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相关概念视频

Proteomics01:33

Proteomics

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A proteome is the entire set of proteins that a cell type produces. We can study proteomes using the knowledge of genomes because genes code for mRNAs, and the mRNAs encode proteins. Although mRNA analysis is a step in the right direction, not all mRNAs are translated into proteins.
Proteomics is the study of proteomes' function. It involves the large-scale systematic study of the proteome to denote the protein complement expressed by a genome. Scientist Mark Wilkins coined the term...
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Peptide Identification Using Tandem Mass Spectrometry01:33

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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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A Spin-Tip Enrichment Strategy for Simultaneous Analysis of N-Glycopeptides and Phosphopeptides from Human Pancreatic Tissues
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深度学习预测通过改进的光的识别来促进基于光蛋白学的发现.

Xinpei Yi1, Bo Wen1, Shuyi Ji2

  • 1Lester and Sue Smith Breast Center, Baylor College of Medicine, Houston, Texas, USA; Department of Molecular and Human Genetics, Baylor College of Medicine, Houston, Texas, USA.

Molecular & cellular proteomics : MCP
|December 28, 2023
PubMed
概括

DeepRescore2通过使用深度学习改进类的识别和定位来增强猎枪类蛋白质组学. 这种计算工作流显著增加了已识别的类,有助于复杂数据集中的生物发现.

关键词:
欧洲农业基金会 (EGFR) 是一个基金.深度学习是一种深度学习.碎片离子强度 碎片离子强度肝癌 肝癌 是一种肝癌.类的识别方法光蛋白质组学 光蛋白质组学 光蛋白质组学酸的局部化恢复得分 恢复得分保留时间 保留时间

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科学领域:

  • 蛋白质组学是指蛋白质组学.
  • 计算生物学 计算生物学
  • 生物信息学是一种生物信息学.

背景情况:

  • 射门光蛋白组学对于光的高吞吐量分析至关重要.
  • 低脂的识别率限制了猎枪脂蛋白组学的潜力.
  • 精确的酸盐定位对于理解信号通路至关重要.

研究的目的:

  • 为了介绍DeepRescore2,一个新的计算工作流.
  • 为了提高光的识别和酸盐定位率在猎枪光蛋白组学数据.
  • 改进从蛋白质组分析中获得的生物见解.

主要方法:

  • 利用深度学习来预测保留时间和碎片离子强度.
  • 开发用于类分析的计算工作流程 (DeepRescore2).
  • 与使用合成和生物数据集的最先进的计算工作流程进行基准测试.

主要成果:

  • 在合成数据中,DeepRescore2将正确识别的谱匹配率提高了17%.
  • 与基准数据相比,在生物数据集中发现了19%至46%的类.
  • 独特地确定了EGFR过活化作为预后不良肝癌的新标,经过实验验证.

结论:

  • 在DeepRescore2中的深度学习集成显著改善了光的识别和定位.
  • DeepRescore2促进了更深层次的生物发现,包括新的治疗点.
  • 工作流程增强了用于生物研究和临床应用的猎枪光蛋白组学的实用性.