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相关概念视频

Proteomics01:33

Proteomics

7.5K
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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Spatial Profiling of Protein and RNA Expression in Tissue: An Approach to Fine-Tune Virtual Microdissection
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DeepSP:一个深度学习框架,用于空间蛋白质组学.

Bing Wang1,2, Xiangzheng Zhang1, Chen Xu1

  • 1Department of Histology and Embryology, State Key Laboratory of Reproductive Medicine and Offspring Health, Nanjing Medical University, Nanjing 211166, China.

Journal of proteome research
|June 14, 2023
PubMed
概括
此摘要是机器生成的。

DeepSP是一种新的深度学习框架,使用质谱空间蛋白质组学数据增强了蛋白质细胞下定位 (PSL) 预测. 它提高了准确性和稳定性,有助于理解蛋白质功能和生物过程.

关键词:
注意力机制注意力机制深度学习是一种深度学习.差异矩阵是一个差异矩阵.蛋白质亚细胞局部化空间蛋白质组学 空间蛋白质组学

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Deep Proteome Profiling by Isobaric Labeling, Extensive Liquid Chromatography, Mass Spectrometry, and Software-assisted Quantification
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科学领域:

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

背景情况:

  • 蛋白质亚细胞定位 (PSL) 对于理解蛋白质功能至关重要.
  • 基于质谱 (MS) 的空间蛋白质组学提供高通量PSL预测.
  • 现有的机器学习预测器在PSL注释准确性方面存在局限性.

研究的目的:

  • 开发一个新的深度学习框架,DeepSP,以改善PSL预测.
  • 使用基于MS的空间蛋白质组学数据,提高PSL预测的准确性和稳定性.

主要方法:

  • DeepSP使用了一种新的特征地图,该地图来自蛋白质占用概况的差异矩阵.
  • 包含一个卷积块注意模块来改进预测.
  • 在独立的测试集上进行评估,并用于预测未知的PSL.

主要成果:

  • 与现有方法相比,DeepSP在准确性和稳定性方面取得了显著的改进.
  • 该框架有效地捕捉了蛋白质分布在亚细胞分数中的详细变化.
  • 在预测已知和未知的PSL方面取得了卓越的性能.

结论:

  • DeepSP为空间蛋白质组学中的PSL预测提供了一个高效和强大的框架.
  • 促进对蛋白质功能和生物过程调节的更深入的了解.
  • 预计将推进空间蛋白质组学研究领域.