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

43
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...
43
Mechanistic Models: Compartment Models in Algorithms for Numerical Problem Solving01:29

Mechanistic Models: Compartment Models in Algorithms for Numerical Problem Solving

56
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...
56

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

Narcolepsy with co-occurring epilepsy: diagnostic pitfalls and management strategy.

Therapeutic advances in neurological disorders·2026
Same author

MXene-Based Electrodes for Flexible Supercapacitors: From Material Synthesis to Device Integration.

Materials (Basel, Switzerland)·2026
Same author

Phylo-mobilome Analysis Provide Insights into Transposon Dynamics, Adaptation and Impact on Host Genomes in Solanaceae.

Plant physiology·2026
Same author

Comprehensive cadmium input-output mass balances in two contaminated paddy fields: Implications for soil pollution control and food safety.

Journal of environmental management·2026
Same author

Advances in electrochemical synthesis of urea from CO<sub>2</sub> and nitrogen-containing precursors.

Chemical communications (Cambridge, England)·2026
Same author

Non-Destructive Prediction of NaCl Content in Pork During Ultrasound-Assisted Marination: Multiphysics Simulation and Electrical Impedance Spectroscopy.

Foods (Basel, Switzerland)·2026
Same journal

Interpretable Bayesian Modeling for Multireader Multicase Studies: Addressing Overdispersion and Limited Sample Size in Diagnostic Enhancement Evaluation.

Statistics in medicine·2026
Same journal

Adaptive Sequential Multiple Hypotheses Testing for Concomitant Vaccine Safety Surveillance.

Statistics in medicine·2026
Same journal

Novel Distance Regression for Repeated Outcomes With Missing Data: Applications to Longitudinal and Crossover Studies of Microbiome Beta-Diversity.

Statistics in medicine·2026
Same journal

Optimal Weighted Tests for Replication Studies and the 'Two-Trials Rule' With Multiple Hypotheses.

Statistics in medicine·2026
Same journal

Identifiable Copula-Double-Cox Models: A Fully Parametric Framework for Dependent Right-Censored Survival Data.

Statistics in medicine·2026
Same journal

Moving From Individualized Risk-Based Prevention to Benefit-Based Prevention: Estimating Individualized Life-Years Gained From Prevention Services as a Basis for Eligibility.

Statistics in medicine·2026
查看所有相关文章

相关实验视频

Updated: Jul 10, 2025

Assisted Selection of Biomarkers by Linear Discriminant Analysis Effect Size LEfSe in Microbiome Data
04:57

Assisted Selection of Biomarkers by Linear Discriminant Analysis Effect Size LEfSe in Microbiome Data

Published on: May 16, 2022

15.9K

贝叶斯组成的通用线性模型用于分析微生物组数据.

Li Zhang1, Xinyan Zhang2, Nengjun Yi1

  • 1Department of Biostatistics, University of Alabama at Birmingham, Alabama, USA.

Statistics in medicine
|November 21, 2023
PubMed
概括
此摘要是机器生成的。

这项研究引入了贝叶斯组成的通用线性模型 (BCGLM) 来分析复杂的微生物群数据,通过准确估计微生物对IBD等健康状况的影响来改善疾病预测和个性化医学.

关键词:
贝叶斯模型是贝叶斯模型.美国MCMCMCMCMCMCMCMC组合数据是指组合数据的组成数据.微生物组是一个微生物组.总和为零的限制限制.

更多相关视频

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

4.4K
Compost Microcosms as Microbially Diverse, Natural-like Environments for Microbiome Research in Caenorhabditis elegans
07:19

Compost Microcosms as Microbially Diverse, Natural-like Environments for Microbiome Research in Caenorhabditis elegans

Published on: September 13, 2022

2.2K

相关实验视频

Last Updated: Jul 10, 2025

Assisted Selection of Biomarkers by Linear Discriminant Analysis Effect Size LEfSe in Microbiome Data
04:57

Assisted Selection of Biomarkers by Linear Discriminant Analysis Effect Size LEfSe in Microbiome Data

Published on: May 16, 2022

15.9K
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

4.4K
Compost Microcosms as Microbially Diverse, Natural-like Environments for Microbiome Research in Caenorhabditis elegans
07:19

Compost Microcosms as Microbially Diverse, Natural-like Environments for Microbiome Research in Caenorhabditis elegans

Published on: September 13, 2022

2.2K

科学领域:

  • 微生物组研究的研究.
  • 统计建模 统计建模
  • 生物信息学是一种生物信息学.

背景情况:

  • 人类微生物组显著影响健康和疾病,推动了个性化医学的研究.
  • 传统模型与微生物组数据的组成性质,高维度和特征相似性作斗争.
  • 准确的分析对于将微生物模式与健康结果联系起来至关重要.

研究的目的:

  • 开发先进的统计模型来分析组合微生物组数据.
  • 解决微生物组数据集中高维度和特征相似性的挑战.
  • 改善疾病的预测,并为个性化医学策略提供信息.

主要方法:

  • 提出贝叶斯组成的通用线性模型 (BCGLM).
  • 整合了一个结构化的规则化的马,用于构成系数.
  • 使用马尔科夫链蒙特卡洛 (MCMC) 算法通过R包rstan.
  • 通过先前分配对系数实施了软和至零的限制.

主要成果:

  • 在模拟研究中,BCGLM在现有方法中表现出优越的性能.
  • 在系数估计中获得了更高的准确性,并减少了预测误差.
  • 成功识别了与炎症性肠病 (IBD) 相关的微生物.

结论:

  • BCGLM为分析复杂的微生物组数据提供了一个强大的框架.
  • 该方法增强了对微生物组与宿主相互作用和疾病联系的理解.
  • 为微生物组数据分析和发现提供了一种可重复的方法.