在不平衡的单细胞数据集中改进机器学习的自适应性重新采样
Zeinab Navidi1, Akshaya Thoutam2, Madeline Hughes3
1Department of Computer Science, University of Toronto, Toronto, ON, Canada.
bioRxiv : the preprint server for biology
|November 24, 2025
概括
我们开发了自适应重新采样 (AR),这是一种改进单细胞转录组学机器学习的新方法. 通过在训练期间通过适应性重新采样数据来提高模型性能,从而从复杂的生物数据中获得更好的洞察力.
科学领域:
- 计算生物学 计算生物学
- 基因组学就是基因组学.
- 机器学习 机器学习
背景情况:
- 单细胞转录组学的机器学习模型提供了生物学见解.
- 目前的工具与代表性不足或分布不良的蜂数据作斗争.
- 有效的表示学习对于分析复杂的单细胞数据至关重要.
研究的目的:
- 为单细胞转录组学引入一种可通用的适应性重新采样 (AR) 方法.
- 通过解决当前模型的局限性来增强单细胞表示学习.
- 提高机器学习模型在各种单细胞数据上的性能.
主要方法:
- 开发了一种基于已学习的潜在数据结构的在线,自适应性重新采样策略.
- 综合适应重新采样 (AR) 与模型培训同时进行.
- 对基因表达重建,细胞类型分类和扰乱反应预测任务进行评估的AR.
主要成果:
- 适应性重新采样 (AR) 显著改善了各种任务和数据集的下游性能.
- 增强现实训练方法提高了学习的细胞嵌入的质量.
- 在单细胞转录组分析中,与标准培训方法相比,表现优越.
结论:
- 适应性重新采样 (AR) 是一种有价值的技术,用于改进单细胞转录学中的机器学习模型.
- 增强现实有效地解决了代表性不足和分布不良的蜂数据带来的挑战.
- 拟议的方法增强了对生物见解的表示学习和预测准确性.
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