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 Experiment Videos

Recent advances in gene expression data clustering: a case study with comparative results.

George B Bezerra1, Geraldo M A Cançado, Marcelo Menossi

  • 1Laboratório de Bioinformática e Computação Bio-Inspirada (LBiC/DCA/FEEC), UNICAMP, Caixa Postal 6101, 13083-852 Campinas, SP, Brazil. bezerra@dca.fee.unicamp.br

Genetics and Molecular Research : GMR
|December 13, 2005
PubMed
Summary

Related Concept Videos

You might also read

Related Articles

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

Sort by
Same author

Trends and intellectual property landscape in soybean breeding: a patent and paper Bibliometric analysis.

GM crops & food·2026
Same author

Engineering glyphosate tolerance in sugarcane through overexpression of a mutated version of its native <i>EPSPS</i> gene.

GM crops & food·2026
Same author

Enhanced expression of the <i>ScTpx2</i> gene confers tolerance to drought stress in transgenic sugarcane.

GM crops & food·2026
Same author

Physiological and Proteomic Responses of Sugarcane to Water Deficit Stress: Insights from a Self-Fertilized Clone.

International journal of molecular sciences·2025
Same author

Gene Co-Expression Networks Highlight Key Nodes Associated With Ammonium Nitrate in Sugarcane.

Physiologia plantarum·2025
Same author

Fine-Tuning of <i>Arabidopsis thaliana</i> Response to Endophytic Colonization by <i>Gluconacetobacter diazotrophicus</i> PAL5 Revealed by Transcriptomic Analysis.

Plants (Basel, Switzerland)·2024

A new hierarchical artificial immune network (HaiNet) offers improved gene expression data clustering. This immune-inspired method provides more consistent and informative results than traditional self-organizing maps for analyzing complex biological data.

Area of Science:

  • Bioinformatics
  • Computational Biology
  • Machine Learning in Biology

Background:

  • Gene expression data analysis presents challenges due to high dimensionality and multiple analytical perspectives.
  • Existing data clustering techniques show partial success when applied to gene expression datasets.
  • Advanced computational resources, including hierarchical structures, are needed for effective gene expression analysis.

Purpose of the Study:

  • To introduce an immune-inspired hierarchical clustering algorithm, HaiNet, specifically designed for gene expression data analysis.
  • To evaluate the performance of HaiNet on a novel dataset of maize plants under aluminum stress.
  • To compare HaiNet's effectiveness against a commonly used method, the self-organizing map.

Main Methods:

Related Experiment Videos

  • Development of the hierarchical artificial immune network (HaiNet) algorithm.
  • Application of HaiNet to a newly generated gene expression dataset from maize plants exposed to varying aluminum concentrations.
  • Comparative analysis of HaiNet against the self-organizing map (SOM) algorithm.

    • Main Results:

    • HaiNet demonstrated superior performance in clustering gene expression data compared to the self-organizing map.
    • The algorithm yielded more consistent and informative results in the analysis of maize plant gene expression data.
    • HaiNet effectively handles the complexities inherent in high-dimensional gene expression datasets.

    Conclusions:

    • HaiNet is a promising technique for the analysis of gene expression data, offering enhanced clustering capabilities.
    • The immune-inspired hierarchical approach provides a robust framework for extracting meaningful insights from complex biological datasets.
    • HaiNet represents an advancement in computational tools for bioinformatics and plant science research.