利用基因组签名,通过机器和深度学习技术,了解SARS-CoV-2的动态
Ahmed M A Elsherbini1, Amr Hassan Elkholy1, Youssef M Fadel1
1Bioinformatics Group, Center for Informatics Science, School of Information Technology and Computer Science, Nile University, Giza, Egypt.
BMC bioinformatics
|March 28, 2024
概括
一个新的工具,GenoSig,使用核酸频率和机器学习快速分类SARS-CoV-2菌株和跟踪变体,为流行病学研究提供无对齐的替代方案.
科学领域:
- 基因组学和生物信息学
- 流行病学 流行病学
- 机器学习应用 机器学习应用
背景情况:
- 在SARS-CoV-2的流行病需要有效的方法来跟踪病毒的演变和传播.
- 基于对齐的传统遗传学方法对于大型基因组数据集来说是计算密集的.
- 需要快速,无对齐的方法来表征病毒菌株和监测变异.
研究的目的:
- 介绍GenoSig,一种用于SARS-CoV-2菌株表征的新型,无对齐的工具.
- 用核酸频率签名来评估机器学习 (ML) 和深度学习 (DL) 模型的有效性.
- 评估工具在分类谱系和大陆起源预测方面的表现.
主要方法:
- 开发了GenoSig,这是一个C++工具,使用了Di和Tri核酸频率签名.
- 应用各种ML和DL模型,包括随机森林 (RF) 和深度学习架构.
- 交叉验证和对独立数据集的验证,以评估类和大陆起源的准确性.
主要成果:
- 基诺Sig实现了高的交叉验证准确率:DL的 87.88% (±0.013) 和RF的 86.37% (±0.0009).
- 后来的SARS-CoV-2菌群 (GRA,GRY,GK) 的预测比早期的 (G,GH) 更准确.
- 模型显示欧洲,北美和南美的准确性更高,在大陆分析中DL超过RF.
结论:
- 基诺西格为SARS-CoV-2的分类学和流行病学分析提供了一种快速而简单的无对齐方法.
- 该工具有效地利用核酸频率特征与ML / DL模型用于变体表征.
- 这种方法可以适应SARS-CoV-2之外的基因组学和流行病学中类似的研究问题.
关键词:
深度学习 (Deep Learning) 是一种深度学习.一个二核酸的频率.基因标志 基因标志 基因标志基因组签名 基因组签名机器学习 机器学习随机的森林 随机的森林这就是SARS-CoV-2病毒.三核酸的频率是什么更多相关视频
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