集成基于学习的方法用于对白类进行自动分类.
Thi Kim Chi Duong1,2, Van Lang Tran3, The Bao Nguyen2
1Department of Information Technology, Lac Hong University, Dong Nai Province, Vietnam.
Frontiers in genetics
|October 27, 2023
概括
这项研究引入了一种机器学习模型,用于使用内部转录间隔器 (ITS) 基因序列对白类型进行分类. 该模型准确地识别了白类,有助于保护这些有价值的真菌.
科学领域:
- 菌类学 菌类学是指菌类学.
- 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:
- 类是有价值的食用真菌,但由于它们的短暂,季节性生长,传统的识别方法不可靠.
- 准确的物种识别对于理解和保护这些重要的资源至关重要.
研究的目的:
- 开发一种新的,自动化的方法来分类白类型.
- 利用机器学习和基因测序进行可靠的识别.
主要方法:
- 利用渐变增强机器学习和序列编码在内部转录间隔器 (ITS) 基因数据上.
- 使用NCBI和BOLD数据库中的ITS序列训练模型.
- 应用组合学习技术用于物种分类.
主要成果:
- 在测试数据集中获得了高精度 (0.91) 和平均AUCROC 0.99.
- 用越南现场收集的ITS序列验证了模型,显示与NCBI BLAST一致的结果.
- 机器学习模型证明了有效的物种识别.
结论:
- 拟议的机器学习模型提供了一个有前途的自动化解决方案,用于分类白类型.
- 这种方法可以加强对当地生长模式的研究,并为罕见的虫资源的保护策略提供信息.
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