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In-vitro Mutagenesis01:16

In-vitro Mutagenesis

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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.
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Lethal Alleles02:41

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Agouti: A Lethal Allele
Lucien Cuénot discovered lethal alleles in 1905 while studying the inheritance of coat color in mice. The agouti gene is responsible for the color of the coat in mice. This gene codes for an agouti-signaling protein, which is responsible for melanin distribution in mammals. The wild-type allele gives rise to gray-brown coat color in mice, while the mutant allele gives rise to yellow coat color. In addition to coat color, the agouti gene is associated with the yellow...
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Mutation, Gene Flow, and Genetic Drift01:09

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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).
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Related Experiment Video

Updated: Oct 6, 2025

Mutagenesis and Functional Selection Protocols for Directed Evolution of Proteins in E. coli
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A Modified Memetic Algorithm with an Application to Gene Selection in a Sheep Body Weight Study.

Maoxuan Miao1, Jinran Wu2, Fengjing Cai1

  • 1College of Mathematics and Physics, Wenzhou University, Wenzhou 325035, China.

Animals : an Open Access Journal From MDPI
|January 20, 2022
PubMed
Summary

This study introduces a modified memetic algorithm (MA) for efficient gene subset selection. The novel approach improves accuracy and reduces computation time for identifying minimal gene subsets, outperforming existing methods.

Keywords:
gene selectionlocal search operatormemetic algorithmmodificationssheep weight

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Area of Science:

  • Bioinformatics
  • Computational Biology
  • Machine Learning

Background:

  • Selecting minimal gene subsets is crucial for accurate predictions but is computationally challenging due to redundancy and potential information loss.
  • Traditional genetic algorithms struggle with exploitation capability and dimension reduction in predictor variables.
  • Overfitting and missing key genes are significant issues in high-dimensional biological data analysis.

Purpose of the Study:

  • To develop a modified memetic algorithm (MA) that enhances gene subset selection.
  • To improve the exploitation capability and dimension reduction of genetic algorithms.
  • To identify a minimal best subset of genes efficiently and accurately.

Main Methods:

  • A modified memetic algorithm (MA) incorporating an improved splicing method was developed.
  • Novel aspects include iterative subset updating, an 'add'/'del' operator based on backward sacrifice, and replacement of the mutation operator.
  • The algorithm was evaluated using a Hu sheep body weight dataset.

Main Results:

  • The proposed MA achieved superior performance in identifying minimal gene subsets compared to traditional genetic algorithms and advanced adaptive best-subset selection algorithms.
  • The algorithm demonstrated efficiency, requiring fewer iterations to obtain optimal subsets.
  • Experimental results confirmed enhanced exploitation capability and search efficiency.

Conclusions:

  • The modified memetic algorithm offers a more effective and efficient solution for minimal gene subset selection.
  • This approach addresses key challenges in high-dimensional data analysis, improving predictive model performance.
  • The MA provides a robust tool for biological data analysis and feature selection.