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

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
Protein-protein Interfaces02:04

Protein-protein Interfaces

12.5K
Many proteins form complexes to carry out their functions, making protein-protein interactions (PPIs) essential for an organism's survival. Most PPIs are stabilized by numerous weak noncovalent chemical forces. The physical shape of the interfaces determines the way two proteins interact. Many globular proteins have closely-matching shapes on their surfaces, which form a large number of weak bonds. Additionally, many PPIs occur between two helices or between a surface cleft and a...
12.5K

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

Validation of an Enzyme-Linked Immunosorbent Assay for Measuring Leptin, a Key Metabolic Hormone, in Dried Blood Spot Samples.

American journal of human biology : the official journal of the Human Biology Council·2025
Same author

Image processing approach provides robust feature extraction for classification with small sample sizes.

Proceedings of the 2023 7th International Conference on Information System and Data Mining : Atlanta, GA, United States, May 10-12, 202. International Conference on Information System and Data Mining (7th : 2023 : Atlanta, Ga.)·2025
Same author

Influence of multi-species data on gene-disease associations in substance use disorder using random walk with restart models.

PloS one·2025
Same author

A graph theoretical approach to experimental prioritization in genome-scale investigations.

Mammalian genome : official journal of the International Mammalian Genome Society·2024
Same author

Long-term drinking stability in the open-access self-administration monkey model.

Alcohol (Fayetteville, N.Y.)·2023
Same author

Exploring links between pathogen avoidance motivation, COVID-19 case counts, and immune function.

American journal of human biology : the official journal of the Human Biology Council·2022
Same journal

Transcriptomic analysis reveals FcγR-mediated phagocytosis as a key pathway for the anti-inflammatory action of <i>Polygonatum sibiricum</i> polysaccharides in loach.

Frontiers in genetics·2026
Same journal

A novel <i>ABO</i> splice site variant underlying the A<sub>3</sub> phenotype: immunogenetic basis and functional dissection.

Frontiers in genetics·2026
Same journal

Case Report: Identification of two novel <i>ALMS1</i> variants in a patient with a ciliopathy resembling Alström syndrome.

Frontiers in genetics·2026
Same journal

Integrative analysis identifies Hspa5 as a key regulator of the ERS/UPR-immune axis in spinal cord injury.

Frontiers in genetics·2026
Same journal

Evaluation of genomic selection to improve survival of eastern oysters infected with <i>Perkinsus marinus</i>.

Frontiers in genetics·2026
Same journal

A rescue assay for genetic diagnosis of oculocutaneous albinism using melanocytic MNT1 knock-out cells.

Frontiers in genetics·2026
查看所有相关文章

相关实验视频

Updated: Jul 2, 2025

Inherent Dynamics Visualizer, an Interactive Application for Evaluating and Visualizing Outputs from a Gene Regulatory Network Inference Pipeline
10:44

Inherent Dynamics Visualizer, an Interactive Application for Evaluating and Visualizing Outputs from a Gene Regulatory Network Inference Pipeline

Published on: December 7, 2021

2.1K

KNeXT:一个基于NetworkX的拓相关KEGG解析器.

Everest Uriel Castaneda1,2, Erich J Baker3

  • 1Department of Biology, Baylor University, Waco, TX, United States.

Frontiers in genetics
|February 28, 2024
PubMed
概括
此摘要是机器生成的。

由于数据结构的限制,从KEGG标记语言 (KGML) 文件中自动创建基因网络是很困难的. KNeXT解析器准确地重建遗传和混合网络,保留拓环境和节点位置,以可靠的路径可视化.

关键词:
凯格 (KEGG) 是一个在KGML图表中,KGML图表显示了这是一个KGML解析器.网络X 网络X 网络XPython 是一个 Python 语言.

更多相关视频

Network Pharmacology and Validation of the Antidepressant Mechanisms of Qiangzhifang in a Chronic Restraint Stress-induced Depression Rat Model
08:15

Network Pharmacology and Validation of the Antidepressant Mechanisms of Qiangzhifang in a Chronic Restraint Stress-induced Depression Rat Model

Published on: June 6, 2025

50
A Web Tool for Generating High Quality Machine-readable Biological Pathways
08:01

A Web Tool for Generating High Quality Machine-readable Biological Pathways

Published on: February 8, 2017

17.6K

相关实验视频

Last Updated: Jul 2, 2025

Inherent Dynamics Visualizer, an Interactive Application for Evaluating and Visualizing Outputs from a Gene Regulatory Network Inference Pipeline
10:44

Inherent Dynamics Visualizer, an Interactive Application for Evaluating and Visualizing Outputs from a Gene Regulatory Network Inference Pipeline

Published on: December 7, 2021

2.1K
Network Pharmacology and Validation of the Antidepressant Mechanisms of Qiangzhifang in a Chronic Restraint Stress-induced Depression Rat Model
08:15

Network Pharmacology and Validation of the Antidepressant Mechanisms of Qiangzhifang in a Chronic Restraint Stress-induced Depression Rat Model

Published on: June 6, 2025

50
A Web Tool for Generating High Quality Machine-readable Biological Pathways
08:01

A Web Tool for Generating High Quality Machine-readable Biological Pathways

Published on: February 8, 2017

17.6K

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
  • 系统生物学 系统生物学

背景情况:

  • 从基因和基因组 (KEGG) 标记语言 (KGML) 文件中重建基因和混合基因复合网络具有挑战性.
  • 当前的数据结构掩盖了原来的拓环境,导致了不准确的网络表示.
  • 重叠的接入号码和重复的标识符会导致网络社区的人工崩.

研究的目的:

  • 开发一种新的基于Python的解析器,KNeXT,用于从KGML数据中准确地回顾基因和混合网络.
  • 克服现有的解析器在保护生物网络的拓环境方面的局限性.
  • 确保高保真度的拓表示和对原始节点位置的程序访问.

主要方法:

  • 开发了一个基于Python的KEGG NetworkX拓 (KNeXT) 解析器.
  • 通过内置的API来摄取KGML文件以动态创建拓表示.
  • 使用NetworkX框架生成与其他图形框架兼容的分别为tab的文件.
  • 启用了本地文件或单个文件的解析,并将其转换为NCBI或UniProt ID.

主要成果:

  • KNeXT从KGML地图数据中准确地汇总了基因和混合网络.
  • 解析器保留了原来的拓环境和节点位置 (x-y轴).
  • 生成的以标签区分的文件保留了对路径数据的程序访问权限,并且可以导入其他图形框架.
  • KNeXT支持对本地文件/文件进行解析和ID转换 (NCBI/UniProt).

结论:

  • KNeXT提供了一个强大的解决方案,可以准确地重建KEGG路径,克服以前方法的局限性.
  • 该工具确保保留原始网络上下文和拓细节.
  • 在生物信息学研究中,KNeXT提高了途径可视化和分析的可靠性.