关于机器学习在临床蛋白质组学中用于生物标志物发现的作用的2025年观点
Charlotte Adams1, Wout Bittremieux2
1Department of Biomedical Sciences, University of Antwerp, Antwerp, Belgium.
Expert review of proteomics
|August 7, 2025
概括
机器学习可以加速临床蛋白质组学中的生物标志物发现,但面临着挑战. 负责任的应用需要严格的设计和验证,而不仅仅是复杂的算法,以实现现实世界的影响.
科学领域:
- 临床蛋白质组学 临床蛋白质组学
- 生物标志物发现发现
- 机器学习应用 机器学习应用
背景情况:
- 机器学习 (ML) 显示了在临床蛋白质学中推动生物标志物发现的前景.
- 目前,现实世界的影响受到方法上的限制和夸张的期望所阻碍.
- 算法进步本身无法克服数据分析中的关键问题.
研究的目的:
- 批判性地评估ML在临床蛋白质学中的应用,以发现生物标志物.
- 突出阻碍ML模型成功翻译的常见陷.
- 倡导务实和负责任的方法来实现ML在这个领域.
主要方法:
- 在临床蛋白质组学中对ML方法的基于前景的批判性检查.
- 分析常见挑战,包括小样本大小,批量效应和过度装配.
- 评估模型的概括性,可解释性和复杂模型的性能,如深度学习.
主要成果:
- 算法新性不足以解决基本的方法问题.
- 复杂的模型 (例如,深度学习) 往往会加剧问题,并提供有限的解释性.
- 在典型的临床蛋白质组学数据集中,在复杂模型中观察到微不足道的性能增长.
结论:
- 在临床蛋白质组学中负责任地使用ML需要严格的研究设计和验证.
- 建模实践中的透明度和可重复性对于翻译成功至关重要.
- 优先考虑简单性,可解释性和领域知识而不是复杂性是临床影响的关键.
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