使用随机森林类型对A组轮状病毒VP7和VP4基因型进行分类
Hoc Tran1, Robert Friendship1, Zvonimir Poljak1
1Department of Population Medicine, Ontario Veterinary College, University of Guelph, Guelph, ON, Canada.
Frontiers in genetics
|June 16, 2023
概括
机器学习,特别是随机森林模型,准确地分类A组轮状病毒基因型VP7和VP4. 这种方法提供了对不断增加的轮状病毒序列数据的有效和快速分析.
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:
- 全球范围内,A组轮状病毒在儿童和动物中引起严重腹.
- 随着轮状病毒序列数据的可用性不断增加,需要有效的基因定型方法.
- 机器学习方法尚未广泛探索轮状病毒基因定型.
研究的目的:
- 探索机器学习的有效性,特别是随机森林模型,用于分类A组轮状病毒基因型.
- 开发一种高效准确的方法,利用序列数据对轮状病毒进行基因定型.
主要方法:
- 训练随机森林模型从双向和多重序列对齐的位置特征.
- 员工重复10倍交叉验证和1次遗漏交叉验证用于模型评估.
- 在未见的测试数据集上验证模型,以评估现实世界的性能.
主要成果:
- 随机森林模型在分类VP7和VP4基因型时表现出高精度 (0.972-0.996) 和kappa值 (0.969-0.996).
- 多个序列对齐模型的准确性略高,但计算速度比对齐模型慢.
- 重复的10倍交叉验证速度快于可比准确度的留出一个的交叉验证.
结论:
- 随机森林模型对于分类A组轮状病毒VP7和VP4基因型非常有效.
- 这些模型为分析越来越多的轮状病毒序列数据提供了快速而准确的解决方案.
- 拟议的机器学习方法增强了轮状病毒基因定型能力.
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