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

Sensitivity, Specificity, and Predicted Value01:13

Sensitivity, Specificity, and Predicted Value

1.3K
In healthcare diagnostics, laboratory tests play a crucial role in identifying and diagnosing a wide range of medical conditions. However, interpreting test results is not always straightforward. An abnormal test result does not always confirm the presence of a disease, just as a normal result does not guarantee its absence. To assess the reliability of these diagnostic tools, healthcare practitioners rely on two key statistical indicators: sensitivity and specificity.
Sensitivity is the...
1.3K
Cell Specific Gene Expression01:58

Cell Specific Gene Expression

16.5K
Multicellular organisms contain a variety of structurally and functionally distinct cell types, but the DNA in all the cells originated from the same parent cells. The differences in the cells can be attributed to the differential gene expression. Liver cells, whose functions include detoxification of blood, production of bile to metabolize fats, and synthesis of proteins essential for metabolism, must express a specific set of genes to perform their functions. Gene expression also varies with...
16.5K
Cell Specific Gene Expression01:58

Cell Specific Gene Expression

5.6K
5.6K
What are Estimates?01:06

What are Estimates?

8.8K
It isn't easy to measure a parameter such as the mean height or the mean weight of a population. So, we draw samples from the population and calculate the mean height or mean weight of the individuals in the sample. This sample data acts as a representative measure of the population parameter. These sample statistics are known as estimates. 
The estimate for the mean of a sample is denoted by ͞x, whereas the mean of the population is designated as μ. Further, parameters such...
8.8K
Estimation of k and VD of Aminoglycosides01:20

Estimation of k and VD of Aminoglycosides

232
Aminoglycosides are a class of antibiotics used to treat various bacterial infections. Clinicians must determine the elimination rate constant (k) and volume of distribution (VD) to optimize therapeutic efficacy and minimize toxicity. The k value represents the rate at which the drug is removed from the body, and the VD reflects the degree to which the drug distributes into body tissues. Accurately estimating these parameters allows healthcare professionals to tailor drug dosing to individual...
232
One-Compartment Open Model for IV Bolus Administration: Estimation of Clearance00:56

One-Compartment Open Model for IV Bolus Administration: Estimation of Clearance

355
Clearance is a key pharmacokinetic parameter that quantifies the volume of body fluid from which a drug is entirely removed within a specific time frame. It is crucial in assessing how a drug is eliminated from the body and has critical clinical applications.
In the one-compartment open model for intravenous (IV) bolus administration, clearance is estimated by dividing the elimination rate by the plasma drug concentration. This equation leverages the elimination rate constant and the apparent...
355

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

The GR of CA1 is involved in anxiety-like behavior induced by 0.8/2.65 GHz dual-frequency electromagnetic radiation.

Frontiers in molecular neuroscience·2026
Same author

P2Y12-AMPKα2 signaling contributes to cardiomyocyte senescence in doxorubicin-induced heart failure.

Molecular and cellular biochemistry·2026
Same author

POISE: Spectral Inference of Parent-of-Origin Effects in Unlabeled Genomic Data.

bioRxiv : the preprint server for biology·2026
Same author

CondFoodGen: A Conditional Two-Stream Network for Controllable Food Image Generation.

IEEE transactions on image processing : a publication of the IEEE Signal Processing Society·2026
Same author

Surface Gradient Sn Doping of Copper Enables Adsorption-Controlled Electrohydrogenation of Biomass-Derived Aldehydes.

ACS nano·2026
Same author

Antioxidant lipid nanoparticles enhance mRNA stability for regeneration therapy and gene editing.

Nature communications·2026

相关实验视频

Updated: Jan 29, 2026

Patient-specific Modeling of the Heart: Estimation of Ventricular Fiber Orientations
12:09

Patient-specific Modeling of the Heart: Estimation of Ventricular Fiber Orientations

Published on: January 8, 2013

14.1K

贝叶斯CNV:贝叶斯的层次模型,用于对细胞自由DNA的敏感和特定拷贝数的估计.

Austin Talbot1, Alex Kotlar1, Lavanya Rishishwar1

  • 1Pillar Biosciences Inc., Natick, MA 01760, USA.

Diagnostics (Basel, Switzerland)
|January 28, 2026
PubMed
概括

贝叶斯CNV使用一种新的贝叶斯模型准确地检测无细胞DNA (cfDNA) 中的副本数变异 (CNV). 这种方法为目标测序面板提供了更好的灵敏度和特异性,提高了诊断可靠性.

关键词:
贝叶斯的等级模型是贝叶斯的等级模型.副本号码的变化 副本号的变化液体活检活检液体活检这是下一代测序.机器学习的概率学.有针对性的测序.热力学集成热力学集成

更多相关视频

Multimodal Hierarchical Imaging of Serial Sections for Finding Specific Cellular Targets within Large Volumes
11:19

Multimodal Hierarchical Imaging of Serial Sections for Finding Specific Cellular Targets within Large Volumes

Published on: March 20, 2018

10.9K
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

相关实验视频

Last Updated: Jan 29, 2026

Patient-specific Modeling of the Heart: Estimation of Ventricular Fiber Orientations
12:09

Patient-specific Modeling of the Heart: Estimation of Ventricular Fiber Orientations

Published on: January 8, 2013

14.1K
Multimodal Hierarchical Imaging of Serial Sections for Finding Specific Cellular Targets within Large Volumes
11:19

Multimodal Hierarchical Imaging of Serial Sections for Finding Specific Cellular Targets within Large Volumes

Published on: March 20, 2018

10.9K
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

科学领域:

  • 基因组学就是基因组学.
  • 生物信息学是一种生物信息学.
  • 计算生物学 计算生物学

背景情况:

  • 在下一代测序 (NGS) 数据中检测副本数变异 (CNV) 是一个挑战,特别是低信号无细胞DNA (cfDNA) 和向面板.
  • 在cfDNA测序中的高噪音水平使准确的CNV识别复杂化.

研究的目的:

  • 开发和验证贝叶斯CNV,这是一个贝叶斯的层次模型,用于从目标测序数据中对基因水平拷贝比率进行可靠的估计.
  • 为cfDNA分析提供准确的CNV调用,不确定性量化和基于证据的质量控制 (QC) 度量.

主要方法:

  • 实现了贝叶斯的层次模型,用于基因层次的复制比率估计,使用目标的安普利康读取深度.
  • 利用热力学集成可靠地估计QC的边际日志概率.
  • 在OncoReveal Core Lbx面板上使用已知CNV的基准样本对IonCopy和DeviCNV进行基准BayesCNV.

主要成果:

  • 贝叶斯CNV获得了0.87的灵敏度和0.996的特异性,超过了竞争对手的方法.
  • 边际日志概率有效地区分了FFPE数据集中的退化和高质量样本,超过了传统的QC指标.
  • 证明了准确和可解释的基因水平CNV估计与不确定性量化.

结论:

  • 贝叶斯CNV提供了一个强大而准确的解决方案,用于在向cfDNA测序中检测CNV.
  • 集成的质量控制指标提高了CNV调用具有挑战性的低输入样本的可靠性.
  • 该方法提供可解释的结果和不确定性量化,对于临床应用至关重要.