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

Network constrained clustering for gene microarray data.

Dongxiao Zhu1, Alfred O Hero, Hong Cheng

  • 1Bioinformatics Program, University of Michigan, Ann Arbor, MI 48109, USA. zhud@umich.edu

Bioinformatics (Oxford, England)
|September 6, 2005
PubMed
Summary
This summary is machine-generated.

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

Association of Salivary Micro Ribonucleic Acid Levels With the Severity of Severe Acute Respiratory Syndrome Coronavirus 2 Infection in Children.

Pediatric discovery·2026
Same author

Conserved host genes act as anchors for fast-evolving piRNA clusters via promoter hijacking.

Research square·2026
Same author

Stress Responsive bZIP Transcription Factors ATF4 and BACH1 Cooperate With MAF-Family bZIP Protein NRL to Fine-Tune Rod Photoreceptor Gene Expression.

Investigative ophthalmology & visual science·2026
Same author

Revisiting retinal and macular degeneration in the genomics era.

Nature reviews. Genetics·2026
Same author

Halofantrine protects photoreceptors in multiple models of retinal degeneration.

Research square·2026
Same author

Asparagus racemosus Root Extract (SheVari4®) Alleviates Menopausal Symptoms in Pre-, Peri-, and Post-menopausal Healthy Women.

Cureus·2026
Same journal

3DICE: Interpretable 3D Cross-Modal Learning for Drug-Target Interaction Prediction and Large-Scale Drug Discovery.

Bioinformatics (Oxford, England)·2026
Same journal

KASSPer: Kinase Active Site Structure Prediction using Protein and Ligand Language Models and Its Application to Virtual Screening.

Bioinformatics (Oxford, England)·2026
Same journal

IDR searcher: a search engine solution for public image resources.

Bioinformatics (Oxford, England)·2026
Same journal

KCFtools: Rapid alignment-free method for introgression screening and GWAS using k-mer profiles.

Bioinformatics (Oxford, England)·2026
Same journal

Meta2DB: Curated shotgun metagenomic feature sets and metadata for health state prediction.

Bioinformatics (Oxford, England)·2026
Same journal

conMItion: an R package adjusting confounding factors for associations in multi-omics.

Bioinformatics (Oxford, England)·2026
See all related articles

This study introduces an improved gene clustering method using co-expression networks to identify gene signaling pathways from microarray data. The novel approach effectively groups functionally related genes, outperforming traditional methods in pathway rediscovery.

Area of Science:

  • Bioinformatics
  • Systems Biology
  • Genomics

Background:

  • Gene expression analysis is crucial for understanding cellular functions.
  • Traditional clustering methods may struggle with complex gene relationships.
  • Network-based approaches offer a new perspective for bioinformatics problems.

Purpose of the Study:

  • To develop an improved gene clustering approach for inferring gene signaling pathways from gene microarray data.
  • To group functionally related genes effectively, even with dissimilar expression patterns.
  • To enhance the discovery of biological pathways using network analysis.

Main Methods:

  • Constructing co-expression networks incorporating both linear and non-linear gene associations.
  • Applying controlled biological and statistical significance to network construction.

Related Experiment Videos

  • Utilizing a novel gene clustering algorithm inspired by metabolic network studies.

    • Main Results:

    • The proposed approach successfully groups functionally related genes into tight clusters.
    • Demonstrated superior performance compared to traditional clustering methods on yeast and retinal datasets.
    • Effectively rediscovered the yeast galactose metabolism pathway and clustered photoreceptor differentiation pathway genes.

    Conclusions:

    • The network-based gene clustering approach provides a powerful tool for inferring gene signaling pathways.
    • This method enhances the accuracy and efficiency of pathway analysis from gene expression data.
    • The GeneNT R package is available for public use, facilitating further research.