通过使用深度自动编码器检测异常来预测SARS-CoV-2血统的主导地位
Simone Rancati1, Giovanna Nicora1, Mattia Prosperi2,3
1Department of Electrical, Computer and Biomedical Engineering, University of Pavia, Pavia, Italy.
bioRxiv : the preprint server for biology
|November 14, 2023
概括
DeepAutoCoV是一种新型的深度学习系统,可以预测未来的主导性严重急性呼吸综合征冠状病毒2 (SARS-CoV-2) 血统的高精度和早期预警时间. 这一进步有助于预防性公共卫生策略对抗不断演变的COVID-19变种.
科学领域:
- 病毒学 病毒学
- 基因组学就是基因组学.
- 计算生物学 计算生物学
背景情况:
- COVID-19 流行病的标志是 SARS-CoV-2 变种的持续出现,具有增强的传染性和免疫逃避.
- 预测新的主导血统的兴起对于有效的公共卫生反应至关重要.
研究的目的:
- 开发和验证DeepAutoCoV,这是一个无监督的深度学习系统,用于预测SARS-CoV-2的未来主导系 (FDL).
- 与基线方法相比,评估系统在早期检测和预测准确度方面的表现.
主要方法:
- 在超过1600万个SARS-CoV-2尖端蛋白序列上训练了一种无监督的深度学习异常检测系统 (DeepAutoCoV).
- 定义FDL为 (子) 血统,占每周GISAID提交的>10%.
- 通过使用全球和国家特定数据集验证了该系统,数据集覆盖了大约4年.
主要成果:
- DeepAutoCoV成功地在非常低的频率 (0.01%-3%) 上识别了FDL,中位数的预测时间为4-17周.
- 该系统表现出卓越的预测能力,比基线方法好大约5倍和25倍.
- 确定了与增加病毒适应性相关的特定突变,提供了可解释的见解.
结论:
- DeepAutoCoV提供了一个强大的工具,用于早期检测和预测新出现的SARS-CoV-2血统.
- 该系统能够提前显著地标记像B.1.617.2这样的变种,从而支持积极的公共卫生干预.
- 来自DeepAutoCoV的可解释输出可以指导针对不断发展的病毒威胁的预防策略的优化.
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