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Related Concept Videos

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...
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...
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...
Protein Networks02:26

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,...
In-vitro Mutagenesis01:16

In-vitro Mutagenesis

To learn more about the function of a gene, researchers can observe what happens when the gene is inactivated or “knocked out,” by creating genetically engineered knockout animals. Knockout mice have been particularly useful as models for human diseases such as cancer, Parkinson’s disease, and diabetes.
Genome-wide Association Studies-GWAS01:11

Genome-wide Association Studies-GWAS

Genome-wide association studies or GWAS are used to identify whether common SNPs are associated with certain diseases. Suppose specific SNPs are more frequently observed in individuals with a particular disease than those without the disease. In that case, those SNPs are said to be associated with the disease. Chi-square analysis is performed to check the probability of the allele likely to be associated with the disease.
GWAS does not require the identification of the target gene involved in...

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Related Experiment Video

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Informatic Analysis of Sequence Data from Batch Yeast 2-Hybrid Screens
09:14

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Published on: June 28, 2018

A support vector machine approach for detecting gene-gene interaction.

Shyh-Huei Chen1, Jielin Sun, Latchezar Dimitrov

  • 1Department of Industrial Management, National Yunlin University of Science and Technology, Yunlin, Taiwan.

Genetic Epidemiology
|October 31, 2007
PubMed
Summary
This summary is machine-generated.

This study introduces a computational framework using support vector machines and combinatorial optimization to detect complex gene-gene and gene-environment interactions, aiding in understanding disease mechanisms.

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Area of Science:

  • Computational biology
  • Genetic epidemiology
  • Bioinformatics

Background:

  • Genetic factors significantly influence human diseases, often involving complex interactions.
  • Understanding gene-gene and gene-environment interactions is crucial for elucidating disease mechanisms.

Purpose of the Study:

  • To develop a computational framework for detecting high-order gene-gene and gene-environment interactions.
  • To apply support vector machine and combinatorial optimization techniques within this framework.

Main Methods:

  • Utilized support vector machine (SVM) for classification.
  • Employed combinatorial optimization techniques, including local search and genetic algorithms.
  • Developed a computational framework integrating these methods for interaction detection.

Main Results:

  • The proposed approach successfully identifies high-order gene-gene and gene-environment interactions.
  • Demonstrated strong classification power and reduced overfitting.
  • Showcased ability to handle unbalanced data and produce stable models.

Conclusions:

  • The computational framework is a promising tool for genetic epidemiologists.
  • Highlights the utility of SVM and combinatorial optimization in analyzing complex genetic data.
  • Encourages wider adoption and extension of these methods in genetic research.