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

DNA Microarrays02:34

DNA Microarrays

Microarrays are high-throughput and relatively inexpensive assays that can be automated to analyze large quantities of data at a time. They are used in genome-wide studies to compare gene or protein expression under two varied conditions, such as healthy and diseased states. Microarrays consist of glass or silica slides on which probe molecules are covalently attached through surface functionalization. Most commonly, the slides are prepared through the chemisorption of silanes to silica...
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...
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...

You might also read

Related Articles

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

Sort by
Same author

Multi-omics network inference with a Gaussian copula model.

BMC bioinformatics·2026
Same author

Species-specific characteristics of variables associated with milk lipolysis induced by a feed restriction in cows, ewes and goats.

Journal of dairy science·2026
Same author

Entorhinal Cortex Wolframin-1-expressing neurons propagate tau to CA1 neurons and impair hippocampal memory.

bioRxiv : the preprint server for biology·2026
Same author

De-escalation strategies in the treatment of oral squamous cell carcinoma: A cross-sectional study in oral and maxillofacial surgery in Germany, Austria, and Switzerland.

Journal of cranio-maxillo-facial surgery : official publication of the European Association for Cranio-Maxillo-Facial Surgery·2026
Same author

Guiding eQTL mapping and genomic prediction of gene expression in three pig breeds with tissue-specific epigenetic annotations from early development.

Genomics·2025
Same author

Sex specific correction of maternal inflammation-induced behavioral abnormalities by the inhibition of colony-stimulating factor 1 receptor.

Brain, behavior, and immunity·2025
Same journal

Balanced mediated pathway detection in genomic data.

Statistical applications in genetics and molecular biology·2026
Same journal

Annealed variational mixtures for disease subtyping and biomarker discovery.

Statistical applications in genetics and molecular biology·2026
Same journal

Performance of the permutation test approach with base calling errors for detecting changes in variant allele frequencies in ctDNA for a single patient.

Statistical applications in genetics and molecular biology·2026
Same journal

BLOG: Bayesian longitudinal omics with group constraints.

Statistical applications in genetics and molecular biology·2026
Same journal

AI-driven risk prediction and categorization in cystic fibrosis leveraging AttentiveLSTM and Fox Wolf Optimizer.

Statistical applications in genetics and molecular biology·2026
Same journal

Perfect collinearity not created equal: measuring and visualizing the severity of multi-collinearity of modern omics data.

Statistical applications in genetics and molecular biology·2026
See all related articles

Related Experiment Video

Updated: Jun 15, 2026

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

An empirical Bayesian method for estimating biological networks from temporal microarray data.

Andrea Rau1, Florence Jaffrézic, Jean-Louis Foulley

  • 1Purdue University, INRA AgroParisTech, USA. arau@stat.purdue.edu

Statistical Applications in Genetics and Molecular Biology
|March 4, 2010
PubMed
Summary
This summary is machine-generated.

We developed an efficient method to infer gene regulatory networks using Dynamic Bayesian Networks. Our approach accurately models gene expression data, offering a faster alternative for biological network analysis.

More Related Videos

JUMPn: A Streamlined Application for Protein Co-Expression Clustering and Network Analysis in Proteomics
07:28

JUMPn: A Streamlined Application for Protein Co-Expression Clustering and Network Analysis in Proteomics

Published on: October 19, 2021

Inherent Dynamics Visualizer, an Interactive Application for Evaluating and Visualizing Outputs from a Gene Regulatory Network Inference Pipeline
10:44

Inherent Dynamics Visualizer, an Interactive Application for Evaluating and Visualizing Outputs from a Gene Regulatory Network Inference Pipeline

Published on: December 7, 2021

Related Experiment Videos

Last Updated: Jun 15, 2026

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

JUMPn: A Streamlined Application for Protein Co-Expression Clustering and Network Analysis in Proteomics
07:28

JUMPn: A Streamlined Application for Protein Co-Expression Clustering and Network Analysis in Proteomics

Published on: October 19, 2021

Inherent Dynamics Visualizer, an Interactive Application for Evaluating and Visualizing Outputs from a Gene Regulatory Network Inference Pipeline
10:44

Inherent Dynamics Visualizer, an Interactive Application for Evaluating and Visualizing Outputs from a Gene Regulatory Network Inference Pipeline

Published on: December 7, 2021

Area of Science:

  • Computational Biology
  • Systems Biology
  • Genomics

Background:

  • Gene regulatory networks (GRNs) control cellular functions through complex gene interactions.
  • Inferring GRN topology from gene expression data is challenging due to high dimensionality and limited replicates.
  • Dynamic Bayesian Networks (DBNs) are effective for modeling time-series gene expression data.

Purpose of the Study:

  • To develop an efficient computational method for inferring gene regulatory network topology.
  • To improve the accuracy and speed of network inference from gene expression time-series data.
  • To provide a publicly available tool for researchers in systems biology.

Main Methods:

  • An iterative empirical Bayesian procedure was developed.
  • A Kalman filter was integrated to estimate posterior distributions of network parameters.
  • The method was compared against existing approaches using simulated and real microarray data.

Main Results:

  • The proposed method demonstrated comparable performance to existing methods on both simulated and real data.
  • The new approach significantly reduced computational time.
  • The method effectively estimates network parameters from high-dimensional gene expression data.

Conclusions:

  • The developed iterative empirical Bayesian method with a Kalman filter is an efficient and accurate tool for inferring gene regulatory networks.
  • This method offers a computationally advantageous alternative for analyzing microarray time-series data.
  • The R package ebdbNet provides public access to the implemented code for broader research application.