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平衡伦理和统计:机器学习可以根据特征焦虑对小鼠进行高度准确的分类

Johannes Miedema¹, Beat Lutz^2,3, Susanne Gerber^1,4

¹Institute of Human Genetics, University Medical Center of the Johannes Gutenberg University Mainz, Mainz, Germany.

Translational psychiatry

|August 21, 2025

概括

机器学习模型准确地将小鼠分为持续性和阶段性焦虑类型. 这种方法提高了行为表型的稳定性,并减少了临床前研究中的动物数量.

科学领域:

行为神经科学
动物模型
机器学习

背景情况:

在疾病易受伤害和治疗反应方面,个体差异和性别特异性模式至关重要,但在临床前研究中经常被忽视焦虑和压力障碍.
之前用于分类小鼠焦虑内型的无监督集群方法需要大样本,这与动物福利原则相冲突.

研究的目的:

开发和验证监督机器学习 (ML) 模型,以准确地按性别分类小鼠的焦虑内因型.
在不同样本大小和实验条件下评估ML模型的稳定性和通用性.

主要方法:

收集了470只小鼠的数据,以训练和验证监督的ML模型,以分类持续和阶段性焦虑反应者.
在独立的动物群中评估模型性能和通用性.
评估样本大小和协议修改对分类器性能的影响.

主要成果:

监督的ML模型在按性别分类小鼠的持续性和阶段性内型方面取得了很高的准确性和概括性.
ML分类器的性能独立于样本大小和调节方案的变化.
开发的ML方法证明了稳定性和可复制性.

结论:

机器学习辅助技术为临床前焦虑研究中的行为表型提供了强大且可重复的方法.
这些ML模型通过使用可能较小的样本大小和可适应的协议来实现准确的分类来促进3R原则 (替换,减少,精细化).

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