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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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Updated: Jan 8, 2026

Deep Proteome Profiling by Isobaric Labeling, Extensive Liquid Chromatography, Mass Spectrometry, and Software-assisted Quantification
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DeepMLP:一种蛋白质学驱动的深度学习框架,用于识别全癌症中错误定位的蛋白质.

Bing Wang1,2, Qilei Lin3, Xin He4

  • 1Department of Bioinformatics, Fujian Key Laboratory of Medical Bioinformatics, Institute of Precision Medicine, School of Medical Technology and Engineering, Fujian Medical University, Fuzhou 350122, China.

Journal of chemical information and modeling
|December 23, 2025
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概括
此摘要是机器生成的。

DeepMLP是一个新的深度学习框架,使用蛋白质组学数据识别癌症中错误局部化的蛋白质 (MLPs). 这种方法通过准确预测蛋白质细胞下定位 (PSL) 和发现潜在的癌症驱动因素来增强癌症研究.

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科学领域:

  • 蛋白质组学和生物信息学
  • 癌症生物学 癌症生物学
  • 计算生物学 计算生物学

背景情况:

  • 精确的蛋白质细胞下定位 (PSL) 对蛋白质功能至关重要;错误定位与癌症等疾病有关.
  • 现有的空间蛋白质组学方法昂贵而复杂,限制了与癌症相关的错位蛋白 (MLP) 的大规模分析.

研究的目的:

  • 开发一个深度学习框架,DeepMLP,用于识别癌症中的MLP,使用基于质谱的蛋白质组学数据.
  • 在正常和瘤条件下提高PSL预测的准确性和稳定性.

主要方法:

  • 开发了DeepMLP,这是一个蛋白质学驱动的深度学习框架,将路径意识蛋白质表示与动态蛋白质-蛋白质相互作用 (PPI) 网络集成在一起.
  • 使用交叉注意力机制来构建表示和图形注意力网络来集成PPI网络.
  • 与PSL预测的最先进方法对比的基准DeepMLP.

主要成果:

  • 与现有方法相比,DeepMLP在PSL预测方面表现出卓越的准确性和稳定性.
  • 系统地识别了各种癌症类型的潜在MLP,包括错误局部化的蛋白激酶.
  • 功能性丰富分析显示,已识别的MLP在与癌症相关的代谢和信号通路中具有显著的参与.

结论:

  • DeepMLP提供了一种强大的,具有成本效益的方法来识别癌症中的MLP.
  • 已识别的MLP和激酶有可能成为新的生物标志物或瘤发生中的治疗点.
  • 这一框架促进了对蛋白质错位在癌症发展中的作用的理解.