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利用机器学习和血凝素序列来识别可能的流感H3Nx病毒宿主

Famke Alberts¹, Olaf Berke², Grazieli Maboni³

¹Department of Population Medicine, Ontario Veterinary College, University of Guelph, 50 Stone Road East, Guelph, Ontario, Canada.

Preventive veterinary medicine

|October 1, 2024

概括

机器学习准确地识别H3流感病毒宿主,使用血凝素基因序列. 这种工具有助于快速分类和理解病毒宿主跳跃的公共卫生.

科学领域:

病毒学病毒学
生物信息学是一种生物信息学.
机器学习机器学习

背景情况:

甲型流感病毒,特别是H3亚型,由于其广泛的宿主范围,造成了重大的公共卫生和农业风险.
准确的宿主识别对于监测和控制流感传播和潜在的流行病至关重要.

研究的目的:

开发和验证机器学习分类器,以识别H3流感病毒最可能的宿主物种.
评估这些分类器对快速序列分类和识别感兴趣的序列的有用性.

主要方法:

训练并验证了四个机器学习分类器,使用H3病毒的血凝素基因段进行分类.
在独立验证和附加数据集上评估分类器性能,包括猪,狗和环境序列.
利用预测的概率得分来分析主机融合,并识别具有多个主机潜力的序列.

主要成果:

一个梯度增强机器分类器在独立验证数据集上的宿主分类中实现了98.0%的准确性.
预测的概率得分有效突出了感兴趣的序列,包括那些具有跨物种传播潜力的序列.
分类器在分析猪和狗H3N2病毒的宿主融合以及识别环境序列方面证明了适用性.

结论:

机器学习为快速识别流感病毒的宿主提供了一个强大的,特定物种的工具,即使是来源不明的病毒.
开发的分类器可以帮助监测,风险评估和了解病毒演变和宿主适应.
这种方法有可能提高对流感爆发和流行病的准备能力.

关键词:

基因组序列的测序主机范围的主机范围.流感是一个病毒病毒.跨物种的传播方式机器学习是机器学习.

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