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

Genetic Lingo01:11

Genetic Lingo

111.0K
Overview
111.0K
Behavioral Genetics and Its Designs01:23

Behavioral Genetics and Its Designs

762
Behavior genetics explores how genetic inheritance influences human behavior. It focuses on how genes, passed from parents to offspring, contribute to the development of behavioral traits and tendencies. This branch of genetics seeks to understand the complex interplay between inherited genetic factors and environmental influences in shaping our behaviors.
The primary methodologies used in behavior genetics include family studies, twin studies, and adoption studies, each providing unique...
762
Genetic Drift03:33

Genetic Drift

42.0K
Natural selection—probably the most well-known evolutionary mechanism—increases the prevalence of traits that enhance survival and reproduction. However, evolution does not merely propagate favorable traits, nor does it always benefit populations.
42.0K
Genetic Screens02:46

Genetic Screens

5.3K
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...
5.3K
Mutation, Gene Flow, and Genetic Drift01:09

Mutation, Gene Flow, and Genetic Drift

60.9K
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).
60.9K
What is Genetic Engineering?00:49

What is Genetic Engineering?

77.2K
Overview
77.2K

You might also read

Related Articles

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

Sort by
Same author

Structural and mutational insights define ERMA as the ER Mg<sup>2+</sup> ATPase and reservoir gatekeeper.

Science advances·2026
Same author

Physics-Informed Artificial Intelligence Design of Picomolar Nanobodies Enables Deep Tumor Penetration and High-Contrast Imaging.

Research (Washington, D.C.)·2026
Same author

Leaf-Stomata-Inspired 3D Suspended Ultrasensitive E-Skin for Dual-Modal Tactile and Nociceptive Sensing in Robotics.

Nano letters·2026
Same author

Evolutionary Digital Twin for Oil and Gas Pipelines: A Cognitive Multi-Agent Framework with Continuous Feedback Learning.

Sensors (Basel, Switzerland)·2026
Same author

The application effect of the 5E-microteaching integration model in the standardized training of general practitioners.

Frontiers in medicine·2026
Same author

A Paradigm Shift in Snakebite Envenoming Therapy: From Conventional Antivenoms to Rationally Designed, Broadly Neutralizing Combination Therapies.

ACS pharmacology & translational science·2026
Same journal

circ2DGNN: circRNA-Disease Association Prediction via Transformer-Based Graph Neural Network.

IEEE/ACM transactions on computational biology and bioinformatics·2024
Same journal

Hierarchical Hypergraph Learning in Association- Weighted Heterogeneous Network for miRNA- Disease Association Identification.

IEEE/ACM transactions on computational biology and bioinformatics·2024
Same journal

Discriminative Domain Adaption Network for Simultaneously Removing Batch Effects and Annotating Cell Types in Single-Cell RNA-Seq.

IEEE/ACM transactions on computational biology and bioinformatics·2024
Same journal

MLW-BFECF: A Multi-Weighted Dynamic Cascade Forest Based on Bilinear Feature Extraction for Predicting the Stage of Kidney Renal Clear Cell Carcinoma on Multi-Modal Gene Data.

IEEE/ACM transactions on computational biology and bioinformatics·2024
Same journal

An End-to-End Knowledge Graph Fused Graph Neural Network for Accurate Protein-Protein Interactions Prediction.

IEEE/ACM transactions on computational biology and bioinformatics·2024
Same journal

Generative Biomedical Event Extraction With Constrained Decoding Strategy.

IEEE/ACM transactions on computational biology and bioinformatics·2024
See all related articles

Related Experiment Video

Updated: Nov 19, 2025

The Modular Design and Production of an Intelligent Robot Based on a Closed-Loop Control Strategy
11:53

The Modular Design and Production of an Intelligent Robot Based on a Closed-Loop Control Strategy

Published on: October 14, 2017

12.0K

Data-Driven Boolean Network Inference Using a Genetic Algorithm With Marker-Based Encoding.

Xiang Liu, Ning Shi, Yan Wang

    IEEE/ACM Transactions on Computational Biology and Bioinformatics
    |January 29, 2021
    PubMed
    Summary
    This summary is machine-generated.

    This study introduces a novel algorithm for inferring Boolean networks, enhancing gene regulatory network analysis. The method accurately identifies both network topology and dynamics using a marker-based genetic algorithm.

    More Related Videos

    Gene Digital Circuits Based on CRISPR-Cas Systems and Anti-CRISPR Proteins
    10:46

    Gene Digital Circuits Based on CRISPR-Cas Systems and Anti-CRISPR Proteins

    Published on: October 18, 2022

    2.1K
    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

    2.5K

    Related Experiment Videos

    Last Updated: Nov 19, 2025

    The Modular Design and Production of an Intelligent Robot Based on a Closed-Loop Control Strategy
    11:53

    The Modular Design and Production of an Intelligent Robot Based on a Closed-Loop Control Strategy

    Published on: October 14, 2017

    12.0K
    Gene Digital Circuits Based on CRISPR-Cas Systems and Anti-CRISPR Proteins
    10:46

    Gene Digital Circuits Based on CRISPR-Cas Systems and Anti-CRISPR Proteins

    Published on: October 18, 2022

    2.1K
    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

    2.5K

    Area of Science:

    • Systems Biology
    • Computational Biology
    • Bioinformatics

    Background:

    • Boolean networks are essential for understanding gene regulatory network (GRN) topology and dynamics.
    • Existing data-driven methods often struggle with inferring both network structure and function simultaneously due to encoding limitations.

    Purpose of the Study:

    • To develop a novel algorithm for the simultaneous inference of Boolean network topology and dynamics.
    • To overcome the limitations of inflexible encoding schemes in current evolutionary algorithms.

    Main Methods:

    • A marker-based genetic algorithm is proposed to encode regulatory nodes and logical operators within a chromosome.
    • The algorithm utilizes markers and expanded logical operators to infer a wider range of Boolean functions.

    Main Results:

    • The novel algorithm was applied to two artificial and three real-world gene regulatory networks.
    • Experimental results show superior accuracy in inferring both network topology and dynamics compared to existing algorithms.

    Conclusions:

    • The proposed marker-based genetic algorithm offers a more effective approach for Boolean network inference.
    • This method advances the analysis of gene regulatory networks by enabling simultaneous inference of topology and dynamics.