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

Mechanistic Models: Compartment Models in Individual and Population Analysis01:23

Mechanistic Models: Compartment Models in Individual and Population Analysis

26
Mechanistic models are utilized in individual analysis using single-source data, but imperfections arise due to data collection errors, preventing perfect prediction of observed data. The mathematical equation involves known values (Xi), observed concentrations (Ci), measurement errors (εi), model parameters (ϕj), and the related function (ƒi) for i number of values. Different least-squares metrics quantify differences between predicted and observed values. The ordinary least...
26
Mutation, Gene Flow, and Genetic Drift01:09

Mutation, Gene Flow, and Genetic Drift

58.0K
In a population that is not at Hardy-Weinberg equilibrium, the frequency of alleles changes over time. Therefore, any deviations from the five conditions of Hardy-Weinberg equilibrium can alter the genetic variation of a given population. Conditions that change the genetic variability of a population include mutations, natural selection, non-random mating, gene flow, and genetic drift (small population size).
58.0K
Mechanistic Models: Compartment Models in Algorithms for Numerical Problem Solving01:29

Mechanistic Models: Compartment Models in Algorithms for Numerical Problem Solving

38
Mechanistic models play a crucial role in algorithms for numerical problem-solving, particularly in nonlinear mixed effects modeling (NMEM). These models aim to minimize specific objective functions by evaluating various parameter estimates, leading to the development of systematic algorithms. In some cases, linearization techniques approximate the model using linear equations.
In individual population analyses, different algorithms are employed, such as Cauchy's method, which uses a...
38
Stratified Sampling Method01:16

Stratified Sampling Method

11.7K
Sampling is a technique to select a portion (or subset) of the larger population and study that portion (the sample) to gain information about the population. The sampling method ensures that samples are drawn without bias and accurately represent the population. Because measuring the entire population in a study is not practical, researchers use samples to represent the population of interest.
To choose a stratified sample, divide the population into groups called strata and then take a...
11.7K
Model Approaches for Pharmacokinetic Data: Distributed Parameter Models01:06

Model Approaches for Pharmacokinetic Data: Distributed Parameter Models

56
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...
56
Genetic Variation01:25

Genetic Variation

256
Genetic variation is the diversity in DNA sequences found among individuals of the same species. This diversity is crucial for a species' survival because it helps organisms adapt to environmental changes. Genetic variation begins with fertilization, where an egg and sperm cell merge. Each of these cells carries 23 chromosomes, up to 46 in the fertilized egg. Chromosomes are long DNA strands that contain genes, the basic units of heredity.
Genes exist in different versions called alleles,...
256

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

Genetic drivers of etiologic heterogeneity in thyroid cancer.

Nature communications·2026
Same author

Cross-definition GWAS of IBS in 2.8 million individuals reveals cardiometabolic and triglyceride-linked mechanisms.

Gut·2026
Same author

Drivers of Vaccine Uptake for Aboriginal and Torres Strait Islander Children to Inform Tailored Strategies: A Qualitative Study Exploring Health Service Provider Perspective.

The Medical journal of Australia·2026
Same author

Tread lightly interpreting group differences in genetic risk.

ArXiv·2026
Same author

The Biobank Rare Variant consortium powers the discovery of rare genetic associations through global collaboration.

medRxiv : the preprint server for health sciences·2026
Same author

IgA Targeting in the Infant Gut Is Modulated by Diet and Increasingly Directed Towards Persistent Species.

bioRxiv : the preprint server for biology·2026

相关实验视频

Updated: Jun 1, 2025

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

在大规模遗传总结统计数据中,通过混合模型来表征亚体结构.

Hayley R Stoneman1, Adelle M Price2, Nikole Scribner Trout2

  • 1Department of Biomedical Informatics, University of Colorado Anschutz Medical Campus, Aurora, CO 80045, USA; Human Medical Genetics and Genomics Program, University of Colorado Anschutz Medical Campus, Aurora, CO 80045, USA.

American journal of human genetics
|January 17, 2025
PubMed
概括

Summix2通过估计和调整人口亚结构来协调遗传总结数据. 这提高了多样化的遗传数据集的可用性,导致了更公平,更强大的研究成果.

关键词:
混合混合的 混合的混是一种混.公平的研究公平的研究.联合学习的联合学习遗传相似性 遗传相似性基因总结数据遗传总结数据统一化和化 统一化和化当地的祖先.人口分层的人口分层.选择的选择选择的选择.基层结构的基础结构.数据总结数据的总结.

更多相关视频

A Novel Bayesian Change-point Algorithm for Genome-wide Analysis of Diverse ChIPseq Data Types
12:39

A Novel Bayesian Change-point Algorithm for Genome-wide Analysis of Diverse ChIPseq Data Types

Published on: December 10, 2012

11.3K
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: Jun 1, 2025

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
A Novel Bayesian Change-point Algorithm for Genome-wide Analysis of Diverse ChIPseq Data Types
12:39

A Novel Bayesian Change-point Algorithm for Genome-wide Analysis of Diverse ChIPseq Data Types

Published on: December 10, 2012

11.3K
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:

  • 遗传学 遗传学 是一个
  • 生物信息学是一种生物信息学.
  • 人口遗传学 人口遗传学

背景情况:

  • 遗传总结数据对于各种分析是有价值的,但由于未被定位的人口亚结构,它可能会产生偏见.
  • 样本内部和样本之间的异质性在个人层面的遗传数据被汇集成总结统计数据时被掩盖.
  • 现有的方法难以协调多样化的遗传总结数据集,特别是在混合或研究不足的人群中.

研究的目的:

  • 开发和介绍Summix2,一种用于协调遗传总结数据的新方法和软件.
  • 在遗传总结数据集中估计和调整人口亚结构.
  • 提高公开可用的遗传数据的可用性和公平性.

主要方法:

  • Summix2使用一个计算高效的混合模型来描述人口的基层结构.
  • 该方法对子结构进行估计和调整,以实现数据协调.
  • 该软件通过广泛的模拟和对公共遗传数据的应用来验证.

主要成果:

  • Summix2准确地描述了微小规模的人口结构.
  • 该方法识别了遗传数据集中的确定偏差.
  • Summix2可以检测受当地基结构偏差影响的潜在选择区域.

结论:

  • Summix2促进了多样化的遗传总结数据的稳健整合.
  • 该方法提高了功率,并减少了基因分析中的偏差.
  • Summix2通过解决基层结构来促进更公平,更全面的遗传研究.