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

Correlation of Experimental Data01:23

Correlation of Experimental Data

230
Dimensional analysis simplifies complex physical problems and guides experimental investigations, but it does not provide complete solutions. It identifies the dimensionless groups that influence a phenomenon, but experimental data is needed to establish the specific relationships and validate theoretical predictions.
For example, a spherical particle moving through a viscous fluid experiences drag. Dimensional analysis shows that the drag force depends on the particle's diameter, velocity,...
230
Model Approaches for Pharmacokinetic Data: Distributed Parameter Models01:06

Model Approaches for Pharmacokinetic Data: Distributed Parameter Models

69
Pharmacokinetic models are mathematical constructs that represent and predict the time course of drug concentrations in the body, providing meaningful pharmacokinetic parameters. These models are categorized into compartment, physiological, and distributed parameter models.
The distributed parameter models are specifically designed to account for variations and differences in some drug classes. This model is particularly useful for assessing regional concentrations of anticancer or...
69
Correlation and Regression00:53

Correlation and Regression

1.2K
In statistics, correlation describes the degree of association between two variables. In the subfield of linear regression, correlation is mathematically expressed by the correlation coefficient, which describes the strength and direction of the relationship between two variables. The coefficient is symbolically represented by 'r' and ranges from -1 to +1. A positive value indicates a positive correlation where the two variables move in the same direction. A negative value suggests a...
1.2K
Calculating and Interpreting the Linear Correlation Coefficient01:11

Calculating and Interpreting the Linear Correlation Coefficient

5.9K
The correlation coefficient, r, developed by Karl Pearson in the early 1900s, is numerical and provides a measure of strength and direction of the linear association between the independent variable, x, and the dependent variable, y. Hence, it is also known as the Pearson product-moment correlation coefficient. It can be calculated using the following equation:
5.9K
Poisson Probability Distribution01:09

Poisson Probability Distribution

8.0K
A Poisson probability distribution is a discrete probability distribution. It gives the probability of a number of events occurring in a fixed interval of time or space if these events happen at a known average rate and independently of the time since the last event. For example, a book editor might be interested in the number of words spelled incorrectly in a particular book. It might be that, on average, there are five words spelled incorrectly in 100 pages. The interval is 100 pages.
The...
8.0K
Statistical Analysis: Overview01:11

Statistical Analysis: Overview

6.6K
When we take repeated measurements on the same or replicated samples, we will observe inconsistencies in the magnitude. These inconsistencies are called errors. To categorize and characterize these results and their errors, the researcher can use statistical analysis to determine the quality of the measurements and/or suitability of the methods.
One of the most commonly used statistical quantifiers is the mean, which is the ratio between the sum of the numerical values of all results and the...
6.6K

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

Colocalization of eQTLs With Type 2 Diabetes and Glycemic Traits Using Whole-Genome Sequences in Diverse Populations From the NHLBI Trans-Omics in Precision Medicine (TOPMed) Program.

Diabetes·2026
Same author

A Resampling-Based Framework for Network Structure Learning in High-Dimensional Data.

ArXiv·2026
Same author

Genetic associations with longevity in a Calabrian cohort: an exploratory genome-wide study.

GeroScience·2026
Same author

Rare coding variant architecture and gene discovery from 130,000 sequenced cases of atrial fibrillation.

Research square·2026
Same author

Discovery of gene-alcohol interaction loci influencing blood pressure in 1.1 million individuals from multiple populations.

Research square·2026
Same author

Metabolomic signatures of extreme old age: findings from the New England Centenarian Study.

