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

Multicompartment Models: Overview01:14

Multicompartment Models: Overview

128
Multicompartment models are mathematical constructs that depict how drugs are distributed and eliminated within the body. They segment the body into several compartments, symbolizing various physiological or anatomical areas connected through drug transfer processes such as absorption, metabolism, distribution, and elimination.
These models offer a more comprehensive representation of drug behavior in the body than one-compartment models. They accommodate the complexity of drug distribution,...
128
One-Compartment Open Model: Wagner-Nelson and Loo Riegelman Method for ka Estimation01:24

One-Compartment Open Model: Wagner-Nelson and Loo Riegelman Method for ka Estimation

464
This lesson introduces two critical methods in pharmacokinetics, the Wagner-Nelson and Loo-Riegelman methods, used for estimating the absorption rate constant (ka) for drugs administered via non-intravenous routes. The Wagner-Nelson method relates ka to the plasma concentration derived from the slope of a semilog percent unabsorbed time plot. However, it is limited to drugs with one-compartment kinetics and can be impacted by factors like gastrointestinal motility or enzymatic degradation.
On...
464
Overview of Minitab01:11

Overview of Minitab

120
Minitab is a statistical software package designed for data analysis. With its origins in the 1970s and development at Pennsylvania State University, Minitab has grown significantly in its capabilities and applications. It plays a crucial role in quality management projects, especially in Six Sigma initiatives, by offering tools for process improvement and statistical analysis. Minitab's significance lies in its user-friendly interface, making complex statistical analysis accessible to...
120
Biostatistics: Overview01:20

Biostatistics: Overview

237
Biostatistics plays a crucial role in understanding and analyzing data in healthcare and biology. Biostatisticians conduct experiments, gather evidence, and draw meaningful conclusions using statistical methods and techniques. Different variables form the foundation of biostatistical analysis, allowing researchers to understand and interpret data effectively. These variables are classified into different types, each serving a specific purpose in statistical analysis.
Discrete variables are...
237

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

PYRIN m<sup>6</sup>A modification drives cardiomyocyte PANoptosis in sepsis-induced cardiomyopathy.

Acta pharmacologica Sinica·2026
Same author

Targeting PHKA2 by Thymol alleviates sepsis induced cardiomyocyte pyroptosis via FOXA1/KLF4-mediated macrophage polarization.

Phytomedicine : international journal of phytotherapy and phytopharmacology·2026
Same author

Genetic Association and Clinical Relevance of TNFSF13B/BAFF and PADI4 Polymorphisms in ANCA-Associated Vasculitis: A Case-Control Study with Genetic Model Analysis in Guangxi Population.

Genes·2026
Same author

Seasonal Dynamics Without Reset: Core Microbiota Stability Across Development in a Gall-Dwelling Weevil.

Insects·2026
Same author

Prevalence and associated factors of sarcopenia in stroke inpatients: a cross-sectional study with SarQoL-assessed quality of life.

BMC neurology·2026
Same author

Author Correction: Rice roots recruit Bacillus via the secretion of heptadecanoic acid.

Nature plants·2026

相关实验视频

Updated: Jun 24, 2025

Divergence of Root Microbiota in Different Habitats based on Weighted Correlation Networks
09:49

Divergence of Root Microbiota in Different Habitats based on Weighted Correlation Networks

Published on: September 25, 2021

4.3K

ggClusterNet:用于微生物组网络分析和基于模块化的多个网络布局的R包.

Tao Wen1,2, Penghao Xie1, Shengdie Yang1

  • 1Jiangsu Provincial Key Lab for Organic Solid Waste Utilization, Key Laboratory of Green Intelligent Fertilizer Innovation, Jiangsu Collaborative Innovation Center for Solid Organic Wastes, Educational Ministry Engineering Center of Resource-Saving Fertilizers Nanjing Agricultural University Nanjing China.

iMeta
|June 13, 2024
PubMed
概括

研究人员开发了ggClusterNet,这是一个用于生态网络分析的R包. 该工具通过集成的功能和多个布局算法简化了微生物组网络可视化和挖掘.

关键词:
一个R包一个R包微生物组是一个微生物组.网络分析 网络分析视觉化的可视化

更多相关视频

Heuristic Mining of Hierarchical Genotypes and Accessory Genome Loci in Bacterial Populations
08:03

Heuristic Mining of Hierarchical Genotypes and Accessory Genome Loci in Bacterial Populations

Published on: December 7, 2021

2.1K
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

3.1K

相关实验视频

Last Updated: Jun 24, 2025

Divergence of Root Microbiota in Different Habitats based on Weighted Correlation Networks
09:49

Divergence of Root Microbiota in Different Habitats based on Weighted Correlation Networks

Published on: September 25, 2021

4.3K
Heuristic Mining of Hierarchical Genotypes and Accessory Genome Loci in Bacterial Populations
08:03

Heuristic Mining of Hierarchical Genotypes and Accessory Genome Loci in Bacterial Populations

Published on: December 7, 2021

2.1K
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

3.1K

科学领域:

  • 生态生态学 生态生态学
  • 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:

  • 网络分析在生态研究中越来越重要.
  • 现有的生态网络分析工具可能很繁.
  • 需要更加用户友好和强大的分析软件.

研究的目的:

  • 引入ggClusterNet,这是一个用于生态网络分析的新型R包.
  • 为研究人员提供一个易于使用的工具来构建,可视化和挖掘微生物网络.
  • 为了提高生态研究中的网络分析的易用性和效率.

主要方法:

  • 开发一个R包,ggClusterNet.
  • 集成十个不同的网络布局算法,以增强可视化.
  • 包括用于网络挖掘,财产计算和双边网络分析的各种功能.
  • 实施管道功能,以简化网络分析.

主要成果:

  • ggClusterNet提供十个专门的网络布局算法,用于微生物组网络可视化.
  • 该包包括微生物网络分析的集成功能,如相关性,属性计算和模块识别.
  • 管道功能有助于快速网络和双边网络分析.
  • 该软件包在GitHub和Gitee上公开提供,附有全面的文档.

结论:

  • ggClusterNet为生态网络分析提供了一个方便而强大的解决方案.
  • 该套件简化了研究人员复杂的网络挖掘和可视化任务.
  • 预计ggClusterNet将通过改善可访问性和功能,促进生态网络研究的进步.