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相关概念视频

Protein Networks02:26

Protein Networks

4.6K
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,...
4.6K
Regulated mRNA Transport02:22

Regulated mRNA Transport

7.1K
In eukaryotes, transcription and translation are compartmentalized; an mRNA is first synthesized in the nucleus and then selectively transported to the cytoplasm for protein synthesis. Before transport, a pre-mRNA undergoes several steps of post-transcriptional modifications including splicing, 5' capping, and the addition of a poly-adenine tail. Various proteins bind to the pre-mRNA during these modifications. The mRNA transport takes place with the help of multiple proteins playing...
7.1K
Nuclear Localization Signals and Import01:46

Nuclear Localization Signals and Import

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Proteins targeted to the nucleus carry short stretches of amino acid sequences called the nuclear localization signal or NLS. Classical nuclear localization signals are of two types: monopartite and bipartite NLS. Monopartite classical NLS (cNLS) consists of a single cluster of 4-8 amino acids. Bipartite cNLS consists of two clusters of  2-3 amino acids and a 9-12 residue long proline-rich linker bridging the two clusters. Signal clusters are rich in positively charged amino acids such as...
8.0K
Nuclear Protein Sorting01:34

Nuclear Protein Sorting

6.6K
Nuclear protein sorting is the selective trafficking of histones, polymerases, gene regulatory proteins into the nucleus and exporting RNAs and ribosomes to the cytosol. It is a tightly controlled process that regulates gene expression within a cell.
Proteins targeted to the nucleus carry nuclear localization signals or NLS recognized by import receptors in the cytosol. Similarly, proteins with nuclear export signals are recognized by export receptors. Import and export receptors are...
6.6K
RNA-seq03:21

RNA-seq

12.3K
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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相关实验视频

Updated: Mar 11, 2026

In Silico Identification and Characterization of circRNAs During Host-Pathogen Interactions
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In Silico Identification and Characterization of circRNAs During Host-Pathogen Interactions

Published on: October 21, 2022

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通过融合circRNA序列和网络信息来预测circRNA亚细胞定位.

Lei Chen1, Jinghai Hu2, Bo Zhou3

  • 1College of Information Engineering, Shanghai Maritime University, Shanghai, 201306, People's Republic of China. chen_lei1@163.com.

Scientific reports
|March 10, 2026
PubMed
概括

预测循环RNA (circRNA) 位置是理解它们功能的关键. 一个新的计算模型,CircLoc,使用序列和网络特征准确预测circRNA亚细胞定位.

关键词:
在CircRNA中.图表注意力自动编码器在Node2vec中,可以使用Node2vec.亚细胞局部化的局部化.

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相关实验视频

Last Updated: Mar 11, 2026

In Silico Identification and Characterization of circRNAs During Host-Pathogen Interactions
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In Silico Identification and Characterization of circRNAs During Host-Pathogen Interactions

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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
  • 分子生物学分子生物学

背景情况:

  • 循环RNAs (circRNAs) 越来越多地被认为是它们在生物过程中的关键作用.
  • 了解circRNA功能需要确定它们的亚细胞局部.
  • 传统的本地化实验方法昂贵且耗时.

研究的目的:

  • 开发一个高效的计算模型,CircLoc,用于预测circRNA亚细胞定位.
  • 利用circRNA序列和网络信息进行准确的预测.

主要方法:

  • 使用k-mer,大型语言模型 (RNAErnie) 和网络表示学习 (node2vec,图表注意力自动编码器) 的特征提取.
  • 使用自我注意层和完全连接的预测层集成提取的功能.
  • 通过十倍交叉验证进行模型评估.

主要成果:

  • 赛克洛克的平均AUC为0.7856和AUPR为0.4055.5,平均AUC为0.7856和AUPR为0.4055.
  • 性能超过了传统的多标签分类和miRNA本地化预测模型.
  • 废弃试验证实了该模型的有效性和特征的重要性.

结论:

  • CircLoc提供了一种有效的计算方法,用于预测circRNA亚细胞定位.
  • 该模型可以作为推进circRNA研究的宝贵工具.