一个用户友好的机器学习程序,用光图像来量化口腔特征
Angres Gabriel J1, Gillert Alexander2, Muroyama Andrew1
1Department of Cell and Developmental Biology, University of California San Diego, La Jolla, CA, 92093, USA.
bioRxiv : the preprint server for biology
|December 3, 2025
概括
研究人员开发了一个半自动口腔注释工具QuickSpotter,以加快分析植物叶子发育的速度. 这项创新有助于理解口腔形态如何影响光合作用和植物健康.
科学领域:
- 植物生物学 植物生物学
- 计算生物学 计算生物学
- 遗传学 遗传学 是一个
背景情况:
- 胃膜是植物光合作用和气体交换的关键毛孔.
- 胃体形态影响光合作用效率,但很难在大型数据集中手动分析.
研究的目的:
- 开发一个半自动化工具 (QuickSpotter) 从光图像进行有效的口腔注释.
- 为了实现对口腔发育和形态学的高通量分析.
主要方法:
- 开发了用于半自动口腔注释的QuickSpotter.
- 引入了StomEdit,用于快速校对注释.
- 使用PairCaller来识别口腔.
主要成果:
- 快速Spotter准确地在不同发育阶段对成熟的口腔进行注释.
- 该工具量化了在节骨发育期间的口腔形态演变.
- 在药理治疗下发现了口腔发育的微妙差异.
结论:
- 该QuickSpotter套件可方便对口腔发育进行大规模的定量分析.
- 在各种条件下使叶子特征的高通量表型化成为可能.
- 进步对影响口腔形态的遗传和环境因素的理解.
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