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

223
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...
223
Analysis Methods of Pharmacokinetic Data: Model and Model-Independent Approaches01:14

Analysis Methods of Pharmacokinetic Data: Model and Model-Independent Approaches

463
Drug disposition in the body is a complex process and can be studied using two major approaches: the model and the model-independent approaches.
The model approach uses mathematical models to describe changes in drug concentration over time. Pharmacokinetic models help characterize drug behavior in patients, predict drug concentration in the body fluids, calculate optimum dosage regimens, and evaluate the risk of toxicity. However, ensuring that the model fits the experimental data accurately...
463
One-Compartment Open Model: Wagner-Nelson and Loo Riegelman Method for ka Estimation01:24

One-Compartment Open Model: Wagner-Nelson and Loo Riegelman Method for ka Estimation

1.1K
This lesson introduces two critical methods in pharmacokinetics, the Wagner-Nelson and Loo-Riegelman methods, used for estimating the absorption rate constant (ka) for drugs administered via non-intravenous routes. The Wagner-Nelson method relates ka to the plasma concentration derived from the slope of a semilog percent unabsorbed time plot. However, it is limited to drugs with one-compartment kinetics and can be impacted by factors like gastrointestinal motility or enzymatic degradation.
On...
1.1K
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-Independent Approaches for Pharmacokinetic Data: Noncompartmental Analysis00:59

Model-Independent Approaches for Pharmacokinetic Data: Noncompartmental Analysis

297
Noncompartmental analyses offer an alternative method for describing drug pharmacokinetics without relying on a specific compartmental model. In this approach, the drug's pharmacokinetics are assumed to be linear, with the terminal phase log-linear. This assumption allows for simplified analysis and interpretation of the drug's behavior in the body.
One important characteristic of noncompartmental analyses is that drug exposure increases proportionally with increasing doses. This...
297
Mechanistic Models: Compartment Models in Individual and Population Analysis01:23

Mechanistic Models: Compartment Models in Individual and Population Analysis

226
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...
226

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

Glycemic response trajectories on metformin monotherapy in real-world diabetes care.

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

Robust ranking of renewable energy alternatives handling uncertainty using novel hesitant bi-fuzzy MEREC-MOORA and Dombi aggregation approach.

Scientific reports·2026
Same author

Patient Preference Phenotypes for Post-operative Anticoagulation After Hip or Knee Replacement: A Cross-sectional Survey Study.

Journal of general internal medicine·2026
Same author

The Impact of Social Vulnerability on Exercise Outcomes: A Longitudinal Study of Physical Function in Older People With HIV.

Journal of the International Association of Providers of AIDS Care·2026
Same author

Special issue: cell and gene causal inference in the design and analysis of gene therapy clinical trials.

Journal of biopharmaceutical statistics·2026
Same author

Mapping the last mile: Micro-stratification for sustained visceral leishmaniasis elimination in Bangladesh.

PLoS neglected tropical diseases·2026
Same journal

Layered social competition coordinates reproductive hierarchy formation in ants.

bioRxiv : the preprint server for biology·2026
Same journal

Combination epigenetic-targeted therapy increases the immunogenicity of poorly immunogenic sarcomas.

bioRxiv : the preprint server for biology·2026
Same journal

Loss of LanC-like proteins delays post-injury regeneration of aging skeletal muscles.

bioRxiv : the preprint server for biology·2026
Same journal

Integrative Transfer Network: Deep Transfer Learning Across Populations and Prediction Targets.

bioRxiv : the preprint server for biology·2026
Same journal

Confidence-supported label-free metabolic imaging with FPhaS phase autofluorescence microscopy.

bioRxiv : the preprint server for biology·2026
Same journal

Sequence-encoded autoinhibition couples mRNA decapping activity to phase separation.

