使用非向液体染色学,基于质谱学的代谢学和机器学习,预测奶牛的
Laura V Randall1, Dong-Hyun Kim2, Salah M A Abdelrazig2
1University of Nottingham, School of Veterinary Medicine and Science, Sutton Bonington Campus, Sutton Bonington, Leicestershire, LE12 5RD, United Kingdom.
Journal of dairy science
|July 27, 2023
概括
代谢学和机器学习可以在明显的迹象出现之前准确地预测奶牛的. 这些新的方法提供了早期检测,改善了动物福利,并预防了初哺乳期奶牛的的风险.
科学领域:
- 兽医医学 兽医医学 兽医医学
- 动物科学动物科学
- 生物技术是生物技术.
背景情况:
- 是乳牛中普遍存在的问题,严重影响动物健康和福利.
- 当前视力的评估方法在早期检测方面存在重大局限性.
- 需要新的诊断方法来改善乳牛的管理.
研究的目的:
- 调查代谢学和机器学习的使用,以开发用于检测奶牛的新方法.
- 评估代谢学概况的预测准确性,以便在临床症状显现之前识别.
- 评估早期预测和预防腿风险的潜力.
主要方法:
- 在尿液样本上使用液态染色体质谱法 (LC-MS) 的非向代谢学.
- 机器学习模型和稳定性选择方法用于数据分析.
- 研究了两组初哺乳期奶牛母牛群,在腿检测之前和检测时采集了样本.
主要成果:
- 机器学习模型在视觉检测时实现了81-82%的的预测准确度.
- 稳定性选择方法为提供了80-81%的预测准确度.
- 模型预测的准确率在分娩前为71%和分娩后为75%,表明了早期检测潜力.
结论:
- 非定向的LC-MS分析与机器学习相结合,可以在可观察到的步态变化之前预测奶牛的.
- 这些方法为早期预测和预防的风险提供了实质性的进展.
- 需要进一步的外部验证来确认这些发现的概括性.
关键词:
乳牛牛奶牛牛牛牛牛牛牛牛牛牛牛牛牛牛牛牛牛牛牛牛牛牛牛牛牛牛牛牛牛牛牛牛牛牛牛牛牛牛牛牛牛牛牛牛牛牛牛牛牛牛牛牛牛牛这就是惰,惰,惰.液态染色学 基于质谱的代谢学 基于质谱的代谢学机器学习是机器学习.更多相关视频
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