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

Genomics02:02

Genomics

36.3K
Genomics is the science of genomes: it is the study of all the genetic material of an organism. In humans, the genome consists of information carried in 23 pairs of chromosomes in the nucleus, as well as mitochondrial DNA. In genomics, both coding and non-coding DNA is sequenced and analyzed. Genomics allows a better understanding of all living things, their evolution, and their diversity. It has a myriad of uses: for example, to build phylogenetic trees, to improve productivity and...
36.3K
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,...
3.9K
Combinatorial Gene Control02:33

Combinatorial Gene Control

8.3K
Combinatorial gene control is the synergistic action of several transcriptional factors to regulate the expression of a single gene. The absence of one or more of these factors may lead to a significant difference in the level of gene expression or repression.
The expression of more than 30,000 genes is controlled by approximately 2000-3000 transcription factors. This is possible because a single transcription factor can recognize more than one regulatory sequence. The specificity in gene...
8.3K

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

Transformer-assisted hierarchical deep reinforcement learning for energy and spectrum efficient MIMO-MC-CDMA in 6G networks.

Scientific reports·2026
Same author

Adaptive homomorphic federated learning framework for multi-institutional medical imaging with optimized diagnostic accuracy.

Scientific reports·2026
Same author

Enhancing parkinson disease detection through feature based deep learning with autoencoders and neural networks.

Scientific reports·2025
Same author

DRN-CDR: A cancer drug response prediction model using multi-omics and drug features.

Computational biology and chemistry·2024
Same author

Improved Protein Real-Valued Distance Prediction Using Deep Residual Dense Network (DRDN).

The protein journal·2022
Same author

Improved 3-D Protein Structure Predictions using Deep ResNet Model.

The protein journal·2021

相关实验视频

Updated: Jul 1, 2025

High-Throughput Transcriptome Analysis for Investigating Host-Pathogen Interactions
14:58

High-Throughput Transcriptome Analysis for Investigating Host-Pathogen Interactions

Published on: March 5, 2022

4.2K

TransNeT-CGP:通过整合转录组学和网络拓特征,以集群为基础的并发性基因优先级.

K R Saranya1, E R Vimina1, F R Pinto2

  • 1Department of Computer Science & IT, School of Computing, Amrita Vishwa Vidyapeetham, Kochi Campus, India.

Computational biology and chemistry
|March 10, 2024
PubMed
概括

这项研究引入了一种新的计算方法,通过分析蛋白质-蛋白质相互作用网络来识别驱动并发性疾病的关键基因. 这种方法有效地优先考虑涉及重叠疾病机制的基因,有助于开发向基因疗法.

关键词:
基因优先级配合疾病聚类 蛋白质与蛋白质相互作用网络

更多相关视频

JUMPn: A Streamlined Application for Protein Co-Expression Clustering and Network Analysis in Proteomics
07:28

JUMPn: A Streamlined Application for Protein Co-Expression Clustering and Network Analysis in Proteomics

Published on: October 19, 2021

3.2K
Candidate Gene Testing in Clinical Cohort Studies with Multiplexed Genotyping and Mass Spectrometry
05:53

Candidate Gene Testing in Clinical Cohort Studies with Multiplexed Genotyping and Mass Spectrometry

Published on: June 21, 2018

10.1K

相关实验视频

Last Updated: Jul 1, 2025

High-Throughput Transcriptome Analysis for Investigating Host-Pathogen Interactions
14:58

High-Throughput Transcriptome Analysis for Investigating Host-Pathogen Interactions

Published on: March 5, 2022

4.2K
JUMPn: A Streamlined Application for Protein Co-Expression Clustering and Network Analysis in Proteomics
07:28

JUMPn: A Streamlined Application for Protein Co-Expression Clustering and Network Analysis in Proteomics

Published on: October 19, 2021

3.2K
Candidate Gene Testing in Clinical Cohort Studies with Multiplexed Genotyping and Mass Spectrometry
05:53

Candidate Gene Testing in Clinical Cohort Studies with Multiplexed Genotyping and Mass Spectrometry

Published on: June 21, 2018

10.1K

科学领域:

  • 计算生物学和生物信息学
  • 系统生物学 系统生物学
  • 遗传学和基因组学 遗传学和基因组学

背景情况:

  • 一种疾病的基因干扰可能会影响其他疾病的途径,导致并发症.
  • 优先考虑调节共同生物机制的关键基因对于重叠疾病的有效基因疗法至关重要.

研究的目的:

  • 提出基于集群的计算方法,在重叠的疾病模块中优先考虑共患基因.
  • 分析蛋白与蛋白相互作用 (PPI) 网络,以确定伴随疾病的关键调节基因.

主要方法:

  • 从互动组中提取了疾病对子网络.
  • 使用基因表达相关性和中间中心性分配边缘权重.
  • 应用加权图集群,根据集群系数和邻近连接性对主导节点进行排名.

主要成果:

  • 与现有方法 (SAPDSB,S2B) 相比,在案例研究 (ALS-SMA,OC-IDBC) 中,拟议的方法确定了更多相关的途径和疾病特异性蛋白质复合体.
  • 排名最高的基因在被删除后显著破坏了网络连接,这表明它们的关键作用.
  • 功能和途径丰富分析证实了已识别的关键基因的机制相关性.

结论:

  • 拟议的基于集群的计算方法有效地识别了共同疾病中的关键基因.
  • 这种方法提供了对导致并发性疾病的复杂分子关系的宝贵见解.
  • 这些发现可以指导开发更精确的基因疗法,用于复杂的重叠疾病.