机器学习模型可以根据宿主和生物地理数据准确地预测蛋白质头条虫 (Onchoproteocephalidea) 的分类
Philippe Vieira Alves1,2, Reinaldo José da Silva1, Tomáš Scholz3
1Institute of Biosciences, Department of Biodiversity and Biostatistics, Section of Parasitology, São Paulo State University (UNESP), Rua Professor Doutor Antonio Celso Wagner Zanin 250, Botucatu, 18618689, Brazil.
概括
在蛋白质头状条虫中的宏观进化过程被利用植物遗传学分析来探索. 主体和生物地理数据准确地预测了寄生虫群,表明了共同进化的关系.
科学领域:
- 寄生虫学的寄生虫学
- 进化生物学 进化生物学
- 分子遗传学分子遗传学
背景情况:
- 蛋白头状虫 (带状虫) 在脊椎动物宿主中广泛存在.
- 它们多样性的关键宏观进化驱动因素仍然不清楚.
- 以前的遗传学研究面临着将寄生虫进化与宿主和生物地理数据相协调的挑战.
研究的目的:
- 为了研究蛋白头状条虫的遗传关系.
- 探索宿主协会和生物地理对蛋白头动物进化的影响.
- 通过机器学习测试寄生虫群组组合宿主和生物地理数据的预测能力.
主要方法:
- 从537个终端使用核RNA28S和线粒体MT-CO1序列进行了家族遗传学分析.
- 适用于同时基因对齐分析的节最佳性标准.
- 机器学习 (随机森林) 将宿主和生物地理数据与多维空间中的寄生虫群相关联.
主要成果:
- 没有恢复Proteocephalidae的单.
- 宿主和生物地理数据准确地预测了蛋白质头类群 (88.85%的平均准确率).
- 模型的准确性与类干扰负相关,支持数据的预测能力.
结论:
- 蛋白头类的进化受到宿主和生物地理因素的显著影响.
- 对宿主和生物地理数据的多维分析为家族遗传重建提供了外部支持.
- 机器学习为了解寄生虫中复杂的共同进化模式提供了一种新的方法.
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