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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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Protein Networks02:26

Protein Networks

3.9K
An organism can have thousands of different proteins, and these proteins must cooperate to ensure the health of an organism. Proteins bind to other proteins and form complexes to carry out their functions. Many proteins interact with multiple other proteins creating a complex network of protein interactions.
These interactions can be represented through maps depicting protein-protein interaction networks, represented as nodes and edges. Nodes are circles that are representative of a protein,...
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RNA-seq03:21

RNA-seq

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RNA sequencing, or RNA-Seq, is a high-throughput sequencing technology used to study the transcriptome of a cell. Transcriptomics helps to interpret the functional elements of a genome and identify the molecular constituents of an organism. Additionally, it also helps in understanding the development of an organism and the occurrence of diseases. 
Before the discovery of RNA-seq, microarray-based methods and Sanger sequencing were used for transcriptome analysis. However, while...
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Conservation of Protein Domains Over Different Proteins02:26

Conservation of Protein Domains Over Different Proteins

10.8K
Protein domains are small structurally independent units that are part of a single amino acid chain.  Although these domains are often structurally independent, they may rely on synergistic effects to perform their functions as part of a larger protein. Protein domains may be conserved within the same organism, as well as across different organisms.
A limited set of protein domains often duplicate and recombine during evolution. These domains can be organized in different combinations to...
10.8K
Improving Translational Accuracy02:07

Improving Translational Accuracy

2.5K
2.5K
Protein-Protein Interfaces02:04

Protein-Protein Interfaces

3.7K
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多数据集集成和剩余连接 通过使用深度学习改进从转录组的蛋白质组预测.

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深度学习模型从转录组学改进了蛋白质组预测. 一个关键的发现是,在记住输入数据的神经架构搜索 (NAS) 模型中,残余连接的好处.

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科学领域:

  • 生物化学 生物化学
  • 生物信息学是一种生物信息学.
  • 计算生物学 计算生物学

背景情况:

  • 蛋白质组和转录组数据经常显示不良相关性.
  • 从基因表达 (转录组学) 中预测蛋白质量是具有挑战性的.
  • 了解转录蛋白关系在生物研究中至关重要.

研究的目的:

  • 为了提高从转录基因数据中预测蛋白质组数量的准确性.
  • 调查深度学习架构对此预测任务的影响.
  • 为了确定蛋白质预测的功能性重要转录.

主要方法:

  • 利用了来自临床蛋白质学瘤分析联盟 (CPTAC) 的公开数据.
  • 采用通过神经架构搜索 (NAS) 开发的深度学习模型.
  • 应用模型解释技术 (SHAP) 来分析转录的重要性.

主要成果:

  • 深度学习模型,特别是那些具有残余连接的模型,显著提高了蛋白质组预测的准确性.
  • 发现剩余连接对于在网络中保留输入信息至关重要.
  • SHAP分析确定了特定的转录组,这些组对于准确的蛋白质水平预测至关重要.

结论:

  • 神经架构搜索驱动的深度学习提供了一种强大的方法,用于从转录组中预测蛋白质组.
  • 建筑选择,就像剩余连接一样,极大地影响了模型的性能.
  • 模型可解释性方法可以揭示对基因-蛋白质关系的生物学见解.