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相关概念视频

Peptide Identification Using Tandem Mass Spectrometry01:33

Peptide Identification Using Tandem Mass Spectrometry

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Tandem mass spectrometry, also known as MS/MS or MS2, is an analytical technique that employs two mass analyzers. Essentially it is a series of mass spectrometers that helps isolate a particular biomolecule and then helps study its chemical properties.
This technique helps gather information regarding the protein from which the peptide was obtained and to study the peptides’ amino acid sequence. Identifying peptides from a complex mixture is an important component of the growing field of...
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MALDI-TOF Mass Spectrometry01:19

MALDI-TOF Mass Spectrometry

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Mass spectrometry is a powerful characterization technique that can identify and separate a wide variety of compounds ranging from chemical to biological entities, based on their mass-to-charge ratio (m/z). The instruments that allow this detection, known as mass spectrometers, have three components: an ion source, a mass analyzer, and a detector. These spectrometers differ based on the nature of their ion source and analyzers.
Matrix-assisted laser desorption ionization (MALDI) is a commonly...
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Quantitative Proteomics Workflow using Multiple Reaction Monitoring Based Detection of Proteins from Human Brain Tissue
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Quantitative Proteomics Workflow using Multiple Reaction Monitoring Based Detection of Proteins from Human Brain Tissue

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基于深度学习的MS2特征检测用于数据独立的步枪蛋白质学.

Jonathan He1, Olivia Liu1, Xuan Guo1

  • 1Department of Computer Science and Engineering, Univeristy of North Texas, Denton, USA.

Proceedings. IEEE International Conference on Bioinformatics and Biomedicine
|August 28, 2023
PubMed
概括

一个新的深度学习模型提高了液体染色体质谱学 (LC-MS) 分析中的鉴定准确性. 这种计算型蛋白质组学工具通过更好地检测MS2数据中的低丰度片段来增强生物标记物的发现.

科学领域:

  • 计算型蛋白质组学是一种计算型蛋白质组学.
  • 发现生物标志物的发现.
  • 质谱学分析的分析.

背景情况:

  • 在LC-MS中准确的标识对于理解蛋白质功能和发现生物标志物至关重要.
  • 目前的MS2数据分析工具与低丰度,杂的片离子作斗争,影响蛋白质组概况.
  • 在LC-MS中特征检测是具有挑战性的,因为重叠的和弱信号,经常依赖于启发式.

研究的目的:

  • 开发一种基于深度学习的模型,用于准确的MS2特征检测.
  • 解决现有工具在识别低丰度片段方面的局限性.
  • 改进使用LC-MS数据进行复杂蛋白质组的定量分析.

主要方法:

  • 开发了一个深度学习模型,结合了创新的滑动窗程序.
  • 将模型应用于高分辨率的MS/MS定量数据以检测特征.
  • 利用先进的算法来处理复杂的蛋白质组数据集.

主要成果:

  • 与现有的工具相比,深度学习模型在的识别和量化方面取得了更高的准确性.
  • 证明了真正正面特征量化率很高.
  • 成功处理了高分辨率的MS/MS定量数据.
关键词:
在MS2功能检测系统中,特征检测是液态色谱学质谱学质谱学机器学习是机器学习.蛋白质组学 蛋白质组学

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Shotgun Lipidomics of Rodent Tissues
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Fast Enzymatic Processing of Proteins for MS Detection with a Flow-through Microreactor

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Last Updated: Jul 18, 2025

Quantitative Proteomics Workflow using Multiple Reaction Monitoring Based Detection of Proteins from Human Brain Tissue
11:49

Quantitative Proteomics Workflow using Multiple Reaction Monitoring Based Detection of Proteins from Human Brain Tissue

Published on: August 28, 2021

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Shotgun Lipidomics of Rodent Tissues
11:46

Shotgun Lipidomics of Rodent Tissues

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Fast Enzymatic Processing of Proteins for MS Detection with a Flow-through Microreactor
09:49

Fast Enzymatic Processing of Proteins for MS Detection with a Flow-through Microreactor

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结论:

  • 深度学习技术为计算蛋白质组学提供了显著的优势.
  • 开发的模型提高了MS2特征检测的准确性和可靠性.
  • 这种方法有望促进生物标志物发现和蛋白质组概况的发展.