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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

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 squares (OLS)...
Model Approaches for Pharmacokinetic Data: Distributed Parameter Models01:06

Model Approaches for Pharmacokinetic Data: Distributed Parameter Models

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...
Modern Molecular Taxonomy01:29

Modern Molecular Taxonomy

Advancements in molecular biology have revolutionized the identification and characterization of bacteria, with multiple methods leveraging DNA sequencing for enhanced precision. As sequencing technologies improve and costs decline, these approaches are increasingly used in clinical, environmental, and evolutionary studies.Multilocus Sequence Typing (MLST) examines several housekeeping genes, essential chromosomal genes encoding cellular functions, to distinguish strains. Approximately...
Methods to Assess Microbial Populations01:30

Methods to Assess Microbial Populations

Assessing microbial populations is crucial for understanding microbial roles in health, ecology, and industry. Various complementary techniques—both culture-based and molecular—enable detailed analysis of microbial abundance, diversity, and function.Viable Plate CountThe viable plate count is a traditional culture-based method used to estimate the number of living microbes in a sample. After serial dilution, the sample is spread onto nutrient agar plates. Each viable cell forms a visible...
Methods to Assess Microbial Communities01:19

Methods to Assess Microbial Communities

Microbial communities, comprising bacteria, archaea, and eukaryotic microorganisms, inhabit diverse ecosystems and play crucial roles in environmental and biological processes. Their diversity is defined by three main parameters: species richness (the number of distinct species), species abundance (the relative quantity of each species), and species evenness (how uniformly individual species are distributed in various locations). These factors together shape the structure and ecological balance...
Introduction to the Human Microbiota01:22

Introduction to the Human Microbiota

Microorganisms colonize various regions of the human body, including the mouth, nasal passages, throat, stomach, intestines, urogenital tract, and skin. The total number of microbial cells is estimated to range from 10¹³ to 10¹⁴—comparable to, or exceeding, the number of human somatic cells. This host–microbiome relationship has led to the conceptualization of humans as supraorganisms, wherein microbial communities perform vital roles in development, immunity, and disease...

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相关实验视频

Updated: Jun 16, 2026

Assembly and Tracking of Microbial Community Development within a Microwell Array Platform
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Assembly and Tracking of Microbial Community Development within a Microwell Array Platform

Published on: June 6, 2017

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一个广义的贝叶斯斯斯托哈斯基块模型,用于微生物群体检测.

Kevin C Lutz1, Michael L Neugent2, Tejasv Bedi3

  • 1Peter O'Donnell Jr. School of Public Health, The University of Texas Southwestern Medical Center, Dallas, Texas.

Statistics in medicine
|January 24, 2025
PubMed
概括
此摘要是机器生成的。

我们开发了一种新的贝叶斯模型来分析微生物共发生网络. 这种方法可以改善社区对复杂微生物组数据的检测,为疾病研究提供了一个新的工具.

关键词:
贝叶斯的随机区块模型是贝叶斯的.马尔科夫随机场是一个随机场.社区检测 社区检测微生物组共发生网络.这是一个分类学树.

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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:

  • 下一代测序加速了微生物组研究,增加了定量网络分析.
  • 了解微生物群体社区结构是疾病研究的关键.
  • 超基因组数据存在挑战:高维度,组成性质,深度不均,过度分散和零通货膨胀.

研究的目的:

  • 为微生物共发生网络分析和社区检测提出一种新的统计方法.
  • 为了应对分析高维,组成微生物组数据的挑战.
  • 利用分类学信息来改善微生物群体社区结构推断.

主要方法:

  • 开发了一种针对微生物组数据量身定制的贝叶斯概括的随机区块模型.
  • 应用修改的中心日志比率转换到微生物群丰度数据.
  • 使用马尔科夫随机字段的纳入分类树信息.
  • 使用马尔科夫链蒙特卡洛采样用于联合参数推断.

主要成果:

  • 拟议的模型在模拟研究中表现优于竞争对手的方法,即使没有有信息的分类树数据.
  • 成功地将该方法应用于绝经后妇女的真实尿道微生物群数据集.
  • 首次揭示了绝经后妇女尿道微生物组共发生网络结构.

结论:

  • 贝叶斯概括的随机区块模型为微生物群体检测提供了一个强大的方法.
  • 这种统计方法为先进的微生物组研究提供了有价值的新工具.
  • 这些发现为了解尿道微生物群在绝经后健康中的作用开辟了新的途径.