GBNSS: A Method Based on Graph Neural Networks (GNNs) for Global Biological Network Similarity Search
Biological network similarity search plays a crucial role in the analysis of biological networks for human disease research and drug discovery. A biological network similarity search aims to efficiently identify novel networks biologically homologous to the query networks. Great progress has been ac...
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| Format: | Article |
| Language: | English |
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MDPI AG
2024-10-01
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| Series: | Applied Sciences |
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| Online Access: | https://www.mdpi.com/2076-3417/14/21/9844 |
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| author | Yi Wang Feng Zhan Cuiyu Huang Yiran Huang |
| author_facet | Yi Wang Feng Zhan Cuiyu Huang Yiran Huang |
| author_sort | Yi Wang |
| collection | DOAJ |
| description | Biological network similarity search plays a crucial role in the analysis of biological networks for human disease research and drug discovery. A biological network similarity search aims to efficiently identify novel networks biologically homologous to the query networks. Great progress has been achieved in biological network similarity searches. However, it remains a challenge to mine the biological network information fully to improve the accuracy of query results without increasing time overheads. In this study, we propose a biological network similarity search method based on graph neural networks named GBNSS, which combines topological and biological information (GO annotations) of biological networks into graph neural networks to find topologically and biologically similar biological networks in the database. Additionally, GBNSS is a topology-free biological network similarity search method with an arbitrary network structure. The experimental results on four benchmark datasets show that GBNSS outperforms the existing methods in terms of computational efficiency and search accuracy. Case studies further demonstrate that GBNSS is capable of searching similar networks in real-world biological networks. |
| format | Article |
| id | doaj-art-a192adae03a14bf0854b637b62b86b6c |
| institution | OA Journals |
| issn | 2076-3417 |
| language | English |
| publishDate | 2024-10-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Applied Sciences |
| spelling | doaj-art-a192adae03a14bf0854b637b62b86b6c2025-08-20T02:14:15ZengMDPI AGApplied Sciences2076-34172024-10-011421984410.3390/app14219844GBNSS: A Method Based on Graph Neural Networks (GNNs) for Global Biological Network Similarity SearchYi Wang0Feng Zhan1Cuiyu Huang2Yiran Huang3School of Computer and Electronics Information, Guangxi University, Nanning 530004, ChinaSchool of Computer and Electronics Information, Guangxi University, Nanning 530004, ChinaTianjin Key Laboratory of Biosensing and Molecular Recognition, College of Chemistry, Nankai University, Tianjin 300071, ChinaSchool of Computer and Electronics Information, Guangxi University, Nanning 530004, ChinaBiological network similarity search plays a crucial role in the analysis of biological networks for human disease research and drug discovery. A biological network similarity search aims to efficiently identify novel networks biologically homologous to the query networks. Great progress has been achieved in biological network similarity searches. However, it remains a challenge to mine the biological network information fully to improve the accuracy of query results without increasing time overheads. In this study, we propose a biological network similarity search method based on graph neural networks named GBNSS, which combines topological and biological information (GO annotations) of biological networks into graph neural networks to find topologically and biologically similar biological networks in the database. Additionally, GBNSS is a topology-free biological network similarity search method with an arbitrary network structure. The experimental results on four benchmark datasets show that GBNSS outperforms the existing methods in terms of computational efficiency and search accuracy. Case studies further demonstrate that GBNSS is capable of searching similar networks in real-world biological networks.https://www.mdpi.com/2076-3417/14/21/9844biological network searchgraph neural networksgraph convolutional networksGO annotations |
| spellingShingle | Yi Wang Feng Zhan Cuiyu Huang Yiran Huang GBNSS: A Method Based on Graph Neural Networks (GNNs) for Global Biological Network Similarity Search Applied Sciences biological network search graph neural networks graph convolutional networks GO annotations |
| title | GBNSS: A Method Based on Graph Neural Networks (GNNs) for Global Biological Network Similarity Search |
| title_full | GBNSS: A Method Based on Graph Neural Networks (GNNs) for Global Biological Network Similarity Search |
| title_fullStr | GBNSS: A Method Based on Graph Neural Networks (GNNs) for Global Biological Network Similarity Search |
| title_full_unstemmed | GBNSS: A Method Based on Graph Neural Networks (GNNs) for Global Biological Network Similarity Search |
| title_short | GBNSS: A Method Based on Graph Neural Networks (GNNs) for Global Biological Network Similarity Search |
| title_sort | gbnss a method based on graph neural networks gnns for global biological network similarity search |
| topic | biological network search graph neural networks graph convolutional networks GO annotations |
| url | https://www.mdpi.com/2076-3417/14/21/9844 |
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