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Updated: Jan 14, 2026

Large Scale Non-targeted Metabolomic Profiling of Serum by Ultra Performance Liquid Chromatography-Mass Spectrometry UPLC-MS
07:34

Large Scale Non-targeted Metabolomic Profiling of Serum by Ultra Performance Liquid Chromatography-Mass Spectrometry UPLC-MS

Published on: March 14, 2013

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一个机器学习框架,用于将脂质分类到非目标代谢学中,使用质量电荷比和保留时间

Christelle Colin-Leitzinger1, Yonatan Ayalew Mekonnen2, Isis Narvaez-Bandera3

  • 1Department of Cancer Epidemiology, Moffitt Cancer Center, Tampa, FL, USA.

Metabolomics : Official journal of the Metabolomic Society
|October 18, 2025
PubMed
概括

机器学习模型现在可以将代谢物分类为"脂质"或"非脂质",仅使用从液态染色体质谱法 (LC-MS) 数据中获得的质量与电荷比 (m/z) 和保留时间 (RT). 这种方法绕过了复杂的双重质谱 (MS2) 数据的需求,简化了代谢物识别.

关键词:
在 LCMS 中使用.机器学习 机器学习质量与电荷的比率.保存时间 保存时间未知代谢物的代谢物.

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