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

Genes@Work: an efficient algorithm for pattern discovery and multivariate feature selection in gene expression data.

Jorge Lepre1, J Jeremy Rice, Yuhai Tu

  • 1IBM T.J. Watson Research Center, P.O. Box 218, Yorktown Heights, NY 10598, USA.

Bioinformatics (Oxford, England)
|February 7, 2004
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

Towards a decentralized future for open-science databases.

Nature genetics·2026
Same author

Erratum: Placental epigenetic clocks derived from crowdsourcing: Implications for the study of accelerated aging in obstetrics.

iScience·2026
Same author

Benchmarking large language models for predictive modeling in biomedical research with a focus on reproductive health.

Cell reports. Medicine·2026
Same author

Nonequilibrium Thermodynamics for Biochemical Networks: Energetic Principles of Cellular Function.

Annual review of biophysics·2026
Same author

Chromosomal Topological Domain Formation Modulates Transcription and the Coupling of Neighboring Genes in <i>Escherichia coli</i>.

bioRxiv : the preprint server for biology·2025
Same author

Microbiome preterm birth DREAM challenge: Crowdsourcing machine learning approaches to advance preterm birth research.

Cell reports. Medicine·2025
Same journal

MCFST: Spatial domain identification method based on multi-view graph convolutional network and graph fusion network.

Bioinformatics (Oxford, England)·2026
Same journal

SpaBiT: Enhancing Spatial Transcriptomics Resolution via Bidirectional Attention Transformers.

Bioinformatics (Oxford, England)·2026
Same journal

EDEL: Enhancing Dense Retrievers for Curation of Biomedical Knowledge Bases.

Bioinformatics (Oxford, England)·2026
Same journal

Informative Relational Learning for Adverse Reaction Prediction with Enhanced Generalization to Novel Drugs.

Bioinformatics (Oxford, England)·2026
Same journal

An interpretable deep learning framework uncovers features governing CRISPR-Cas9 genome-editing efficiency.

Bioinformatics (Oxford, England)·2026
Same journal

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

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

A new algorithm efficiently identifies gene expression patterns linked to disease phenotypes. This method discovered validated genes differentiating lymphoma types, outperforming other approaches in high-dimensional data analysis.

Area of Science:

  • Bioinformatics
  • Computational Biology
  • Genomics

Background:

  • High-dimensional gene expression datasets require advanced feature selection methods.
  • Existing approaches often overlook the combinatorial nature of gene interactions.
  • Understanding multivariate gene associations is crucial for linking expression patterns to phenotypes.

Purpose of the Study:

  • To develop and present a deterministic algorithm for discovering multivariate gene associations in gene expression data.
  • To identify gene patterns that are differentially expressed compared to control datasets.
  • To validate the algorithm's effectiveness in identifying biologically relevant gene sets.

Main Methods:

  • A deterministic, exhaustive, and efficient algorithm for pattern discovery in gene expression data.

Related Experiment Videos

  • The algorithm avoids exhaustive enumeration of the entire pattern space.
  • Application of the algorithm to identify differentiating genes between two lymphoma types.

    • Main Results:

    • The algorithm successfully identified a set of genes differentiating two lymphoma types.
    • These identified genes demonstrated consistent behavior in an independent dataset from a different laboratory and array platform.
    • The discovered genes, significant via multivariate statistics, would be missed by conventional methods.

    Conclusions:

    • The presented algorithm offers a novel and effective approach for pattern discovery in complex gene expression datasets.
    • The method provides a validated set of genes for differentiating specific disease subtypes, such as lymphoma.
    • Genes identified by this algorithm show robust and consistent biological relevance across different experimental conditions.