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Yao Z, Li F, Xie W, Chen J, Wu J, Zhan Y, Wu X, Wang Z, Zhang G. DeepSF-4mC: A deep learning model for predicting DNA cytosine 4mC methylation sites leveraging sequence features. Comput Biol Med 2024; 171:108166. [PMID: 38382385 DOI: 10.1016/j.compbiomed.2024.108166] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/06/2023] [Revised: 02/15/2024] [Accepted: 02/15/2024] [Indexed: 02/23/2024]
Abstract
N4-methylcytosine (4mC) is a DNA modification involving the addition of a methyl group to the fourth nitrogen atom of the cytosine base. This modification may influence gene regulation, providing potential insights into gene control mechanisms. Traditional laboratory methods for detecting 4mC DNA methylation have limitations, but the rise of artificial intelligence has introduced efficient computational strategies for 4mC site prediction. Despite this progress, challenges persist in terms of model performance and interpretability. To tackle these challenges, we propose DeepSF-4mC, a deep learning model specifically designed for predicting DNA cytosine 4mC methylation sites by leveraging sequence features. Our approach incorporates multiple encoding techniques to enhance prediction accuracy, increase model stability, and reduce the computational resources needed. Leveraging transfer learning, we harness existing models to enhance performance through learned representations or fine-tuning. Ensemble learning techniques combine predictions from multiple models, boosting robustness and accuracy. This research contributes to DNA methylation analysis and lays the groundwork for understanding 4mC's multifaceted role in biological processes. The web server for DeepSF-4mC is accessible at: http://deepsf-4mc.top/and the original code can be found at: https://github.com/754131799/DeepSF-4mC.
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Affiliation(s)
- Zhaomin Yao
- Department of Nuclear Medicine, General Hospital of Northern Theater Command, Shenyang, Liaoning, 110016, China; College of Medicine and Biological Information Engineering, Northeastern University, Shenyang, Liaoning, 110167, China
| | - Fei Li
- College of Computer Science and Technology, Jilin University, Changchun, Jilin, 130012, China
| | - Weiming Xie
- Department of Nuclear Medicine, General Hospital of Northern Theater Command, Shenyang, Liaoning, 110016, China; College of Medicine and Biological Information Engineering, Northeastern University, Shenyang, Liaoning, 110167, China
| | - Jiaming Chen
- Department of Nuclear Medicine, General Hospital of Northern Theater Command, Shenyang, Liaoning, 110016, China; College of Medicine and Biological Information Engineering, Northeastern University, Shenyang, Liaoning, 110167, China
| | - Jiezhang Wu
- Department of Nuclear Medicine, General Hospital of Northern Theater Command, Shenyang, Liaoning, 110016, China; College of Medicine and Biological Information Engineering, Northeastern University, Shenyang, Liaoning, 110167, China
| | - Ying Zhan
- Department of Nuclear Medicine, General Hospital of Northern Theater Command, Shenyang, Liaoning, 110016, China; College of Medicine and Biological Information Engineering, Northeastern University, Shenyang, Liaoning, 110167, China
| | - Xiaodan Wu
- Department of Nuclear Medicine, General Hospital of Northern Theater Command, Shenyang, Liaoning, 110016, China
| | - Zhiguo Wang
- Department of Nuclear Medicine, General Hospital of Northern Theater Command, Shenyang, Liaoning, 110016, China; College of Medicine and Biological Information Engineering, Northeastern University, Shenyang, Liaoning, 110167, China.
| | - Guoxu Zhang
- Department of Nuclear Medicine, General Hospital of Northern Theater Command, Shenyang, Liaoning, 110016, China; College of Medicine and Biological Information Engineering, Northeastern University, Shenyang, Liaoning, 110167, China.
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