Fusing graph transformer with multi-aggregate GCN for enhanced drug–disease associations prediction
Abstract Background Identification of potential drug–disease associations is important for both the discovery of new indications for drugs and for the reduction of unknown adverse drug reactions. Exploring the potential links between drugs and diseases is crucial for advancing biomedical research an...
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| Format: | Article |
| Language: | English |
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BMC
2024-02-01
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| Series: | BMC Bioinformatics |
| Subjects: | |
| Online Access: | https://doi.org/10.1186/s12859-024-05705-w |
| _version_ | 1797273042658787328 |
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| author | Shihui He Lijun Yun Haicheng Yi |
| author_facet | Shihui He Lijun Yun Haicheng Yi |
| author_sort | Shihui He |
| collection | DOAJ |
| description | Abstract Background Identification of potential drug–disease associations is important for both the discovery of new indications for drugs and for the reduction of unknown adverse drug reactions. Exploring the potential links between drugs and diseases is crucial for advancing biomedical research and improving healthcare. While advanced computational techniques play a vital role in revealing the connections between drugs and diseases, current research still faces challenges in the process of mining potential relationships between drugs and diseases using heterogeneous network data. Results In this study, we propose a learning framework for fusing Graph Transformer Networks and multi-aggregate graph convolutional network to learn efficient heterogenous information graph representations for drug–disease association prediction, termed WMAGT. This method extensively harnesses the capabilities of a robust graph transformer, effectively modeling the local and global interactions of nodes by integrating a graph convolutional network and a graph transformer with self-attention mechanisms in its encoder. We first integrate drug–drug, drug–disease, and disease–disease networks to construct heterogeneous information graph. Multi-aggregate graph convolutional network and graph transformer are then used in conjunction with neural collaborative filtering module to integrate information from different domains into highly effective feature representation. Conclusions Rigorous cross-validation, ablation studies examined the robustness and effectiveness of the proposed method. Experimental results demonstrate that WMAGT outperforms other state-of-the-art methods in accurate drug–disease association prediction, which is beneficial for drug repositioning and drug safety research. |
| first_indexed | 2024-03-07T14:37:52Z |
| format | Article |
| id | doaj.art-7fd20ccd36d246e09e6c032fe82c10de |
| institution | Directory Open Access Journal |
| issn | 1471-2105 |
| language | English |
| last_indexed | 2024-03-07T14:37:52Z |
| publishDate | 2024-02-01 |
| publisher | BMC |
| record_format | Article |
| series | BMC Bioinformatics |
| spelling | doaj.art-7fd20ccd36d246e09e6c032fe82c10de2024-03-05T20:31:37ZengBMCBMC Bioinformatics1471-21052024-02-0125111810.1186/s12859-024-05705-wFusing graph transformer with multi-aggregate GCN for enhanced drug–disease associations predictionShihui He0Lijun Yun1Haicheng Yi2School of Information Science and Technology, Yunnan Normal UniversitySchool of Information Science and Technology, Yunnan Normal UniversitySchool of Computer Science, Northwestern Polytechnical UniversityAbstract Background Identification of potential drug–disease associations is important for both the discovery of new indications for drugs and for the reduction of unknown adverse drug reactions. Exploring the potential links between drugs and diseases is crucial for advancing biomedical research and improving healthcare. While advanced computational techniques play a vital role in revealing the connections between drugs and diseases, current research still faces challenges in the process of mining potential relationships between drugs and diseases using heterogeneous network data. Results In this study, we propose a learning framework for fusing Graph Transformer Networks and multi-aggregate graph convolutional network to learn efficient heterogenous information graph representations for drug–disease association prediction, termed WMAGT. This method extensively harnesses the capabilities of a robust graph transformer, effectively modeling the local and global interactions of nodes by integrating a graph convolutional network and a graph transformer with self-attention mechanisms in its encoder. We first integrate drug–drug, drug–disease, and disease–disease networks to construct heterogeneous information graph. Multi-aggregate graph convolutional network and graph transformer are then used in conjunction with neural collaborative filtering module to integrate information from different domains into highly effective feature representation. Conclusions Rigorous cross-validation, ablation studies examined the robustness and effectiveness of the proposed method. Experimental results demonstrate that WMAGT outperforms other state-of-the-art methods in accurate drug–disease association prediction, which is beneficial for drug repositioning and drug safety research.https://doi.org/10.1186/s12859-024-05705-wDrug repositioningDrug–disease associationsGraph transformerGraph neural networksNeural collaborative filtering |
| spellingShingle | Shihui He Lijun Yun Haicheng Yi Fusing graph transformer with multi-aggregate GCN for enhanced drug–disease associations prediction BMC Bioinformatics Drug repositioning Drug–disease associations Graph transformer Graph neural networks Neural collaborative filtering |
| title | Fusing graph transformer with multi-aggregate GCN for enhanced drug–disease associations prediction |
| title_full | Fusing graph transformer with multi-aggregate GCN for enhanced drug–disease associations prediction |
| title_fullStr | Fusing graph transformer with multi-aggregate GCN for enhanced drug–disease associations prediction |
| title_full_unstemmed | Fusing graph transformer with multi-aggregate GCN for enhanced drug–disease associations prediction |
| title_short | Fusing graph transformer with multi-aggregate GCN for enhanced drug–disease associations prediction |
| title_sort | fusing graph transformer with multi aggregate gcn for enhanced drug disease associations prediction |
| topic | Drug repositioning Drug–disease associations Graph transformer Graph neural networks Neural collaborative filtering |
| url | https://doi.org/10.1186/s12859-024-05705-w |
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