A Novel Approach Utilizing Domain Adversarial Neural Networks for the Detection and Classification of Selective Sweeps
The identification and classification of selective sweeps are of great significance for improving the understanding of biological evolution and exploring opportunities for precision medicine and genetic improvement. Here, a domain adaptation sweep detection and classification (DASDC) method is prese...
| Main Authors: | , , , , , , , |
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| Format: | Journal Article |
| Language: | Inglés |
| Published: |
Wiley
2024
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| Subjects: | |
| Online Access: | https://hdl.handle.net/10568/140505 |
| _version_ | 1855532020414808064 |
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| author | Hui Song Jinyu Chu Wangjiao Li Xinyun Li Lingzhao Fang Han Jianlin Shuhong Zhao Yunlong Ma |
| author_browse | Han Jianlin Hui Song Jinyu Chu Lingzhao Fang Shuhong Zhao Wangjiao Li Xinyun Li Yunlong Ma |
| author_facet | Hui Song Jinyu Chu Wangjiao Li Xinyun Li Lingzhao Fang Han Jianlin Shuhong Zhao Yunlong Ma |
| author_sort | Hui Song |
| collection | Repository of Agricultural Research Outputs (CGSpace) |
| description | The identification and classification of selective sweeps are of great significance for improving the understanding of biological evolution and exploring opportunities for precision medicine and genetic improvement. Here, a domain adaptation sweep detection and classification (DASDC) method is presented to balance the alignment of two domains and the classification performance through a domain-adversarial neural network and its adversarial learning modules. DASDC effectively addresses the issue of mismatch between training data and real genomic data in deep learning models, leading to a significant improvement in its generalization capability, prediction robustness, and accuracy. The DASDC method demonstrates improved identification performance compared to existing methods and excels in classification performance, particularly in scenarios where there is a mismatch between application data and training data. The successful implementation of DASDC in real data of three distinct species highlights its potential as a useful tool for identifying crucial functional genes and investigating adaptive evolutionary mechanisms, particularly with the increasing availability of genomic data. |
| format | Journal Article |
| id | CGSpace140505 |
| institution | CGIAR Consortium |
| language | Inglés |
| publishDate | 2024 |
| publishDateRange | 2024 |
| publishDateSort | 2024 |
| publisher | Wiley |
| publisherStr | Wiley |
| record_format | dspace |
| spelling | CGSpace1405052025-10-26T13:02:25Z A Novel Approach Utilizing Domain Adversarial Neural Networks for the Detection and Classification of Selective Sweeps Hui Song Jinyu Chu Wangjiao Li Xinyun Li Lingzhao Fang Han Jianlin Shuhong Zhao Yunlong Ma data genomics training The identification and classification of selective sweeps are of great significance for improving the understanding of biological evolution and exploring opportunities for precision medicine and genetic improvement. Here, a domain adaptation sweep detection and classification (DASDC) method is presented to balance the alignment of two domains and the classification performance through a domain-adversarial neural network and its adversarial learning modules. DASDC effectively addresses the issue of mismatch between training data and real genomic data in deep learning models, leading to a significant improvement in its generalization capability, prediction robustness, and accuracy. The DASDC method demonstrates improved identification performance compared to existing methods and excels in classification performance, particularly in scenarios where there is a mismatch between application data and training data. The successful implementation of DASDC in real data of three distinct species highlights its potential as a useful tool for identifying crucial functional genes and investigating adaptive evolutionary mechanisms, particularly with the increasing availability of genomic data. 2024-04 2024-03-19T10:54:57Z 2024-03-19T10:54:57Z Journal Article https://hdl.handle.net/10568/140505 en Open Access Wiley Song, H., Chu, J., Li, W., Li, X., Fang, L., Han, J., Zhao, S., and Ma, y. 2024. A Novel Approach Utilizing Domain Adversarial Neural Networks for the Detection and Classification of Selective Sweeps. Advanced Science 11(14): 2304842. |
| spellingShingle | data genomics training Hui Song Jinyu Chu Wangjiao Li Xinyun Li Lingzhao Fang Han Jianlin Shuhong Zhao Yunlong Ma A Novel Approach Utilizing Domain Adversarial Neural Networks for the Detection and Classification of Selective Sweeps |
| title | A Novel Approach Utilizing Domain Adversarial Neural Networks for the Detection and Classification of Selective Sweeps |
| title_full | A Novel Approach Utilizing Domain Adversarial Neural Networks for the Detection and Classification of Selective Sweeps |
| title_fullStr | A Novel Approach Utilizing Domain Adversarial Neural Networks for the Detection and Classification of Selective Sweeps |
| title_full_unstemmed | A Novel Approach Utilizing Domain Adversarial Neural Networks for the Detection and Classification of Selective Sweeps |
| title_short | A Novel Approach Utilizing Domain Adversarial Neural Networks for the Detection and Classification of Selective Sweeps |
| title_sort | novel approach utilizing domain adversarial neural networks for the detection and classification of selective sweeps |
| topic | data genomics training |
| url | https://hdl.handle.net/10568/140505 |
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