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

What is Gene Expression?01:42

What is Gene Expression?

196.7K
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...
196.7K
What is Gene Expression?01:36

What is Gene Expression?

11.4K
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...
11.4K
Time-Series Graph00:54

Time-Series Graph

5.2K
A time-series graph is a line graph with repeated measurements taken at successive intervals of time. It is also called a time series chart. To construct a time-series graph, one must look at both pieces of a paired data set. The horizontal axis is used to plot the time increments, and the vertical axis is used to plot the values of the variable that one is measuring. By using the axes in this way, each point on the graph will correspond to time and a measured quantity. The points on the graph...
5.2K
Mutation, Gene Flow, and Genetic Drift01:09

Mutation, Gene Flow, and Genetic Drift

64.3K
In a population that is not at Hardy-Weinberg equilibrium, the frequency of alleles changes over time. Therefore, any deviations from the five conditions of Hardy-Weinberg equilibrium can alter the genetic variation of a given population. Conditions that change the genetic variability of a population include mutations, natural selection, non-random mating, gene flow, and genetic drift (small population size).
64.3K
Discrete-Time Fourier Series01:20

Discrete-Time Fourier Series

684
The Discrete-Time Fourier Series (DTFS) is a fundamental concept in signal processing, serving as the discrete-time counterpart to the continuous-time Fourier series. It allows for the representation and analysis of discrete-time periodic signals in terms of their frequency components. Unlike its continuous counterpart, which utilizes integrals, the calculation of DTFS expansion coefficients involves summations due to the discrete nature of the signal.
For a discrete-time periodic signal x[n]...
684
Cell Specific Gene Expression01:58

Cell Specific Gene Expression

16.5K
Multicellular organisms contain a variety of structurally and functionally distinct cell types, but the DNA in all the cells originated from the same parent cells. The differences in the cells can be attributed to the differential gene expression. Liver cells, whose functions include detoxification of blood, production of bile to metabolize fats, and synthesis of proteins essential for metabolism, must express a specific set of genes to perform their functions. Gene expression also varies with...
16.5K

You might also read

Related Articles

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

Sort by
Same author

Structure aware graph community cluster pruning for efficient neural network compression in Parkinson's disease diagnosis.

Scientific reports·2026
Same author

LBF-MI: Limited Boolean Functions and Mutual Information to Infer a Gene Regulatory Network from Time-Series Gene Expression Data.

Genes·2025
Same author

Comparative analysis of YOLO models for green coffee bean detection and defect classification.

Scientific reports·2024
Same author

Optimized Crop Disease Identification in Bangladesh: A Deep Learning and SVM Hybrid Model for Rice, Potato, and Corn.

Journal of imaging·2024
Same author

In Silico Pleiotropy Analysis in KEGG Signaling Networks Using a Boolean Network Model.

Biomolecules·2022
Same author

A novel constrained genetic algorithm-based Boolean network inference method from steady-state gene expression data.

Bioinformatics (Oxford, England)·2021

Related Experiment Video

Updated: Feb 2, 2026

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

2.6K

A Boolean network inference from time-series gene expression data using a genetic algorithm.

Shohag Barman1, Yung-Keun Kwon1

  • 1Department of IT Convergence, University of Ulsan, 93 Nam-gu, Ulsan, Republic of Korea.

Bioinformatics (Oxford, England)
|November 14, 2018
PubMed
Summary
This summary is machine-generated.

We developed a genetic algorithm-based Boolean network inference (GABNI) method for efficiently inferring gene regulatory networks from large-scale gene expression data. GABNI significantly outperforms existing methods in accuracy and scalability.

More Related Videos

An Allele-specific Gene Expression Assay to Test the Functional Basis of Genetic Associations
10:17

An Allele-specific Gene Expression Assay to Test the Functional Basis of Genetic Associations

Published on: November 3, 2010

23.4K
Using an Automated Cell Counter to Simplify Gene Expression Studies: siRNA Knockdown of IL-4 Dependent Gene Expression in Namalwa Cells
10:34

Using an Automated Cell Counter to Simplify Gene Expression Studies: siRNA Knockdown of IL-4 Dependent Gene Expression in Namalwa Cells

Published on: April 14, 2010

16.0K

Related Experiment Videos

Last Updated: Feb 2, 2026

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

2.6K
An Allele-specific Gene Expression Assay to Test the Functional Basis of Genetic Associations
10:17

An Allele-specific Gene Expression Assay to Test the Functional Basis of Genetic Associations

Published on: November 3, 2010

23.4K
Using an Automated Cell Counter to Simplify Gene Expression Studies: siRNA Knockdown of IL-4 Dependent Gene Expression in Namalwa Cells
10:34

Using an Automated Cell Counter to Simplify Gene Expression Studies: siRNA Knockdown of IL-4 Dependent Gene Expression in Namalwa Cells

Published on: April 14, 2010

16.0K

Area of Science:

  • Systems Biology
  • Computational Biology
  • Bioinformatics

Background:

  • Inferring gene regulatory networks from time-series gene expression data is crucial in systems biology.
  • Existing methods struggle with scalability and efficiency when dealing with a large number of genes.

Purpose of the Study:

  • To develop an efficient and scalable method for inferring gene regulatory networks.
  • To address the limitations of current methods in handling large-scale gene expression data.

Main Methods:

  • A novel genetic algorithm-based Boolean network inference (GABNI) method was proposed.
  • GABNI employs a two-stage approach, utilizing mutual information-based Boolean network inference (MIBNI) initially, followed by a modified genetic algorithm (GA) for optimization.
  • The genetic algorithm framework was adapted to efficiently reduce the search space.

Main Results:

  • GABNI demonstrated superior performance compared to four well-known inference methods in extensive simulations.
  • The method achieved significantly higher structural and dynamics accuracies on both artificial and real gene expression datasets.
  • GABNI proved to be an efficient and scalable tool for inferring Boolean networks.

Conclusions:

  • The proposed GABNI method offers an efficient and scalable solution for inferring gene regulatory networks from time-series gene expression data.
  • GABNI overcomes the limitations of previous methods in handling large-scale biological networks.