为了实现机器学习在模拟OMIC和临床数据中的最佳通用性表现
Fei Deng1, Yongfeng Zhang2, Lanjing Zhang3
1Department of Chemical Biology, Ernest Mario School of Pharmacy, Rutgers University, Piscataway, New Jersey.
概括
机器学习模型的性能在内部数据和交叉数据集测试之间有所不同. 这项研究发现,差异地表达的基因是关键因素,癌症类型影响最佳建模策略,以提高概括性.
科学领域:
- 生物信息学是一种生物信息学.
- 计算生物学 计算生物学
- 机器学习 机器学习
背景情况:
- 机器学习 (ML) 模型经常显示数据集内部和数据集交叉测试之间的性能差异.
- 提高模型通用性,同时保持高的数据集内部性能,在ML开发中是一个重大挑战.
研究的目的:
- 在数据集内部和跨数据集测试场景中调查和提高ML模型的性能和通用性.
- 确定影响ML模型性能和在不同癌症类型中概括性的关键因素.
主要方法:
- 在多个数据集中评估了4200ML的肺腺癌模型和1680的质母细胞瘤.
- 采用双重分析框架,结合统计分析和基于夏普利增量解释 (SHAP) 的元分析.
- 利用强大的参数和非参数统计测试来分析模型性能分布.
主要成果:
- 模型性能分布明显偏离正常,需要强大的统计方法.
- 简单的线性模型在肺腺癌中表现出色,而非线性模型在质母细胞瘤中表现出色,表明依赖于疾病的最佳策略.
- 不同表达的基因始终被确定为两种癌症类型中具有高度影响力的因素.
结论:
- 该研究强调了ML模型性能的非正常分布,主张进行可靠的统计测试.
- 确定了影响跨数据集性能和癌症基因组学概括性的关键因素和设计原则.
- 开发和验证了一个多标准框架,用于在各种数据集中选择准确和强大的ML模型.
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