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High-throughput Detection Method for Influenza Virus
Published on: February 4, 2012
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错误分类:使用深度神经网络识别流感A病毒的动物性转变生物标志物候选者
Nissrine Hatibi1,2, Maude Dumont-Lagacé3, Zakaria Alouani2
1Ecole Nationale Supérieure d'Informatique et d'Analyse des Systèmes, Mohammed V University in Rabat, Rabat, Morocco.
Frontiers in genetics
|August 14, 2023
概括
预测动物性流感A病毒菌株对于预防流行病至关重要. 深度学习模型在病毒序列中确定了特定的遗传特征,这些特征表明跨物种传播到人类的风险更高.
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:
- 动物传播的甲型流感病毒构成重大威胁,导致广泛的流行病和高发病率和死亡率.
- 预测哪些病毒株可能在物种之间过渡对于有效的预防和应对策略至关重要.
- 鉴定动物性转变的生物标志物可以提高我们预测和减轻流行病风险的能力.
研究的目的:
- 开发和验证基于遗传序列预测病毒宿主的深度学习模型.
- 确定与A型流感病毒的动物性转变潜力相关的遗传生物标志物.
- 利用神经网络解释方法,了解驱动主机预测和动物传染风险的特征.
主要方法:
- 在一个大型数据集上训练深度学习模型,其中包括848,630个来自A型流感病毒的独特核酸序列,按宿主 (禽类,人类,猪类) 分类.
- 利用mRNA和蛋白质序列作为模型训练的输入,并比较它们的预测性能.
- 应用神经网络解释技术,包括UMAP可视化和错误分类序列的分析,以确定关键的遗传特征.
主要成果:
- mRNA序列提供了比蛋白质序列更高的预测准确度,表明编码级信息对宿主预测至关重要.
- 病毒序列根据宿主在潜伏空间中的起源聚合;流行性动物病菌株出现在宿主之间的边缘.
- 流行性动物性传染病序列显示出较低的预测准确性,这支持了模糊的预测与跨物种传染性相关的假设.
- 在错误分类的禽到人类和猪到人类序列的特定基因 (NA,HA,NP) 中识别了保存的编解子,作为潜在的动物传染生物标志物.
结论:
- 深度学习模型可以有效地预测病毒宿主,并识别与动物感染过渡潜力相关的遗传特征.
- 在关键病毒基因中,模两可的序列预测和特定的编码子模式表明了流行病潜力和跨物种传染性.
- 这些发现为动物性流感A病毒传播的遗传基础提供了宝贵的见解,有助于开发早期预警系统和有针对性的干预措施.
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