卷积神经网络辅助峰值分类在向LC-HRMS/MS中用于马匹兴奋剂控制查分析
Agnès Barnabé1, Vivian Delcourt1, Benoit Loup1
1GIE LCH, Laboratoire des Courses Hippiques, 15 rue de Paradis, 91370 Verrières-le-Buisson, France.
Analytical chemistry
|February 4, 2025
概括
人工智能 (AI) 通过分析染色图来自动化马匹兴奋剂控制,减少重复性任务. 这种深度学习方法确保了准确的选,这对于保持公平竞争和动物福利至关重要.
科学领域:
- 分析化学 分析化学
- 计算生物学 计算生物学
- 兽医科学 兽医科学 兽医科学
背景情况:
- 兴奋剂控制查依赖于耗时的染色体图的手动分析.
- 对众多化合物和代谢物的高通量选对于现代反兴奋剂工作至关重要.
- 机器学习,特别是卷积神经网络 (CNN),为自动化分析任务提供了潜力.
研究的目的:
- 评估深度学习的可行性和准确性,以自动化马匹兴奋剂控制.
- 开发一种人工智能驱动的战略,用于生物治疗查中的染色体分类.
- 为应对在自动化兴奋剂分析中实现零假阴性率 (FNR) 的挑战.
主要方法:
- 实施使用CNN进行染色体图像分类的深度学习策略.
- 整合一个线性差别分析 (LDA) 分类器.
- 培训和验证使用超高压液体染色学数据与高分辨率联质谱学 (UHPLC-HRMS/MS) 的数据.
主要成果:
- 证明了用于马匹兴奋剂控制的深度学习方法的可行性和准确性.
- 开发了一种CNN评分模型,与LDA分类器相结合,以进行可靠的分析.
- 在分类染色体图中实现了高精度,这对于可靠的兴奋剂控制至关重要.
结论:
- 深度学习策略对于在马匹兴奋剂控制中自动化染色体分类是有效的.
- 人工智能,特别是CNN和LDA,可以显著提高兴奋剂控制实验室的效率和准确性.
- 拟议的AI工具对未来在反兴奋剂工作中的应用有希望,确保零FNR.
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