使用机器学习分析土耳其reovirus新兴变种的分析
Maryam KafiKang1, Chamudi Abeysiriwardana1, Vikash K Singh2
1Computer Science Department, University of Rhode Island, Kingston, 02881, RI, USA.
Briefings in bioinformatics
|May 16, 2024
概括
机器学习有效地分类火reoviruses,包括新兴的变体,如火关节炎reovirus (TARV) 和火肝炎reovirus (THRV). 这有助于检测疾病并了解家禽中的reovirus演变.
科学领域:
- 兽医病毒学 兽医病毒学
- 生物信息学是一种生物信息学.
- 机器学习在动物健康中的应用
背景情况:
- 禽类reoviruses通过肠炎,综合炎/关节炎和肝炎等疾病对火造成重大经济损失.
- 新兴的变种,火关节炎复原病毒 (TARV) 和火肝炎复原病毒 (THRV),对火产业构成越来越大的威胁.
- 准确检测和分类的reovirus类型对于疾病管理和预防经济损失至关重要.
研究的目的:
- 区分火reovirus类型并使用集群方法 (K-means, Hierarchical) 识别新兴变体.
- 使用机器学习 (SVM,Naive Bayes,随机森林,决策树) 和深度学习 (CNN) 算法对火reovirus变种进行分类.
- 为了评估这些计算方法在分析真实火reovirus序列数据中的性能.
主要方法:
- 应用K-means和层次聚类来初始分化reovirus类型.
- 实现支持向量机器,天真贝斯,随机森林和决策树算法用于变量分类.
- 利用卷积神经网络 (CNN) 进行基于深度学习的reovirus变体分类.
- 对真实火reovirus序列数据的分析,以验证拟议的计算方法的有效性.
主要成果:
- 机器学习分类器实现了高性能,平均准确率为92%,F1-Macro为93%,F1-Weighted为92%.
- 卷积神经网络 (CNN) 模型显示平均准确率为85%,F1-宏为71%,F1-加权为84%.
- 与CNN模型相比,机器学习方法在分类火reovirus类型和变体方面表现优异.
结论:
- 机器学习算法在分类火reovirus类型和识别新兴变体方面非常有效.
- 这些计算工具为reovirus进化和突变模式提供了宝贵的见解.
- 这些发现支持使用先进的分析方法来早期检测致病性TARV和THRV,帮助控制火行业的疾病.
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