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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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Ribosome Profiling02:24

Ribosome Profiling

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Ribosome profiling or ribo-sequencing is a deep sequencing technique that produces a snapshot of active translation in a cell. It selectively sequences the mRNAs protected by ribosomes to get an insight into a cell’s translation landscape at any given point in time.
Applications of ribosome profiling
Ribosome profiling has many applications, including in vivo monitoring of translation inside a particular organ or tissue type and quantifying new protein synthesis levels.
The technique...
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相关实验视频

Updated: Jul 25, 2025

Mass Spectrometry-Based Proteomics Analyses Using the OpenProt Database to Unveil Novel Proteins Translated from Non-Canonical Open Reading Frames
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Mass Spectrometry-Based Proteomics Analyses Using the OpenProt Database to Unveil Novel Proteins Translated from Non-Canonical Open Reading Frames

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在蛋白质组生物信息学中利用基于变压器的语言模型.

Nguyen Quoc Khanh Le1,2,3,4

  • 1Professional Master Program in Artificial Intelligence in Medicine, College of Medicine, Taipei Medical University, Taipei, Taiwan.

Proteomics
|June 29, 2023
PubMed
概括
此摘要是机器生成的。

基于变压器的自然语言处理 (NLP) 模型正在推进蛋白质组生物信息学. 这些先进的模型增强了复杂蛋白质数据的分析,提高了研究的准确性和效率.

关键词:
生物信息学是一种生物信息学.深度学习是一种深度学习.发现药物的发现.可解释的人工智能自然语言处理自然语言处理.蛋白质表达 蛋白质表达蛋白质功能的预测和预测.变压器的注意力转换器的注意力

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JUMPn: A Streamlined Application for Protein Co-Expression Clustering and Network Analysis in Proteomics
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相关实验视频

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科学领域:

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

背景情况:

  • 生物数据的指数增长需要先进的分析工具.
  • 蛋白质组学,即对蛋白质的研究,是生物信息学中的一个关键领域.
  • 自然语言处理 (NLP) 提供了新的方法来解释生物数据.

研究的目的:

  • 审查最近基于变压器的NLP模型对蛋白质组生物信息学的进展.
  • 检查这些模型的优点,局限性和应用.
  • 突出这一新兴领域的挑战和未来研究方向.

主要方法:

  • 对蛋白质组学中基于变压器的NLP模型的当前文献的综述.
  • 分析自我注意力机制和并行处理能力.
  • 在各种蛋白质组任务中评估模型性能.

主要成果:

  • 变压器模型显示了处理可变长度生物序列的巨大潜力.
  • 自我注意力机制使得能够捕捉蛋白质数据中的远程依赖性.
  • 这些模型为蛋白质组分析提供了提高准确性和效率的方法.

结论:

  • 基于变压器的NLP模型准备彻底改变蛋白质组生物信息学.
  • 需要进一步的研究来应对当前的挑战,并充分利用其潜力.
  • 这些模型有望增强对蛋白质结构,功能和相互作用的理解.