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

相关实验视频

Updated: Jan 9, 2026

Selecting Multiple Biomarker Subsets with Similarly Effective Binary Classification Performances
07:35

Selecting Multiple Biomarker Subsets with Similarly Effective Binary Classification Performances

Published on: October 11, 2018

7.9K

CBKMR:基于Copula的贝叶斯内核机器回归框架,用于在Omics数据中进行最佳标记器检测.

Anirban Chakraborty1, Chloe Mattila1, Debashis Ghosh2

  • 1Department of Public Health Sciences, Medical University of South Carolina, Charleston, South Carolina, USA.

bioRxiv : the preprint server for biology
|December 3, 2025
PubMed
概括
此摘要是机器生成的。

一种新的方法,基于copula的贝叶斯内核机器回归 (CBKMR),可以从omics数据中准确识别细胞类型标记. 这种方法处理复杂的基因相互作用和离散的结果,改进现有的贝叶斯内核机器回归 (BKMR) 和机器学习方法.

关键词:
贝叶斯的变量选择选择是贝叶斯的.批量产品有大量的消费.在 GKMR 中,GKMR 是斯的合体是高斯的合体.最接近的邻居GP近似值.这就是scRNA-seqq.

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

11.7K
Author Spotlight: An Efficient and Robust Software for Automated Fusion of Multiple Preclinical Imaging Modalities
07:13

Author Spotlight: An Efficient and Robust Software for Automated Fusion of Multiple Preclinical Imaging Modalities

Published on: October 27, 2023

1.6K

相关实验视频

Last Updated: Jan 9, 2026

Selecting Multiple Biomarker Subsets with Similarly Effective Binary Classification Performances
07:35

Selecting Multiple Biomarker Subsets with Similarly Effective Binary Classification Performances

Published on: October 11, 2018

7.9K
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

11.7K
Author Spotlight: An Efficient and Robust Software for Automated Fusion of Multiple Preclinical Imaging Modalities
07:13

Author Spotlight: An Efficient and Robust Software for Automated Fusion of Multiple Preclinical Imaging Modalities

Published on: October 27, 2023

1.6K

科学领域:

  • 计算生物学是一种计算生物学.
  • 统计遗传学 统计遗传学
  • 生物信息学是一种生物信息学.

背景情况:

  • 高通量欧米克技术提供了全面的分子数据,但很难识别细胞类型或疾病状态的有效标记器集.
  • 现有的方法,如单变量测试错过基因依赖性,而机器学习方法往往缺乏特征选择和不确定性量化.
  • 贝叶斯核心机器回归 (BKMR) 框架捕捉了非线性和相互作用,但与离散结果扎.

研究的目的:

  • 开发一个改进的贝叶斯内核机器回归模型,用于从omics数据中识别标记集,特别是对于像细胞类型这样的离散结果.
  • 为了提高大型单细胞RNA测序 (scRNA-seq) 数据集的模型的可扩展性.
  • 将拟议模型的性能与模拟和现实应用中的现有方法进行比较.

主要方法:

  • 提出了一个基于copula的贝叶斯内核机器回归 (CBKMR) 模型,使用离散边缘值和高斯对依赖性的copula.
  • 引入了基于GP的近邻变体 (NNCBKMR) 以提高计算效率,将复杂性从O ((N ^ 3) 降低到近线性.
  • 通过模拟研究和应用到scRNA-seq数据集来评估CBKMR.

主要成果:

  • 与标准BKMR和整体机器学习方法相比,CBKMR在捕获非线性效应和选择标记器方面表现出卓越的表现.
  • 对于大型数据集来说,NNCBKMR变体实现了显著的计算加速.
  • CBKMR确定了简洁的基因标记面板,与专家定义的签名相符,提供后期不确定性估计.

结论:

  • CBKMR提供了一个强大的和可扩展的框架,用于在高维的奥米克数据中发现标记物,特别是对于离散的结果.
  • 该模型通过整合非线性,相互作用和不确定性量化来改进现有的BKMR配方和机器学习技术.
  • CBKMR促进了更可靠和可解释的细胞类型和疾病相关标记物的识别.