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

Model Approaches for Pharmacokinetic Data: Distributed Parameter Models01:06

Model Approaches for Pharmacokinetic Data: Distributed Parameter Models

218
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...
218
Cluster Sampling Method01:20

Cluster Sampling Method

13.9K
Appropriate sampling methods ensure 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 cluster sample, divide the population into clusters (groups) and then randomly select some of the clusters. All the members from these clusters are in the cluster sample. For example, if you randomly sample four departments from your...
13.9K
Model Approaches for Pharmacokinetic Data: Compartment Models01:14

Model Approaches for Pharmacokinetic Data: Compartment Models

496
Compartmental analysis is a widely adopted approach to characterizing drug pharmacokinetics. It uses compartment models that conceptualize the body as a collection of reversibly communicating compartments, each representing a group of tissues exhibiting similar drug distribution characteristics. The movement rate of the drug between these compartments is typically described by first-order kinetics.
Two primary types of compartment models are recognized: mammillary and catenary. The more...
496
Mechanistic Models: Compartment Models in Individual and Population Analysis01:23

Mechanistic Models: Compartment Models in Individual and Population Analysis

221
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...
221
Distributions to Estimate Population Parameter01:26

Distributions to Estimate Population Parameter

5.0K
The accurate values of population parameters such as population proportion, population mean, and population standard deviation (or variance) are usually unknown. These are fixed values that can only be estimated from the data collected from the samples. The estimates of each of these parameters are sample proportion, the sample mean, and sample standard deviation (or variance). To obtain the values of these sample statistics, data are required that have particular distribution and central...
5.0K
Clearance Models: Noncompartmental Models01:17

Clearance Models: Noncompartmental Models

227
Clearance is a pharmacokinetic parameter traditionally defined by compartment models, signifying the rate at which a drug is expelled from the body. However, a noncompartmental model offers an alternative method for assessing clearance, primarily employing empirical data obtained after administering a single drug dose.
The noncompartmental approach capitalizes on extensive sampling data, correlating the volume of distribution to systemic exposure and the administered dosage. This method enables...
227

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

A Bayesian functional concurrent zero-inflated Dirichlet-multinomial regression model with application to infant microbiome.

Biostatistics (Oxford, England)·2026
Same author

Melding wildlife surveys to improve conservation inference.

Biometrics·2023
Same author

A Bayesian joint model for compositional mediation effect selection in microbiome data.

Statistics in medicine·2023
Same author

A Bayesian zero-inflated Dirichlet-multinomial regression model for multivariate compositional count data.

Biometrics·2023
Same author

Infinite hidden Markov models for multiple multivariate time series with missing data.

Biometrics·2022
Same author

Bayesian continuous-time hidden Markov models with covariate selection for intensive longitudinal data with measurement error.

Psychological methods·2021

相关实验视频

Updated: Jan 6, 2026

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

17.2K

一个贝叶斯半参数混合模型,用于集群零膨胀微生物组数据.

Suppapat Korsurat1, Matthew D Koslovsky1

  • 1Department of Statistics, Colorado State University, Fort Collins, CO, 80523, United States.

Biometrics
|September 23, 2025
PubMed
概括
此摘要是机器生成的。

这项研究引入了一种新的贝叶斯模型来分析人类微生物组数据. 该方法有效地识别了微生物组成中的子组,这对于了解健康和疾病联系至关重要.

关键词:
组合数据是指组合数据的组成数据.肠道类型 肠道类型混合模型的混合模型.多变量计数数据 数据多变量计数数据

更多相关视频

A Data-Driven Approach to Quantifying Immune States in Sepsis
07:42

A Data-Driven Approach to Quantifying Immune States in Sepsis

Published on: February 7, 2025

457
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

4.8K

相关实验视频

Last Updated: Jan 6, 2026

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

17.2K
A Data-Driven Approach to Quantifying Immune States in Sepsis
07:42

A Data-Driven Approach to Quantifying Immune States in Sepsis

Published on: February 7, 2025

457
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

4.8K

科学领域:

  • 微生物学 微生物学
  • 生物信息学是一种生物信息学.
  • 统计建模 统计建模

背景情况:

  • 人类微生物组研究旨在将微生物组成与健康和疾病联系起来.
  • 现有的集群方法与复杂的零膨胀微生物组数据作斗争,并且通常需要预先指定集群的数量.
  • 这种限制可以在识别有意义的子组时引入偏见.

研究的目的:

  • 为微生物组数据开发一个新的贝叶斯半参数混合模型框架.
  • 同时确定集群的数量并将个人分配给它们.
  • 为应对零通胀和微生物组数据的组成性质所带来的挑战.

主要方法:

  • 开发了一个贝叶斯半参数混合模型.
  • 该模型是为零膨胀的多变量组成计数数据设计的.
  • 通过模拟来评估性能,并应用于现实世界肠道微生物群数据集.

主要成果:

  • 拟议的贝叶斯框架有效地识别了微生物组数据中的子组.
  • 该方法准确地确定了集群的数量,没有事先的假设.
  • 与现有方法相比,模拟证实了较优的集群性能,特别是使用零膨胀数据.

结论:

  • 新的贝叶斯建模框架为微生物组子组发现提供了一个强大的方法.
  • 准确识别微生物子组可以促进对肠道微生物组成的理解,例如在腹等疾病中.
  • 这种方法通过适应数据复杂性和同时学习集群数量来改善推断.