Deep Neural Network-Based Prediction of B-Cell Epitopes for SARS-CoV and SARS-CoV-2: Enhancing Vaccine Design through Machine Learning
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arXiv
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| Hauptverfasser: | , , |
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| Format: | Preprint |
| Veröffentlicht: |
2024
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| _version_ | 1866916500796866560 |
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| author | Shi, Xinyu Tao, Yixin Lin, Shih-Chi |
| author_facet | Shi, Xinyu Tao, Yixin Lin, Shih-Chi |
| contents | The accurate prediction of B-cell epitopes is critical for guiding vaccine development against infectious diseases, including SARS and COVID-19. This study explores the use of a deep neural network (DNN) model to predict B-cell epitopes for SARS-CoVandSARS-CoV-2,leveraging a dataset that incorporates essential protein and peptide features. Traditional sequence-based methods often struggle with large, complex datasets, but deep learning offers promising improvements in predictive accuracy. Our model employs regularization techniques, such as dropout and early stopping, to enhance generalization, while also analyzing key features, including isoelectric point and aromaticity, that influence epitope recognition. Results indicate an overall accuracy of 82% in predicting COVID-19 negative and positive cases, with room for improvement in detecting positive samples. This research demonstrates the applicability of deep learning in epitope mapping, suggesting that such approaches can enhance the speed and precision of vaccine design for emerging pathogens. Future work could incorporate structural data and diverse viral strains to further refine prediction capabilities. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2412_00109 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | Deep Neural Network-Based Prediction of B-Cell Epitopes for SARS-CoV and SARS-CoV-2: Enhancing Vaccine Design through Machine Learning Shi, Xinyu Tao, Yixin Lin, Shih-Chi Machine Learning Computational Engineering, Finance, and Science Biomolecules The accurate prediction of B-cell epitopes is critical for guiding vaccine development against infectious diseases, including SARS and COVID-19. This study explores the use of a deep neural network (DNN) model to predict B-cell epitopes for SARS-CoVandSARS-CoV-2,leveraging a dataset that incorporates essential protein and peptide features. Traditional sequence-based methods often struggle with large, complex datasets, but deep learning offers promising improvements in predictive accuracy. Our model employs regularization techniques, such as dropout and early stopping, to enhance generalization, while also analyzing key features, including isoelectric point and aromaticity, that influence epitope recognition. Results indicate an overall accuracy of 82% in predicting COVID-19 negative and positive cases, with room for improvement in detecting positive samples. This research demonstrates the applicability of deep learning in epitope mapping, suggesting that such approaches can enhance the speed and precision of vaccine design for emerging pathogens. Future work could incorporate structural data and diverse viral strains to further refine prediction capabilities. |
| title | Deep Neural Network-Based Prediction of B-Cell Epitopes for SARS-CoV and SARS-CoV-2: Enhancing Vaccine Design through Machine Learning |
| topic | Machine Learning Computational Engineering, Finance, and Science Biomolecules |
| url | https://arxiv.org/abs/2412.00109 |