探索混沌游戏表示和整合深度学习方法,用于基于全基因组测序的葡萄藤遗传测试
Andrew Vu1, Brendan Park1, Yifeng Li1,2,3
1Department of Computer Science, Brock University, St. Catharines, ON L2S 3A1, Canada.
Bioinformatics advances
|September 22, 2025
概括
准确的葡萄藤识别对于葡萄种植至关重要. 全基因组测序与混沌游戏表示和深度学习相结合,为物种和品种分类提供了具有成本效益和高度准确的方法.
科学领域:
- 基因组学就是基因组学.
- 计算生物学 计算生物学
- 葡萄种植 葡萄种植 葡萄种植
背景情况:
- 葡萄藤 (Vitis spp.) 的准确识别 品种,品种和克隆对于葡萄种植至关重要,但由于众多品种和历史命名不一致而具有挑战性.
- 当前基于DNA的方法虽然优于形态学,但在生物标志物数量和服务可访问性方面存在局限性.
研究的目的:
- 通过探索全基因组测序 (WGS) 与先进的计算技术相结合,开发一种更准确,更实惠的葡萄藤鉴定方法.
- 解决WGS数据的高维度问题,使用混乱游戏表示 (CGR) 和深度学习来改进物种和品种分类.
主要方法:
- 整个基因组测序 (WGS) 数据被用于葡萄酒分析.
- 混沌游戏表示 (CGR) 用于将基因组序列转换为基于图像的数据,以促进模式识别.
- 开发了深度学习模型,并将其应用于用于葡萄品种和品种识别的CGR图像.
主要成果:
- 当CGR方法与深度学习相结合时,葡萄酒分类的准确性很高.
- 对五种物种,物种识别达到了高达99%的平衡精度.
- 品种预测在41个不同的品种中达到80%的平衡精度.
结论:
- 混沌游戏表示和深度学习显示出作为基于WGS的葡萄藤识别的补充工具的巨大潜力.
- 这种综合方法为葡萄种类和葡萄品种在葡萄种植中的分类提供了一个有希望,准确和潜在的成本效益的解决方案.
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