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

Protein Networks02:26

Protein Networks

4.2K
An organism can have thousands of different proteins, and these proteins must cooperate to ensure the health of an organism. Proteins bind to other proteins and form complexes to carry out their functions. Many proteins interact with multiple other proteins creating a complex network of protein interactions.
These interactions can be represented through maps depicting protein-protein interaction networks, represented as nodes and edges. Nodes are circles that are representative of a protein,...
4.2K
Protein Networks02:26

Protein Networks

2.5K
2.5K
Interactions Between Signaling Pathways01:19

Interactions Between Signaling Pathways

6.7K
Signaling cascades usually lack linearity. Multiple pathways interact and regulate one another, allowing cells to integrate and respond to diverse environmental stimuli.
Convergence and divergence, and cross-talk between signaling pathways
Two distinct signaling pathways can converge on a single functional unit, which may either be a single protein or a complex of proteins. The response is either functionally distinct or synergistic between the two pathways but different from the response...
6.7K
Genetic Screens02:46

Genetic Screens

5.2K
Genetic screens are tools used to identify genes and mutations responsible for phenotypes of interest. Genetic screens help identify individuals or a group of people at risk of developing  genetic diseases and help them with early intervention, targeted therapy, and reproductive options.
Forward genetic screens
Forward or “classical” genetic screens involve creating random mutations in an organism’s DNA using radiation, mutagens, or insertion of additional bases, which...
5.2K
Pharmacokinetic Models: Comparison and Selection Criterion01:26

Pharmacokinetic Models: Comparison and Selection Criterion

178
Physiological and compartmental models are valuable tools used in studying biological systems. These models rely on differential equations to maintain mass balance within the system, ensuring an accurate representation of the dynamic processes at play.
Physiological models take a detailed approach by considering specific molecular processes. They can predict drug distribution, metabolism, and elimination changes, providing a comprehensive understanding of how drugs interact with the body.
178
Model Approaches for Pharmacokinetic Data: Distributed Parameter Models01:06

Model Approaches for Pharmacokinetic Data: Distributed Parameter Models

149
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...
149

You might also read

Related Articles

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

Sort by
Same author

Unveiling inflammatory and prehypertrophic cell populations as key contributors to knee cartilage degeneration in osteoarthritis using multi-omics data integration.

Annals of the rheumatic diseases·2024
Same author

Inference of Gene Regulatory Networks Using Bayesian Nonparametric Regression and Topology Information.

Computational and mathematical methods in medicine·2017
Same author

Detecting Susceptibility to Breast Cancer with SNP-SNP Interaction Using BPSOHS and Emotional Neural Networks.

BioMed research international·2016
Same journal

Correction to "Mathematical Modelling of COVID-19 Transmission in Kenya: A Model with Reinfection Transmission Mechanism".

Computational and mathematical methods in medicine·2025
Same journal

RETRACTION: Ligustrazine Inhibits Lung Phosphodiesterase Activity in a Rat Model of Allergic Asthma.

Computational and mathematical methods in medicine·2025
Same journal

RETRACTION: Delivery of miR-224-5p by Exosomes from Cancer-Associated Fibroblasts Potentiates Progression of Clear Cell Renal Cell Carcinoma.

Computational and mathematical methods in medicine·2025
Same journal

RETRACTION: Empirical Analysis of the Nursing Effect of Intelligent Medical Internet of Things in Postoperative Osteoarthritis.

Computational and mathematical methods in medicine·2025
Same journal

RETRACTION: Evaluation and Analysis of the Intervention Effect of Systematic Parent Training Based on Computational Intelligence on Child Autism.

Computational and mathematical methods in medicine·2024
Same journal

RETRACTION: Humanistic Spirit Training of Medical Students Based on Multisource Medical Data Fusion.

Computational and mathematical methods in medicine·2024
See all related articles

Related Experiment Video

Updated: Oct 24, 2025

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

2.4K

Bayesian Gene Selection Based on Pathway Information and Network-Constrained Regularization.

Ming Cao1,2, Yue Fan1, Qinke Peng1

  • 1Faculty of Electronic and Information Engineering, Xi'an Jiaotong University, Xi'an 710049, China.

Computational and Mathematical Methods in Medicine
|August 16, 2021
PubMed
Summary
This summary is machine-generated.

Identifying critical gene biomarkers is crucial for disease management. This study introduces a novel Bayesian network-constrained method that effectively uses pathway structure to improve gene selection accuracy over existing approaches.

More Related Videos

A Pathway Association Study Tool for GWAS Analyses of Metabolic Pathway Information
05:01

A Pathway Association Study Tool for GWAS Analyses of Metabolic Pathway Information

Published on: July 1, 2020

3.4K
Sample Preparation and Analysis of RNASeq-based Gene Expression Data from Zebrafish
11:42

Sample Preparation and Analysis of RNASeq-based Gene Expression Data from Zebrafish

Published on: October 27, 2017

11.1K

Related Experiment Videos

Last Updated: Oct 24, 2025

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

2.4K
A Pathway Association Study Tool for GWAS Analyses of Metabolic Pathway Information
05:01

A Pathway Association Study Tool for GWAS Analyses of Metabolic Pathway Information

Published on: July 1, 2020

3.4K
Sample Preparation and Analysis of RNASeq-based Gene Expression Data from Zebrafish
11:42

Sample Preparation and Analysis of RNASeq-based Gene Expression Data from Zebrafish

Published on: October 27, 2017

11.1K

Area of Science:

  • Bioinformatics
  • Computational Biology
  • Genomics

Background:

  • High-throughput data allows simultaneous gene expression analysis.
  • Identifying discriminative genes (biomarkers) is vital for disease diagnosis, prognosis, and therapy.
  • Existing pathway-based methods often overlook pathway structural information.

Purpose of the Study:

  • To propose a Bayesian gene selection method incorporating pathway structural information.
  • To improve the precision of biomarker identification using network-constrained regularization.
  • To leverage pathway topology for more effective gene selection.

Main Methods:

  • Developed a Bayesian gene selection approach with network-constrained regularization.
  • Incorporated pathway structural information as priors in the Bayesian model.
  • Utilized conjugated priors for efficient parameter estimation via Gibbs sampling.
  • Applied and compared the method on six microarray datasets.

Main Results:

  • The proposed network-constrained Bayesian method demonstrated superior performance compared to Bayesian Lasso, Elastic Net, and Fused Lasso.
  • Incorporating pathway structural information significantly improved gene selection results.
  • The method effectively identified discriminative genes using integrated pathway topology.

Conclusions:

  • The proposed Bayesian network-constrained regularization method is effective for biomarker discovery.
  • Pathway structural information enhances the accuracy and reliability of gene selection.
  • This approach offers a promising tool for precise biomarker identification in complex diseases.