Jove
Visualize
Contact Us
JoVE
x logofacebook logolinkedin logoyoutube logo
ABOUT JoVE
OverviewLeadershipBlogJoVE Help Center
AUTHORS
Publishing ProcessEditorial BoardScope & PoliciesPeer ReviewFAQSubmit
LIBRARIANS
TestimonialsSubscriptionsAccessResourcesLibrary Advisory BoardFAQ
RESEARCH
JoVE JournalMethods CollectionsJoVE Encyclopedia of ExperimentsArchive
EDUCATION
JoVE CoreJoVE BusinessJoVE Science EducationJoVE Lab ManualFaculty Resource CenterFaculty Site
Terms & Conditions of Use
Privacy Policy
Policies

Related Concept Videos

Cluster Sampling Method01:20

Cluster Sampling Method

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...
Survival Tree01:19

Survival Tree

Survival trees are a non-parametric method used in survival analysis to model the relationship between a set of covariates and the time until an event of interest occurs, often referred to as the "time-to-event" or "survival time." This method is particularly useful when dealing with censored data, where the event has not occurred for some individuals by the end of the study period, or when the exact time of the event is unknown.
 Building a Survival Tree
Constructing a survival tree begins...
Noncompartmental Analysis: Mean Residence Time01:05

Noncompartmental Analysis: Mean Residence Time

According to statistical moment theory, mean residence time (MRT) is an important measure in pharmacokinetics. MRT can be defined as the expected mean of a probability density function distribution. It provides valuable insights into drug disposition in the body.
After the administration of a drug through intravenous bolus injection, the drug molecules are distributed throughout the body and remain there for varying periods. The MRT represents the average time these drug molecules stay in the...
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...
Time-Series Graph00:54

Time-Series Graph

A time-series graph is a line graph with repeated measurements taken at successive intervals of time. It is also called a time series chart. To construct a time-series graph, one must look at both pieces of a paired data set. The horizontal axis is used to plot the time increments, and the vertical axis is used to plot the values of the variable that one is measuring. By using the axes in this way, each point on the graph will correspond to time and a measured quantity. The points on the graph...
Model Approaches for Pharmacokinetic Data: Compartment Models01:14

Model Approaches for Pharmacokinetic Data: Compartment Models

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...

You might also read

Related Articles

Articles linked to this work by shared authors, journal, and citation graph.

Sort by
Same author

Intensified Total Neoadjuvant Therapy in Patients With Locally Advanced Rectal Cancer: Long-term Results of a Prospective Phase II Study.

Clinical oncology (Royal College of Radiologists (Great Britain))·2024
Same author

Heavy metal concentration according to shrimp species and organ specificity: Monitoring and human risk assessment.

Marine pollution bulletin·2023
Same author

Method Validation for Determination of Thallium by Inductively Coupled Plasma Mass Spectrometry and Monitoring of Various Foods in South Korea.

Molecules (Basel, Switzerland)·2021
Same author

Intensified Total Neoadjuvant Therapy in Patients With Locally Advanced Rectal Cancer: A Phase II Trial.

Clinical oncology (Royal College of Radiologists (Great Britain))·2021
Same author

General practitioners' management of symptomatic uncomplicated diverticular disease of the colon by using rifaximin, a non-adsorbable antibiotic.

European review for medical and pharmacological sciences·2021
Same author

Impact of COVID-19 outbreak on emergency surgery and emergency department admissions: an Italian level 2 emergency department experience.

The British journal of surgery·2020
Same journal

Bayesian variable selection in sample selection models using spike-and-slab priors.

Computational statistics·2026
Same journal

A reduced basis decomposition approach to efficient data collection in pairwise comparison studies.

Computational statistics·2026
Same journal

A latent class pattern mixture model for nonignorable nonresponses in multivariate categorical data.

Computational statistics·2026
Same journal

A stochastic approach to k-nearest neighbors search using a fixed radius method.

Computational statistics·2026
Same journal

Sparse Bayesian multidimensional scaling(s).

Computational statistics·2025
Same journal

Misspecification-robust likelihood-free inference in high dimensions.

Computational statistics·2025
See all related articles

Related Experiment Videos

Bayesian model-based tight clustering for time course data.

Yongsung Joo1, G Casella, J Hobert

  • 1Department of Statistics, Dongguk University, Seoul 100-715, Korea, yongsungjoo@dongguk.edu.

Computational Statistics
|May 10, 2011
PubMed
Summary
This summary is machine-generated.

This study introduces a Bayesian tight clustering method for gene expression data. It effectively identifies small groups of closely related genes for focused biological research.

Related Experiment Videos

Area of Science:

  • Bioinformatics
  • Computational Biology
  • Genomics

Background:

  • Cluster analysis is vital for exploring gene expression data from microarray analyses.
  • Existing algorithms often yield broad clusters, including numerous genes, hindering detailed biological investigation.

Purpose of the Study:

  • To propose a novel Bayesian tight clustering method for time course gene expression data.
  • To address the limitation of current methods that produce overly inclusive clusters.

Main Methods:

  • Development of a Bayesian approach for clustering gene expression data.
  • Implementation of a method to select a small subset of highly similar genes.
  • Construction of tight clusters exclusively from these selected genes.

Main Results:

  • The proposed Bayesian method successfully identifies and forms tight clusters.
  • It effectively isolates small groups of closely related genes from large datasets.

Conclusions:

  • The Bayesian tight clustering method offers a more precise approach to analyzing time course gene expression data.
  • This method enhances the efficiency of identifying key genes for subsequent biological studies.