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解决方案适应式结合增强机器学习建模通过跨批次和多平台轨道轨道轨道基于枪炮质谱数据集成数据集成
Hiu-Lok Ngan1, Jialing Zhang1, Kenneth Kin-Leung Kwan2,3
1State Key Laboratory of Environmental and Biological Analysis, Department of Chemistry, Hong Kong Baptist University, Hong Kong 999077, P. R. China.
Analytical chemistry
|November 25, 2025
概括
这项研究引入了一种新的质量分辨率适应性对接策略,以整合不同批次和平台的质谱数据. 该方法提高了用于疾病检测的机器学习模型通用性,提高了各种样本类型的准确性.
科学领域:
- 分析化学 分析化学
- 计算生物学 计算生物学
- 生物医学科学 生物医学科学
背景情况:
- 关于质谱 (MS) 数据的机器学习 (ML) 有助于疾病建模,但在批量特定模型和有限的概括性方面面临挑战.
- 传统的数据集成方法与质量到电荷 (m/z) 功能扎,阻碍了跨不同MS批量和平台的可靠数据组合.
- 对强大的数据整合策略的需求对于在使用MS的疾病诊断中推进ML应用至关重要.
研究的目的:
- 开发和验证一个适应大规模分辨率的MS数据的组合和整合战略.
- 提高ML模型在不同MS数据集和平台上的可转移性和通用性.
- 通过可靠整合多批次和多平台MS数据来提高疾病检测准确度.
主要方法:
- 开发了一种新的质量分辨率适应性分类策略,以处理不同质谱分辨率的m / z特征.
- 该策略在混合标准解决方案上进行了测试,在低质量区域中恢复了88-99%的地面真实特征.
- 该方法应用于肝细胞癌的小鼠模型,整合了环境MS成像 (MSI) 和猎枪蛋白质组学数据.
主要成果:
- 拟议的方法证明了在低,中,高质量区域的稳定分组和集成,性能优于传统技术.
- 使用集成数据构建的预测模型与使用传统方法的模型相比,显示出更高的性能.
- 在肝细胞癌小鼠模型中,确定了10种通用代谢物,通过MSI (F1得分=0.87) 和直接输液 (回忆=0.89) 准确检测疾病.
结论:
- 大规模分辨率适应型组合和集成策略有效地克服了MS数据集成传统方法的局限性.
- 这种新的方法显著提高了ML模型在各种样本引入方法中检测疾病的普遍性.
- 该战略有望通过使多样化的数据集更准确,更可靠地集成来推进基于MS的诊断.
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