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 Variation01:25

Genetic Variation

377
Genetic variation is the diversity in DNA sequences found among individuals of the same species. This diversity is crucial for a species' survival because it helps organisms adapt to environmental changes. Genetic variation begins with fertilization, where an egg and sperm cell merge. Each of these cells carries 23 chromosomes, up to 46 in the fertilized egg. Chromosomes are long DNA strands that contain genes, the basic units of heredity.
Genes exist in different versions called alleles,...
377
Genetic Lingo01:11

Genetic Lingo

104.3K
Overview
104.3K
Combinatorial Gene Control02:33

Combinatorial Gene Control

8.4K
Combinatorial gene control is the synergistic action of several transcriptional factors to regulate the expression of a single gene. The absence of one or more of these factors may lead to a significant difference in the level of gene expression or repression.
The expression of more than 30,000 genes is controlled by approximately 2000-3000 transcription factors. This is possible because a single transcription factor can recognize more than one regulatory sequence. The specificity in gene...
8.4K
Mutation, Gene Flow, and Genetic Drift01:09

Mutation, Gene Flow, and Genetic Drift

59.3K
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).
59.3K
Single Nucleotide Polymorphisms-SNPs01:05

Single Nucleotide Polymorphisms-SNPs

15.7K
A single nucleotide polymorphism or SNP is a single nucleotide variation at a specific genomic position in a large population. It is the most prevalent type of sequence variation found in the human genome. Point mutations that occur in more than 1% of the population qualify as SNPs. These are present once every 1000 nucleotides on an average in the human genome. Replacement of a purine with another purine (A/G) or a pyrimidine with another pyrimidine (C/T) is known as a transition. In contrast,...
15.7K
Types of Genetic Transfer Between Organisms02:18

Types of Genetic Transfer Between Organisms

5.5K
5.5K

You might also read

Related Articles

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

Sort by
Same author

Ensuring Fairness in Detecting Mild Cognitive Impairment with MRI.

AMIA ... Annual Symposium proceedings. AMIA Symposium·2025
Same author

Enhancing clinical outcome predictions through effective sample size evaluation in graph-based digital twin modeling.

BioData mining·2025
Same author

Perceptual and technical barriers in sharing and formatting metadata accompanying omics studies.

Cell genomics·2025
Same author

Erratum: A latent transfer learning method for estimating hospital-specific post-acute healthcare demands following SARS-CoV-2 infection.

Patterns (New York, N.Y.)·2025
Same author

AI as an accelerator for defining new problems that transcends boundaries.

BioData mining·2025
Same author

Preoperative anemia is an unsuspecting driver of machine learning prediction of adverse outcomes after lumbar spinal fusion.

The spine journal : official journal of the North American Spine Society·2025
Same journal

On a Population Sizing Model for Evolution Strategies Optimizing the Highly Multimodal Rastrigin Function.

Genetic and Evolutionary Computation Conference : [proceedings]. Genetic and Evolutionary Computation Conference·2024
Same journal

Toward inverse generative social science using multi-objective genetic programming.

Genetic and Evolutionary Computation Conference : [proceedings]. Genetic and Evolutionary Computation Conference·2020
Same journal

GA-Based Selection of Vaginal Microbiome Features Associated with Bacterial Vaginosis.

Genetic and Evolutionary Computation Conference : [proceedings]. Genetic and Evolutionary Computation Conference·2014
Same journal

Mask Functions for the Symbolic Modeling of Epistasis Using Genetic Programming.

Genetic and Evolutionary Computation Conference : [proceedings]. Genetic and Evolutionary Computation Conference·2012
Same journal

A Balanced Accuracy Fitness Function Leads to Robust Analysis using Grammatical Evolution Neural Networks in the Case of Class Imbalance.

Genetic and Evolutionary Computation Conference : [proceedings]. Genetic and Evolutionary Computation Conference·2011
Same journal

Alternative Cross-Over Strategies and Selection Techniques for Grammatical Evolution Optimized Neural Networks.

Genetic and Evolutionary Computation Conference : [proceedings]. Genetic and Evolutionary Computation Conference·2011
See all related articles

Related Experiment Video

Updated: Sep 4, 2025

Following the Dynamics of Structural Variants in Experimentally Evolved Populations
04:52

Following the Dynamics of Structural Variants in Experimentally Evolved Populations

Published on: February 3, 2023

1.0K

Semantic variation operators for multidimensional genetic programming.

William La Cava1, Jason H Moore1

  • 1University of Pennsylvania, Philadelphia, PA.

Genetic and Evolutionary Computation Conference : [Proceedings]. Genetic and Evolutionary Computation Conference
|July 18, 2022
PubMed
Summary
This summary is machine-generated.

This study introduces machine learning to genetic programming, enhancing building block identification. A novel crossover operator achieves state-of-the-art results in regression problems.

Keywords:
feature constructionregressionrepresentation learningvariation

More Related Videos

Heuristic Mining of Hierarchical Genotypes and Accessory Genome Loci in Bacterial Populations
08:03

Heuristic Mining of Hierarchical Genotypes and Accessory Genome Loci in Bacterial Populations

Published on: December 7, 2021

2.3K
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

2.3K

Related Experiment Videos

Last Updated: Sep 4, 2025

Following the Dynamics of Structural Variants in Experimentally Evolved Populations
04:52

Following the Dynamics of Structural Variants in Experimentally Evolved Populations

Published on: February 3, 2023

1.0K
Heuristic Mining of Hierarchical Genotypes and Accessory Genome Loci in Bacterial Populations
08:03

Heuristic Mining of Hierarchical Genotypes and Accessory Genome Loci in Bacterial Populations

Published on: December 7, 2021

2.3K
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

2.3K

Area of Science:

  • Computer Science
  • Artificial Intelligence
  • Machine Learning

Background:

  • Genetic programming (GP) represents solutions as programs, offering potential for building block identification.
  • Current GP methods may not optimally exploit reusable program components.

Purpose of the Study:

  • To enhance building block identification in multidimensional genetic programming.
  • To improve the efficiency and effectiveness of genetic programming through machine learning integration.

Main Methods:

  • Investigated machine learning to bias the promotion of program components.
  • Proposed two semantic operators, including a forward stagewise crossover, for strategic building block placement during crossover.
  • Evaluated performance on regression problems and a large benchmark study.

Main Results:

  • The forward stagewise crossover operator demonstrated significant improvements in regression problems.
  • Achieved state-of-the-art results in a comprehensive benchmark study.
  • Analyzed the propensity of architectures for heuristic search to utilize evolutionary information.

Conclusions:

  • Machine learning integration and semantic crossover operators enhance genetic programming.
  • The proposed methods offer a promising direction for improving evolutionary computation.
  • Further analysis is needed on data representation collinearity and complexity for disentangling variation factors.