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

Epistasis Analysis01:09

Epistasis Analysis

Although Mendel chose seven unrelated traits in peas to study gene segregation, most traits involve multiple gene interactions that create a spectrum of phenotypes. When the interaction of various genes or alleles at different locations influences a phenotype, this is called epistasis. Epistasis often involves one gene masking or interfering with the expression of another (antagonistic epistasis). Epistasis often occurs when different genes are part of the same biochemical pathway. The...
Protein Networks02:26

Protein Networks

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,...
Gene-Environment Interactions01:20

Gene-Environment Interactions

Gene expression is a dynamic process that is significantly influenced by environmental factors. This interaction underlies the complex nature of biological development and the phenotypic differences observed among individuals, even among those with identical genetic makeups. Factors such as radiation, temperature, behavior, nutrition, and stress play pivotal roles in determining how genes are expressed. The concept of the reaction range is central to understanding this interaction. It posits...
What is Gene Expression?01:36

What is Gene Expression?

A gene is a stretch of DNA that serves as the blueprint for functional RNAs and proteins. Since DNA is comprised  of nucleotides and proteins are comprised of amino acids, a mediator is required to convert the information encoded in DNA into proteins. This mediator is the messenger RNA (mRNA). mRNA copies the blueprint from DNA by a process called transcription. In eukaryotes, transcription occurs in the nucleus by complementary base-pairing with the DNA template. The mRNA is then processed and...
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...
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...

You might also read

Related Articles

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

Sort by
Same author

Atp6i deficient mouse model uncovers transforming growth factor-β1 /Smad2/3 as a key signaling pathway regulating odontoblast differentiation and tooth root formation.

International journal of oral science·2023
Same author

Identifiability in Functional Connectivity May Unintentionally Inflate Prediction Results.

ArXiv·2023
Same author

A Mendelian randomization study for drug repurposing reveals bezafibrate and fenofibric acid as potential osteoporosis treatments.

Frontiers in pharmacology·2023
Same author

Identification of potential genetic causal variants for obesity-related traits using statistical fine mapping.

Molecular genetics and genomics : MGG·2023
Same author

ST-V-Net: incorporating shape prior into convolutional neural networks for proximal femur segmentation.

Complex & intelligent systems·2023
Same author

Enteric nervous system damage caused by abnormal intestinal butyrate metabolism may lead to functional constipation.

Frontiers in microbiology·2023
Same journal

Analysis of strength degradation of coal and rock masses and stability of mined areas under long term immersion environment.

PloS one·2026
Same journal

Biogenic Silver-Selenium nanocomposite with anticancer activity and potent efficacy against vancomycin-resistant Staphylococcus aureus.

PloS one·2026
Same journal

Preparation and physicochemical characterization of a biodegradable chitosan/carboxymethyl cellulose hydrogel synthesized in NaOH/urea medium.

PloS one·2026
Same journal

Action-guilt, survivor-guilt, and depression in combat-related PTSD.

PloS one·2026
Same journal

Explainable machine learning for predicting activities of daily living at discharge in stroke patients: A retrospective study using SHAP interpretability.

PloS one·2026
Same journal

Deep learning based two-way feature depiction model for brain tumor detection.

PloS one·2026
See all related articles

Related Experiment Video

Updated: Jun 18, 2026

Analyzing Multifactorial RNA-Seq Experiments with DiCoExpress
05:22

Analyzing Multifactorial RNA-Seq Experiments with DiCoExpress

Published on: July 29, 2022

Identifying gene interaction enrichment for gene expression data.

Jigang Zhang1, Jian Li, Hong-Wen Deng

  • 1School of Medicine, University of Missouri-Kansas City, Kansas City, Missouri, United States of America.

Plos One
|December 4, 2009
PubMed
Summary
This summary is machine-generated.

This study introduces a novel gene interaction enrichment analysis method. It identifies gene interactions within pathways, offering new insights into complex diseases beyond traditional gene set analysis.

More Related Videos

High-Throughput Transcriptome Analysis for Investigating Host-Pathogen Interactions
14:58

High-Throughput Transcriptome Analysis for Investigating Host-Pathogen Interactions

Published on: March 5, 2022

In Silico Identification and Characterization of circRNAs During Host-Pathogen Interactions
10:27

In Silico Identification and Characterization of circRNAs During Host-Pathogen Interactions

Published on: October 21, 2022

Related Experiment Videos

Last Updated: Jun 18, 2026

Analyzing Multifactorial RNA-Seq Experiments with DiCoExpress
05:22

Analyzing Multifactorial RNA-Seq Experiments with DiCoExpress

Published on: July 29, 2022

High-Throughput Transcriptome Analysis for Investigating Host-Pathogen Interactions
14:58

High-Throughput Transcriptome Analysis for Investigating Host-Pathogen Interactions

Published on: March 5, 2022

In Silico Identification and Characterization of circRNAs During Host-Pathogen Interactions
10:27

In Silico Identification and Characterization of circRNAs During Host-Pathogen Interactions

Published on: October 21, 2022

Area of Science:

  • Genomics
  • Bioinformatics
  • Systems Biology

Background:

  • Conventional gene set analysis often overlooks gene interactions, crucial for understanding complex human diseases.
  • Existing methods focus on individual gene effects, potentially missing disease-driving mechanisms.

Purpose of the Study:

  • To develop a gene interaction enrichment analysis method.
  • To incorporate predefined gene sets, like pathways, to identify significant gene interaction effects.
  • To reduce irrelevant genes and extract core gene interactions impacting phenotypes.

Main Methods:

  • Proposed a gene interaction enrichment analysis framework.
  • Integrated knowledge from predefined gene sets (e.g., pathways).
  • Developed strategies for gene reduction and core interaction extraction.

Main Results:

  • Demonstrated utility on two public microarray datasets.
  • Successfully identified gene sets with significant gene interaction enrichments.
  • The method effectively captures gene interactions relevant to phenotypes.

Conclusions:

  • The proposed method enhances the detection of gene interactions in complex diseases.
  • Offers a powerful tool complementing classical gene set analyses.
  • Enriched gene interactions may reveal novel gene regulation mechanisms.