使用基因组数据和机器学习预测宿主物种对流感病毒和冠状病毒的敏感性:一个范围审查
Famke Alberts1, Olaf Berke1,2,3, Leilani Rocha1
1Department of Population Medicine, Ontario Veterinary College, University of Guelph, Guelph, ON, Canada.
Frontiers in veterinary science
|October 10, 2024
概括
机器学习有效地使用多种基因组数据预测病毒宿主范围. 本综述强调了各种方法,并确定了标准化报告中的研究差距,以改善病毒爆发控制.
科学领域:
- 生物信息学是一种生物信息学.
- 计算生物学 计算生物学
- 病毒学 病毒学
背景情况:
- 预测对病毒 (宿主范围) 敏感的物种对于控制人类和动物病毒爆发至关重要.
- 在基技术的进步扩大了机器学习和生物信息学的使用,用于预测病毒宿主.
研究的目的:
- 进行范围审查,确定应用于流感和冠状病毒基因组数据的机器学习方法,以识别易感宿主物种.
主要方法:
- 进行了范围审查,搜索了五个数据库的英语语言,用于病毒宿主识别的机器学习的in-silico研究.
- 选了2000年1月至2022年5月间发表的1217个引文,确定了53个相关出版物.
主要成果:
- 审查确定了32个机器学习算法,29个特征选择方法和43个基因组数据输入格式.
- 流感病毒是最受研究的 (58.5%),越来越关注冠状病毒 (41.5%). 人类,鸟类和猪是研究最多的宿主群.
- 分析了53个不同的主机,通常使用多主机数据.
结论:
- 使用机器学习进行病毒宿主识别的方法是多样化和广泛的.
- 关键的研究缺陷包括缺乏标准化的方法报告和使用广泛的主机类别进行分类.
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