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

Vector Algebra: Graphical Method01:10

Vector Algebra: Graphical Method

12.0K
Vectors can be multiplied by scalars, added to other vectors, or subtracted from other vectors. The vector sum of two (or more) vectors is called the resultant vector or, for short, the resultant.
We use the laws of geometry to construct resultant vectors, followed by trigonometry to find vector magnitudes and directions. For a geometric construction of the sum of two vectors in a plane, we follow the parallelogram rule. Suppose two vectors are at arbitrary positions. Translate either one of...
12.0K
Deconvolution01:20

Deconvolution

138
Deconvolution, also known as inverse filtering, is the process of extracting the impulse response from known input and output signals. This technique is vital in scenarios where the system's characteristics are unknown, and they must be inferred from the observable signals.
Deconvolution involves several mathematical techniques to derive the impulse response. One common approach is polynomial division. In this method, the input and output sequences are treated as coefficients of...
138
Network Function of a Circuit01:25

Network Function of a Circuit

268
Frequency response analysis in electrical circuits provides vital insights into a circuit's behavior as the frequency of the input signal changes. The transfer function, a mathematical tool, is instrumental in understanding this behavior. It defines the relationship between phasor output and input and comes in four types: voltage gain, current gain, transfer impedance, and transfer admittance. The critical components of the transfer function are the poles and zeros.
268
Sequence Networks of Rotating Machines01:24

Sequence Networks of Rotating Machines

98
A Y-connected synchronous generator, grounded through a neutral impedance, is designed to produce balanced internal phase voltages with only positive-sequence components. The generator's sequence networks include a source voltage that is exclusively in the positive-sequence network. The sequence components of line-to-ground voltages at the generator terminals illustrate this configuration.
Zero-sequence current induces a voltage drop across the generator's neutral impedance and other...
98
Convolution: Math, Graphics, and Discrete Signals01:24

Convolution: Math, Graphics, and Discrete Signals

234
In any LTI (Linear Time-Invariant) system, the convolution of two signals is denoted using a convolution operator, assuming all initial conditions are zero. The convolution integral can be divided into two parts: the zero-input or natural response and the zero-state or forced response, with t0 indicating the initial time.
To simplify the convolution integral, it is assumed that both the input signal and impulse response are zero for negative time values. The graphical convolution process...
234
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

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

MR2G: A novel framework for causal network inference using GWAS summary data.

PLoS genetics·2026
Same author

Extracting Genetically-Imputed Causal Features From ECG Data.

Statistical analysis and data mining·2026
Same author

scTWAS: a powerful statistical framework for single-cell transcriptome-wide association studies.

Nature communications·2026
Same author

Large-Scale Genotype-Based Trait Imputation With Multi-Ancestry GWAS Data.

Genetic epidemiology·2026
Same author

Co-expression-wide association studies link genetically regulated interactions with complex traits.

Nature communications·2025
Same author

scTWAS: A powerful statistical framework for single-cell transcriptome-wide association studies.

Research square·2025
Same journal

Fast penalized generalized estimating equations for large longitudinal functional datasets.

Biometrics·2026
Same journal

Causally-interpretable random-effects meta-analysis.

Biometrics·2026
Same journal

Statistical inference for mean function of partially observed functional time series.

Biometrics·2026
Same journal

Subgroup identification via Interaction Tree and Mixed Model for Repeated Measures with application to Alzheimer's disease.

Biometrics·2026
Same journal

Finite mixtures of linear quantile regressions with concomitant variables: a solution to endogeneity in longitudinal data modeling.

Biometrics·2026
Same journal

Discussion on "INTACT: a method for integration of longitudinal physical activity data from multiple sources" by Jingru Zhang, Erjia Cui, Hongzhe Li, and Haochang Shou.

Biometrics·2026
查看所有相关文章

相关实验视频

Updated: Jun 11, 2025

Author Spotlight: Advancing Large-Scale Neural Dynamics Through HD-MEA Technology
09:44

Author Spotlight: Advancing Large-Scale Neural Dynamics Through HD-MEA Technology

Published on: March 8, 2024

4.7K

关于非定向图形的网络解卷.

Zhaotong Lin1,2, Isaac Pan3, Wei Pan1

  • 1Division of Biostatistics and Health Data Science, University of Minnesota, Minneapolis, MN 55455, United States.

Biometrics
|October 8, 2024
PubMed
概括
此摘要是机器生成的。

网络解构 (ND) 从总效应网络重建直接效应网络. 这项研究澄清了ND.

关键词:
直接效应网络,定向图,高斯图形模型.边际相关性,部分相关性,总效应图.

更多相关视频

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
Modeling the Functional Network for Spatial Navigation in the Human Brain
05:55

Modeling the Functional Network for Spatial Navigation in the Human Brain

Published on: October 13, 2023

1.0K

相关实验视频

Last Updated: Jun 11, 2025

Author Spotlight: Advancing Large-Scale Neural Dynamics Through HD-MEA Technology
09:44

Author Spotlight: Advancing Large-Scale Neural Dynamics Through HD-MEA Technology

Published on: March 8, 2024

4.7K
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
Modeling the Functional Network for Spatial Navigation in the Human Brain
05:55

Modeling the Functional Network for Spatial Navigation in the Human Brain

Published on: October 13, 2023

1.0K

科学领域:

  • 网络分析 网络分析
  • 图形理论是指图形的理论.
  • 统计遗传学 统计遗传学

背景情况:

  • 网络解构 (ND) 从总效应网络重建直接效应网络.
  • 现有的ND对非定向图的应用缺乏明确的理论依据.
  • 在许多科学领域,区分直接和间接的影响至关重要.

研究的目的:

  • 为了在非定向图中提供网络解卷的理论理由.
  • 探索ND和精度矩阵之间的关系.
  • 证明在遗传关联研究中ND的新型应用.

主要方法:

  • 在ND中澄清隐性线性模型假设.
  • 导出ND与精度矩阵方法之间的等价性.
  • 对全基因组关联研究数据的ND的应用.

主要成果:

  • 建立了将ND应用于非定向图的正式理由.
  • 证明了ND和精度矩阵方法之间的等价性.
  • ND成功对比了身高和冠状动脉疾病风险的边缘和条件遗传相关性.

结论:

  • 网络解卷是一种理论上合理且实际上适用于定向和非定向图的方法.
  • 该研究为解释复杂网络中直接和间接影响提供了一个强大的框架.
  • 在大规模的遗传研究中,ND为因果推理提供了一个有前途的方法.