism Transformer with Dual-Stream Neural Predictor (GIT-DSP), which leverages knowledge graph technology to address data sparsity and predict CDAs more effectively. Specifically, the model incorporates multiple associations between circRNAs, diseases, and other non-coding RNAs (e.g., lncRNAs, and miRNAs) to construct a multi-source heterogeneous knowledge graph, thereby expanding the scope of CDA exploration. Subsequently, a Graph Isomorphism Transformer model is proposed to fully exploit both local and global association information within the knowledge graph, enabling deeper insights into potential CDAs. Furthermore, a Dual-Stream Neural Predictor is introduced to accurately predict complex circRNA–disease associations in the knowledge graph by integrating dual-stream predictive features. Experimental results demonstrate that GIT-DSP outperforms existing state-of-the-art models, offering valuable insights for precision medicine and disease-related research.https://www.mdpi.com/2218-273X/15/2/234circRNA–disease associationstransformergraph isomorphism networkknowledge representation learning |
| spellingShingle | Hongchan Li Yuchao Qian Zhongchuan Sun Haodong Zhu Prediction of circRNA–Disease Associations via Graph Isomorphism Transformer and Dual-Stream Neural Predictor Biomolecules circRNA–disease associations transformer graph isomorphism network knowledge representation learning |
| title | Prediction of circRNA–Disease Associations via Graph Isomorphism Transformer and Dual-Stream Neural Predictor |
| title_full | Prediction of circRNA–Disease Associations via Graph Isomorphism Transformer and Dual-Stream Neural Predictor |
| title_fullStr | Prediction of circRNA–Disease Associations via Graph Isomorphism Transformer and Dual-Stream Neural Predictor |
| title_full_unstemmed | Prediction of circRNA–Disease Associations via Graph Isomorphism Transformer and Dual-Stream Neural Predictor |
| title_short | Prediction of circRNA–Disease Associations via Graph Isomorphism Transformer and Dual-Stream Neural Predictor |
| title_sort | prediction of circrna disease associations via graph isomorphism transformer and dual stream neural predictor |
| topic | circRNA–disease associations transformer graph isomorphism network knowledge representation learning |
| url | https://www.mdpi.com/2218-273X/15/2/234 |
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