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相关概念视频

Thin-Layer Chromatography (TLC): Overview01:11

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Thin-layer chromatography (TLC) is a chromatography technique that separates compounds based on their polarity. TLC typically uses polar silica gel, a form of silicon dioxide, as the stationary phase. The silica gel contains hydroxyl (OH) groups on its surface, which form hydrogen bonds with polar compounds, influencing their adhesion to the stationary phase.
To begin the analysis, a mixture of compounds is spotted on the starting line on the TLC plate using a thin capillary. The bottom of the...
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High-Resolution Mass Spectrometry (HRMS)01:15

High-Resolution Mass Spectrometry (HRMS)

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The resolution of a mass spectrometer depends on the efficiency of separating ions with different ion masses. The mass of an atom is approximated to the sum of the masses of protons and neutrons inside, considering the masses of protons and neutrons as equal. However, the masses of the proton (1.6726 × 10−24 g) and neutron (1.6749 × 10−24 g) are not truly equal. There is a minor error in the expression of atomic masses relative to the simplest atom of hydrogen. For...
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相关实验视频

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Preparation of Human Tissues Embedded in Optimal Cutting Temperature Compound for Mass Spectrometry Analysis
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通过基于机器学习的保留时间预测,提高LC-HRMS数据中的脂质识别.

Hamada A A Noreldeen1

  • 1National Institute of Oceanography and Fisheries, NIOF, Cairo, Egypt.

Journal of chromatography. A
|January 11, 2025
PubMed
概括

本研究引入了一种机器学习模型,用于在非向性脂管学中准确预测脂质保留时间. 该模型显著改善了脂质识别,并减少了LC-MS/MS数据分析中的错误.

科学领域:

  • 生物化学 生物化学
  • 计算生物学 计算生物学
  • 分析化学 分析化学

背景情况:

  • 使用LC-MS/MS的非向性脂组学在全面的峰值识别方面面临挑战.
  • 准确的脂质注释对于了解疾病机制,生物标志物发现和药物查至关重要.
  • 基于机器学习的保留时间预测可以提高脂质识别的信心.

研究的目的:

  • 开发和验证一种机器学习模型,用于预测LC-MS/MS非向性脂管学中的脂质保留时间.
  • 为了提高脂质峰值注释的准确性和可靠性.
  • 评估不同分子描述符对模型性能的影响.

主要方法:

  • 开发一种机器学习模型,利用分子描述符来预测保留时间.
  • 在LC-MS/MS数据上对模型进行培训和测试,这些数据来自非向性脂管学实验.
  • 用随机森林 (RF) 算法比较分子描述符与分子指纹.
  • 对模型性能进行外部验证.

主要成果:

  • 开发的模型实现了高相关系数 (0.998训练,0.990测试) 和低平均绝对误差 (0.107分钟训练,0.240分钟测试).
  • 外部验证显示了强的性能,相关性为0.991和0.978.8.
关键词:
LC-高分辨率的MSMS可以使用.脂质识别 脂质识别机器学习是机器学习.随机的森林随机的森林保留时间预测模型的模型.

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  • 在使用随机森林算法时,分子描述器的性能优于分子指纹.
  • 该模型在不同的色谱系统中显示出强大的性能.
  • 结论:

    • 机器学习模型显著提高了在非向性脂管学中脂质注释的准确性.
    • 该模型减少了脂质识别中的错误,改善了数据分析.
    • 这种方法为各种应用提供了可靠的工具,包括生物标志物发现和药物查.