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

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

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...
What is Gene Expression?01:42

What is Gene Expression?

Overview
Gene expression is the process in which DNA directs the synthesis of functional products, that is, proteins. Cells can regulate gene expression at various stages. It allows organisms to generate different cell types and enables cells to adapt to internal and external factors.
Genetic Information Flows from DNA to RNA to Protein
A gene is a stretch of DNA that serves as the blueprint for functional RNAs and proteins. Since DNA is made up of nucleotides and proteins consist of amino...
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...
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)...
Biostatistics: Overview01:20

Biostatistics: Overview

Biostatistics plays a crucial role in understanding and analyzing data in healthcare and biology. Biostatisticians conduct experiments, gather evidence, and draw meaningful conclusions using statistical methods and techniques. Different variables form the foundation of biostatistical analysis, allowing researchers to understand and interpret data effectively. These variables are classified into different types, each serving a specific purpose in statistical analysis.
Discrete variables are...
Statistical Methods for Analyzing Epidemiological Data01:25

Statistical Methods for Analyzing Epidemiological Data

Epidemiological data primarily involves information on specific populations' occurrence, distribution, and determinants of health and diseases. This data is crucial for understanding disease patterns and impacts, aiding public health decision-making and disease prevention strategies. The analysis of epidemiological data employs various statistical methods to interpret health-related data effectively. Here are some commonly used methods:

You might also read

Related Articles

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

Sort by
Same author

Profiling of immunomodulatory anti-cytokine autoantibodies associated with disease heterogeneity in a multiethnic Asian cohort.

Scientific reports·2026
Same author

Early Gut Microbiome Alterations in Mild Cognitive Impairment Reflect Changes in Alzheimer Disease.

Alzheimer disease and associated disorders·2026
Same author

Gut Microbiome Differences Between Early Alzheimer Disease and Idiopathic Normal Pressure Hydrocephalus.

Alzheimer disease and associated disorders·2026
Same author

Transcriptomic rewiring of the JAK-STAT pathway in circulating CD4<sup>+</sup>CLA<sup>+</sup> and CD4<sup>+</sup> naïve T cells from patients with atopic dermatitis and psoriasis.

Frontiers in immunology·2026
Same author

MUUMI: an R package for statistical and network-based meta-analysis for multi-omics data integration.

BMC bioinformatics·2026
Same author

Cannabinoids and skin cancer: Mechanistic insights, therapeutic potential, and translational perspectives.

Experimental and molecular pathology·2026
Same journal

OpenIMC: an open-source platform for analyzing single-cell and spatial proteomics by imaging mass cytometry.

BMC bioinformatics·2026
Same journal

NAP: an open source pipeline for cross-domain microbiome profiling using Nanopore sequencing-derived amplicon data.

BMC bioinformatics·2026
Same journal

SurvGME: an R package for survival analysis with graphical and measurement error models.

BMC bioinformatics·2026
Same journal

SimMapNet: a Bayesian framework for gene regulatory network inference using gene ontology similarities as external hint.

BMC bioinformatics·2026
Same journal

Dual channel drug-drug interactions extraction based on cross attention.

BMC bioinformatics·2026
Same journal

FeSseqdb: a curated sequence-level database and interpretable machine learning framework for identifying iron-sulfur proteins.

BMC bioinformatics·2026
See all related articles

Related Experiment Video

Updated: Jun 12, 2026

Analyzing Multifactorial RNA-Seq Experiments with DiCoExpress
05:22

Analyzing Multifactorial RNA-Seq Experiments with DiCoExpress

Published on: July 29, 2022

Bayesian integrated modeling of expression data: a case study on RhoG.

Rashi Gupta1, Dario Greco, Petri Auvinen

  • 1Department of Mathematics and Statistics, University of Helsinki, Helsinki, Finland. rashi1@live.com

BMC Bioinformatics
|June 3, 2010
PubMed
Summary
This summary is machine-generated.

This study introduces a Bayesian hierarchical model for analyzing DNA microarray data. The integrated approach improves the accuracy of identifying differentially expressed genes by accounting for various error sources.

More Related Videos

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

Related Experiment Videos

Last Updated: Jun 12, 2026

Analyzing Multifactorial RNA-Seq Experiments with DiCoExpress
05:22

Analyzing Multifactorial RNA-Seq Experiments with DiCoExpress

Published on: July 29, 2022

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

Area of Science:

  • Genomics
  • Bioinformatics
  • Statistical Modeling

Background:

  • DNA microarrays enable parallel measurement of gene activity across an organism's transcripts.
  • Microarray data analysis involves sequential steps, each introducing potential errors that accumulate.
  • Accurate assessment of uncertainty in conclusions is crucial despite technological limitations.

Purpose of the Study:

  • To develop an integrated statistical approach for DNA microarray data analysis.
  • To jointly model signal saturation, array effects, dye effects, and differential gene expression.
  • To provide a realistic assessment of uncertainties in microarray analysis conclusions.

Main Methods:

  • A Bayesian hierarchical model was developed for analyzing gene expression data.
  • The model integrates correction for signal saturation, systematic array effects, and dye effects.
  • Inference is based on the full posterior distribution of gene expression indices.

Main Results:

  • The integrated model was applied and tested on two distinct microarray datasets.
  • The approach allows simultaneous consideration of various error components and their impact.
  • Differential gene expression analysis is performed while accounting for multiple sources of error.

Conclusions:

  • The proposed method integrates multiple microarray analysis steps into a single joint analysis.
  • This enables extraction of differential expression information that properly accounts for potential errors.
  • The approach enhances the reliability of conclusions drawn from microarray studies.