针对SARS-CoV-2变种的增强深层卷积神经网络分类
Olaitan I Awe1,2, Hesborn Obura3,4, Charles Ssemuyiga5,6
1African Society for Bioinformatics and Computational Biology, Cape Town, South Africa.
Frontiers in artificial intelligence
|September 24, 2025
概括
一个新的深度学习模型仅使用SARS-CoV-2尖端基因序列,快速准确地分类变异. 这种方法支持基因组监测,特别是当全基因组数据有限时.
科学领域:
- 基因组学就是基因组学.
- 病毒学 病毒学
- 机器学习 机器学习
背景情况:
- 对SARS-CoV-2的基因组监测对于跟踪变种至关重要.
- 全基因组测序可能是资源密集的.
- 尖端基因序列为快速分类提供了一个潜在的替代方案.
研究的目的:
- 开发和评估一种用于分类SARS-CoV-2变异的深度学习模型,仅使用尖端基因序列.
- 评估模型的性能与现有工具 (如Nextclade) 相比.
- 探索模型分类的可解释性.
主要方法:
- 精选了35,800个经过优质过的SARS-CoV-2尖峰序列.
- 训练了一种混合卷积神经网络-双向长期短期记忆 (CNN-BiLSTM) 模型.
- 与Nextclade和经典机器学习模型进行基准性能测试.
主要成果:
- 该CNN-BiLSTM模型实现了高精度 (例如99.91%的测试精度).
- 该模型正确识别了100%的Omicron序列,显著优于Nextclade (34.95%).
- 度分析确定了已知的和新的突变动机.
结论:
- 只有尖端的深度学习模型提供了快速而准确的SARS-CoV-2变种分类.
- 这些模型是植物遗传学方法的宝贵补充,特别是在资源有限的环境中.
- 这种方法使得有效的样本分拣可用于进一步的基因组分析和及时监测.
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