Search research articles

ABOUT JoVE

Overview Leadership Blog JoVE Help Center

AUTHORS

Publishing Process Editorial Board Scope & Policies Peer Review FAQ Submit

LIBRARIANS

Testimonials Subscriptions Access Resources Library Advisory Board FAQ

RESEARCH

JoVE Journal Methods Collections JoVE Encyclopedia of Experiments Archive

EDUCATION

JoVE Core JoVE Business JoVE Science Education JoVE Lab Manual Faculty Resource Center Faculty Site

Terms & Conditions of Use

Related Concept Videos

Protein Networks

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,...

Protein Networks

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,...

Structure of a Gene

Structure of a Gene

A gene is the fundamental unit of heredity. Every individual has two copies of each gene, one inherited from each parent. Although most people contain the same genes, there is a small fraction that is slightly different amongst people. A gene with a small difference in its sequence of DNA bases forms different alleles, contributing to different phenotypes.
However, only 1% of the DNA is composed of genes that encode proteins; the rest, 99% is non-coding DNA. This non-coding DNA performs...

What is Gene Expression?

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?

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...

DNA Microarrays

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...

You might also read

Related Articles

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

Sort by

Same author

Tunable Nanoparticle Thin-Film Reveals Distance Dependence of Auger-Mediated Radiation Enhancement in Diffuse Midline Glioma.

ACS nano·2026

Same author

Understanding DMG: current treatment options and prospective solutions using nanoparticles.

Drug delivery and translational research·2026

Same author

Wafer-scale CMOS foundry silicon-on-insulator devices for integrated temporal pulse compression.

Nanophotonics (Berlin, Germany)·2025

Same author

Multicellular tumor-stromal interactions recapitulate aspects of therapeutic response and human oncogenic signaling in a 3D disease model for H3K27M-altered DIPG.

Oncogene·2025

Same author

Quantitative noninvasive measurement of cerebrospinal fluid flow in shunted hydrocephalus.

Journal of neurosurgery·2024

Same author

Ex Vivo Drug Screening Assay with Artificial Membranes: Characterizing Cholesterol Desorbing Competencies of Beta-Cyclodextrins.

Langmuir : the ACS journal of surfaces and colloids·2023

Search research articles

Related Experiment Video

Updated: Jun 5, 2026

Mapping the Structure-Function Relationships of Disordered Oncogenic Transcription Factors Using Transcriptomic Analysis

Mapping the Structure-Function Relationships of Disordered Oncogenic Transcription Factors Using Transcriptomic Analysis

Published on: June 27, 2020

Inferring functional relationships and causal network structure from gene expression profiles.

Radhakrishnan Nagarajan¹, Meenakshi Upreti

¹Division of Biomedical Informatics, University of Arkansas for Medical Sciences, Little Rock, Arkansas, USA.

Methods in Enzymology

|December 29, 2010

Summary

This summary is machine-generated.

This study explores Granger causality (GC) to infer gene networks from expression data. It investigates noise impact and uses VAR parameter estimation for deeper insights into gene functional relationships.

More Related Videos

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

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 5, 2026

Mapping the Structure-Function Relationships of Disordered Oncogenic Transcription Factors Using Transcriptomic Analysis

Mapping the Structure-Function Relationships of Disordered Oncogenic Transcription Factors Using Transcriptomic Analysis

Published on: June 27, 2020

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

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:

Systems Biology
Bioinformatics
Computational Biology

Background:

Gene expression profiles offer insights into gene networks and potential causal relationships.
Discovering functional relationships from expression data can reveal undocumented gene interactions.
Existing techniques for network inference from gene expression data are varied.

Purpose of the Study:

To investigate Granger causality (GC) and its extensions for modeling gene network structures.
To assess the impact of noise variance on Granger causality relationships.
To utilize Vector Autoregressive (VAR) parameter estimation for enhanced functional relationship inference.

Main Methods:

Application of Granger causality (GC) and its extensions to gene expression profiles.
Analysis of the influence of noise variance on GC test results.
Employing VAR parameter estimation for detailed functional relationship analysis.

Main Results:

Granger causality effectively models network structures between genes from expression profiles.
Noise variance significantly impacts the reliability of inferred GC relationships.
VAR parameter estimation refines the understanding of functional relationships identified by GC.

Conclusions:

Granger causality is a valuable tool for inferring gene functional relationships and network structures.
Understanding noise impact is crucial for accurate network inference using GC.
VAR parameter estimation enhances the interpretability of gene network models derived from expression data.