GeroScience·2026
Same journal

A human-specific genetic modifier reconfigures large-scale cortical network dynamics underlying behavioral performance.

bioRxiv : the preprint server for biology·2026
Same journal

<i>Staphylococcus aureus</i> uses a eukaryotic-like uridyltransferase to make UDP-GlcNAc for cell wall synthesis.

bioRxiv : the preprint server for biology·2026
Same journal

Dynamic redistribution of eIF4F controls cap-dependent translation initiation.

bioRxiv : the preprint server for biology·2026
Same journal

When does additional information improve accuracy of RNA secondary structure prediction?

bioRxiv : the preprint server for biology·2026
Same journal

Normative brain-state trajectories reveal deviation from healthy aging in Alzheimer's disease.

bioRxiv : the preprint server for biology·2026
Same journal

Noradrenergic infraslow rhythm during sleep is the critical link between heart-rate dynamics and memory consolidation.

bioRxiv : the preprint server for biology·2026
查看所有相关文章

相关实验视频

Updated: Jun 28, 2025

Author Spotlight: Emerging Technologies and Advanced Tools for Decoding Metabolomics Data Analysis
07:11

Author Spotlight: Emerging Technologies and Advanced Tools for Decoding Metabolomics Data Analysis

Published on: November 10, 2023

2.3K

从相关数据学习高斯图形模型.

Zeyuan Song, Sophia Gunn, Stefano Monti

    bioRxiv : the preprint server for biology
    |April 15, 2024
    PubMed
    概括
    此摘要是机器生成的。

    我们开发了一个Bootstrap算法,从相关的生物医学数据中推断出高斯图形模型 (GGM). 这种方法精确控制了I型错误,并保持了基于家庭的复杂研究的统计能力.

    更多相关视频

    Identification of Disease-related Spatial Covariance Patterns using Neuroimaging Data
    14:27

    Identification of Disease-related Spatial Covariance Patterns using Neuroimaging Data

    Published on: June 26, 2013

    15.6K
    A Psychophysics Paradigm for the Collection and Analysis of Similarity Judgments
    08:12

    A Psychophysics Paradigm for the Collection and Analysis of Similarity Judgments

    Published on: March 1, 2022

    2.5K

    相关实验视频

    Last Updated: Jun 28, 2025

    Author Spotlight: Emerging Technologies and Advanced Tools for Decoding Metabolomics Data Analysis
    07:11

    Author Spotlight: Emerging Technologies and Advanced Tools for Decoding Metabolomics Data Analysis

    Published on: November 10, 2023

    2.3K
    Identification of Disease-related Spatial Covariance Patterns using Neuroimaging Data
    14:27

    Identification of Disease-related Spatial Covariance Patterns using Neuroimaging Data

    Published on: June 26, 2013

    15.6K
    A Psychophysics Paradigm for the Collection and Analysis of Similarity Judgments
    08:12

    A Psychophysics Paradigm for the Collection and Analysis of Similarity Judgments

    Published on: March 1, 2022

    2.5K

    科学领域:

    • 生物统计学 生物统计学
    • 基因组学就是基因组学.
    • 网络分析 网络分析

    背景情况:

    • 高斯图形模型 (GGM) 对于分析生物医学研究中的复杂关系至关重要.
    • 标准的GGM推断方法假定独立的观测,这在集群和纵向数据中经常被侵犯.
    • 忽视数据的相关性可能会导致膨胀的I型错误,损害研究结果.

    研究的目的:

    • 提出一种新的Bootstrap算法,从相关数据中推断GGM.
    • 在生物医学研究中处理非独立观察时解决现有方法的局限性.
    • 为分析基于家庭和纵向数据集的复杂关系提供统计学上可靠的方法.

    主要方法:

    • 开发了一个Bootstrap算法,用相关的观测来估计GGM.
    • 进行了广泛的模拟,使用来自家庭研究的相关数据来评估该方法.
    • 提出的方法用于分析来自长寿家庭研究的多基因风险得分.

    主要成果:

    • 引导方法有效地控制了相关数据中的I型错误膨胀.
    • 与忽视相关性的替代方法相比,拟议的算法保留了统计能力.
    • 长寿家庭研究的应用在现实世界数据集中展示了强大的I型错误控制.

    结论:

    • 拟议的Bootstrap算法提供了一种可靠的方法,可以从相关的生物医学数据中推断GGM.
    • 这种方法适用于复杂的基于家庭和纵向研究,提高了关系推断的准确性.
    • 该方法为在遗传和生物医学研究中分析高维相关数据提供了显著的进步.