Jove
Visualize
联系我们
JoVE
x logofacebook logolinkedin logoyoutube logo
关于 JoVE
概览领导团队博客JoVE 帮助中心
作者
出版流程编辑委员会范围与政策同行评审常见问题投稿
图书馆员
用户评价订阅访问资源图书馆顾问委员会常见问题
研究
JoVE JournalMethods CollectionsJoVE Encyclopedia of Experiments存档
教育
JoVE CoreJoVE BusinessJoVE Science EducationJoVE Lab Manual教师资源中心教师网站
使用条款与条件
隐私政策
政策

相关概念视频

Improving Translational Accuracy02:07

Improving Translational Accuracy

14.1K
Base complementarity between the three base pairs of mRNA codon and the tRNA anticodon is not a failsafe mechanism. Inaccuracies can range from a single mismatch to no correct base pairing at all. The free energy difference between the correct and nearly correct base pairs can be as small as 3 kcal/ mol. With complementarity being the only proofreading step, the estimated error frequency would be one wrong amino acid in every 100 amino acids incorporated. However, error frequencies observed in...
14.1K
Improving Translational Accuracy02:07

Improving Translational Accuracy

3.5K
3.5K
RNA Editing02:23

RNA Editing

9.8K
RNA editing is a post-transcriptional modification where a precursor mRNA (pre-mRNA) nucleotide sequence is changed by base insertion, deletion, or modification. The extent of RNA editing varies from a few hundred bases, in mitochondrial DNA of trypanosomes, to a just single base, in nuclear genes of mammals. Even a single base change in the pre-mRNA can convert a codon for one amino acid into the codon for another amino acid or a stop codon. This type of re-coding can significantly affect the...
9.8K
Genome-wide Association Studies-GWAS01:11

Genome-wide Association Studies-GWAS

15.3K
Genome-wide association studies or GWAS are used to identify whether common SNPs are associated with certain diseases. Suppose specific SNPs are more frequently observed in individuals with a particular disease than those without the disease. In that case, those SNPs are said to be associated with the disease. Chi-square analysis is performed to check the probability of the allele likely to be associated with the disease.
GWAS does not require the identification of the target gene involved in...
15.3K
Translation01:31

Translation

17.5K
Translation is the process of synthesizing proteins from the genetic information carried by messenger RNA (mRNA). Following transcription, it constitutes the final step in the expression of genes. This process is carried out by ribosomes, complexes of protein and specialized RNA molecules. Ribosomes, transfer RNA (tRNA), and other proteins produce a chain of amino acids—the polypeptide—as the end product of translation.
Translation Produces the Building Blocks of Life
Proteins are...
17.5K
Leaky Scanning02:28

Leaky Scanning

5.6K
During most eukaryotic translation processes, the small 40S ribosome subunit scans an mRNA from its 5' end until it encounters the first start AUG codon. The large 60S ribosomal subunit then joins the smaller one to initiate protein synthesis. The location of the translation initiation is largely determined by the nucleotides near the start codon as there may be multiple translation initiation sites present on the mRNA.  Marilyn Kozak discovered that the sequence RCCAUGG (where R...
5.6K

您也可能阅读

相关文章

通过共同作者、期刊和引用图与本文相关的文章。

排序
Same author

A Whole-Brain Connectivity Map of VTA and SNc Glutamatergic and GABAergic Neurons in Mice.

Frontiers in neuroanatomy·2022
Same author

Protein Interaction Network Reconstruction with a Structural Gated Attention Deep Model by Incorporating Network Structure Information.

Journal of chemical information and modeling·2022
Same author

DNA Barcode Identification of Fish Products from Guiyang Markets in Southwestern People's Republic of China.

Journal of food protection·2022
Same author

MiR-206 suppresses the deterioration of intrahepatic cholangiocarcinoma and promotes sensitivity to chemotherapy by inhibiting interactions with stromal CAFs.

