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...
Epistasis01:39

Epistasis

In addition to multiple alleles at the same locus influencing traits, numerous genes or alleles at different locations may interact and influence phenotypes in a phenomenon called epistasis. For example, rabbit fur can be black or brown depending on whether the animal is homozygous dominant or heterozygous at a TYRP1 locus. However, if the rabbit is also homozygous recessive at a locus on the tyrosinase gene (TYR), it will have an unshaded coat that appears white, regardless of its TYRP1...
Combinatorial Gene Control02:33

Combinatorial Gene Control

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...
Genetic Screens02:46

Genetic Screens

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 result in visible changes...
Mechanistic Models: Compartment Models in Algorithms for Numerical Problem Solving01:29

Mechanistic Models: Compartment Models in Algorithms for Numerical Problem Solving

Mechanistic models play a crucial role in algorithms for numerical problem-solving, particularly in nonlinear mixed effects modeling (NMEM). These models aim to minimize specific objective functions by evaluating various parameter estimates, leading to the development of systematic algorithms. In some cases, linearization techniques approximate the model using linear equations.
In individual population analyses, different algorithms are employed, such as Cauchy's method, which uses a...
Pleiotropy01:33

Pleiotropy

Pleiotropy is the phenomenon in which a single gene impacts multiple, seemingly unrelated phenotypic traits. For example, defects in the SOX10 gene cause Waardenburg Syndrome Type 4, or WS4, which can cause defects in pigmentation, hearing impairments, and an absence of intestinal contractions necessary for elimination. This diversity of phenotypes results from the expression pattern of SOX10 in early embryonic and fetal development. SOX10 is found in neural crest cells that form melanocytes,...

You might also read

Related Articles

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

Sort by
Same author

Using GPT-4 to write a scientific review article: a pilot evaluation study.

BioData mining·2024
Same author

Sex classification of 3D skull images using deep neural networks.

Scientific reports·2024
Same author

Distinct Network Patterns Emerge from Cartesian and XOR Epistasis Models: A Comparative Network Science Analysis.

Research square·2024
Same author

PFERM: A Fair Empirical Risk Minimization Approach with Prior Knowledge.

AMIA Joint Summits on Translational Science proceedings. AMIA Joint Summits on Translational Science·2024
Same author

KRAGEN: a knowledge graph-enhanced RAG framework for biomedical problem solving using large language models.

Bioinformatics (Oxford, England)·2024
Same author

Centralized and Federated Models for the Analysis of Clinical Data.

Annual review of biomedical data science·2024
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

Semantic variation operators for multidimensional genetic programming.

Genetic and Evolutionary Computation Conference : [proceedings]. Genetic and Evolutionary Computation Conference·2022
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

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: May 18, 2026

Mapping Bacterial Functional Networks and Pathways in Escherichia Coli using Synthetic Genetic Arrays
14:06

Mapping Bacterial Functional Networks and Pathways in Escherichia Coli using Synthetic Genetic Arrays

Published on: November 12, 2012

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

Ryan J Urbanowicz1, Bill C White, Jason H Moore

  • 1Dartmouth College, 1 Medical Center Dr., Hanover, NH 03755, USA.

Genetic and Evolutionary Computation Conference : [Proceedings]. Genetic and Evolutionary Computation Conference
|September 29, 2012
PubMed
Summary
This summary is machine-generated.

Introducing a genetic "mask" improves symbolic discriminant analysis (SDA) performance in genetic epidemiology. Pre-processing data with this novel building block enhances genetic programming (GP) models for complex multifactorial diseases.

More Related Videos

Efficient PAM-Less Base Editing for Zebrafish Modeling of Human Genetic Disease with zSpRY-ABE8e
07:31

Efficient PAM-Less Base Editing for Zebrafish Modeling of Human Genetic Disease with zSpRY-ABE8e

Published on: February 17, 2023

In Vivo Modeling of the Morbid Human Genome using Danio rerio
12:31

In Vivo Modeling of the Morbid Human Genome using Danio rerio

Published on: August 24, 2013

Related Experiment Videos

Last Updated: May 18, 2026

Mapping Bacterial Functional Networks and Pathways in Escherichia Coli using Synthetic Genetic Arrays
14:06

Mapping Bacterial Functional Networks and Pathways in Escherichia Coli using Synthetic Genetic Arrays

Published on: November 12, 2012

Efficient PAM-Less Base Editing for Zebrafish Modeling of Human Genetic Disease with zSpRY-ABE8e
07:31

Efficient PAM-Less Base Editing for Zebrafish Modeling of Human Genetic Disease with zSpRY-ABE8e

Published on: February 17, 2023

In Vivo Modeling of the Morbid Human Genome using Danio rerio
12:31

In Vivo Modeling of the Morbid Human Genome using Danio rerio

Published on: August 24, 2013

Area of Science:

  • Genetic Epidemiology
  • Computational Biology
  • Bioinformatics

Background:

  • Complex multifactorial diseases present challenges in genetic epidemiology due to nonlinear genotype-to-phenotype relationships.
  • Epistasis, or gene-gene interactions, significantly complicates the mapping from genotype to phenotype.

Purpose of the Study:

  • To introduce and evaluate the effectiveness of a novel building block, the genetic "mask", within Symbolic Discriminant Analysis (SDA).
  • To determine if incorporating expert knowledge via "mask" building blocks enhances SDA performance for modeling gene-gene interactions.

Main Methods:

  • Symbolic Discriminant Analysis (SDA) utilizing Genetic Programming (GP) to evolve predictive models.
  • Introduction of the "genetic mask" as a new building block, encoding pre-constructed relationships between attributes.
  • Comparison of SDA performance with and without the "genetic mask" building blocks.

Main Results:

  • The "genetic mask" building block was successfully integrated into the SDA framework.
  • The results indicate that the availability of "mask" building blocks improves SDA performance.
  • Pre-processing data with expert-derived relationships enhances Genetic Programming (GP) model efficacy.

Conclusions:

  • The "genetic mask" is a valuable addition to SDA, improving its ability to model complex genetic interactions.
  • Data pre-processing, particularly incorporating expert knowledge, is a beneficial strategy for enhancing GP performance in genetic epidemiology.