International journal of biological sciences·2022
Same author

A Nomogram for Predicting Brain Metastasis in IIIA-N2 Non-Small Cell Lung Cancer After Complete Resection: A Competing Risk Analysis.

Frontiers in oncology·2021
Same author

Fast and simple calibration of frequency response and IQ skew for a coherent optical transmitter using a low-bandwidth photodetector.

Optics letters·2021

相关实验视频

Updated: Jan 17, 2026

A Knowledge Graph Approach to Elucidate the Role of Organellar Pathways in Disease via Biomedical Reports
07:35

A Knowledge Graph Approach to Elucidate the Role of Organellar Pathways in Disease via Biomedical Reports

Published on: October 13, 2023

2.1K

对比的超图协作过用于转移RNA与疾病的关联预测.

Tianxiang Ouyang1, Yuanpeng Zhang2, Zhijian Huang1

  • 1School of Computer Science and Engineering, Central South University, 932 Lushan South Road, Yuelu District, Changsha, Hunan 410083, China.

Briefings in bioinformatics
|September 25, 2025
PubMed
概括

这项研究介绍了CoHGCL,这是一种用于预测转移RNA (tRNA) 和疾病关联的新型计算框架. 该方法显著提高了识别这些关键链接的准确性,以了解疾病机制.

关键词:
过度图形 (hypergraph) 是一个超图形.神经协作过神经协作过节点级的对比学习学习.与tRNA疾病相关的关联

更多相关视频

In Silico Identification and Characterization of circRNAs During Host-Pathogen Interactions
10:27

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

Published on: October 21, 2022

2.0K
Author Spotlight: Impact of Intergenic Interactions on Disease-Identifying Dark Biomarkers
03:37

Author Spotlight: Impact of Intergenic Interactions on Disease-Identifying Dark Biomarkers

Published on: March 1, 2024

1.2K

相关实验视频

Last Updated: Jan 17, 2026

A Knowledge Graph Approach to Elucidate the Role of Organellar Pathways in Disease via Biomedical Reports
07:35

A Knowledge Graph Approach to Elucidate the Role of Organellar Pathways in Disease via Biomedical Reports

Published on: October 13, 2023

2.1K
In Silico Identification and Characterization of circRNAs During Host-Pathogen Interactions
10:27

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

Published on: October 21, 2022

2.0K
Author Spotlight: Impact of Intergenic Interactions on Disease-Identifying Dark Biomarkers
03:37

Author Spotlight: Impact of Intergenic Interactions on Disease-Identifying Dark Biomarkers

Published on: March 1, 2024

1.2K

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
  • 基因组学就是基因组学.

背景情况:

  • 转移RNA (tRNA) 对于蛋白质合成和细胞平衡至关重要.
  • 新出现的证据将tRNA与各种疾病进展联系起来.
  • 准确预测tRNA与疾病的关联对于疾病机制研究和精准医学至关重要.

研究的目的:

  • 开发一种先进的计算框架,用于预测tRNA与疾病的关联.
  • 克服现有方法在处理复杂和异质数据方面的局限性.
  • 提高tRNA疾病关联预测的准确性和可靠性.

主要方法:

  • 引入了对比的超图协作过 (CoHGCL),集成了超图对比学习和协作过.
  • 使用图表注意网络和随机步行与重启用于结构和拓特征提取.
  • 利用节点级对比学习用于多视图特征嵌入和通用矩阵因子化/MLP用于关联建模.

主要成果:

  • 与现有方法相比,CoHGCL在五倍交叉验证中表现优越.
  • 获得了0.9623的接收器操作特征曲线 (AUC) 下面的面积和0.9430.0的精度召回曲线 (AUPRC) 下面的面积.
  • 案例研究验证了CoHGCL在发现新型和生物学相关的tRNA疾病关联方面的能力.

结论:

  • CoHGCL提供了一种强大而有效的方法来预测tRNA与疾病的关联.
  • 该框架促进了对tRNA在疾病发病过程中的作用的理解.
  • CoHGCL为精准医学和未来的生物医学研究提供了宝贵的工具.