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

Extracting conserved gene expression motifs from gene expression data.

T M Murali1, Simon Kasif

  • 1Bioinformatics Program, 48 Cummington St., Boston University, Boston, MA 02152, USA.

Pacific Symposium on Biocomputing. Pacific Symposium on Biocomputing
|February 27, 2003
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

Provenance Tracing in Network Diffusion Algorithms.

Pacific Symposium on Biocomputing. Pacific Symposium on Biocomputing·2026
Same author

SynVerse: a modular framework for building and evaluating deep learning-based drug synergy prediction models.

Briefings in bioinformatics·2025
Same author

NEFFy: a versatile tool for computing the number of effective sequences.

Bioinformatics (Oxford, England)·2025
Same author

ICoN: integration using co-attention across biological networks.

Bioinformatics advances·2025
Same author

SPIDER: constructing cell-type-specific protein-protein interaction networks.

Bioinformatics advances·2024
Same author

Current and future directions in network biology.

Bioinformatics advances·2024
Same journal

Trust, Reproducibility, and Progress: The Roles of Independent Blind Prediction and Assessment and Benchmarking in Computational Biology.

Pacific Symposium on Biocomputing. Pacific Symposium on Biocomputing·2026
Same journal

The Evolving Cyberinfrastructure at the National Institutes of Health to Support Data and AI in Biomedical Research.

Pacific Symposium on Biocomputing. Pacific Symposium on Biocomputing·2026
Same journal

Applications of AI & ML in Biomanufacturing of Cell and Gene Therapies.

Pacific Symposium on Biocomputing. Pacific Symposium on Biocomputing·2026
Same journal

AI for Health: Leveraging Artificial Intelligence to Revolutionize Healthcare.

Pacific Symposium on Biocomputing. Pacific Symposium on Biocomputing·2026
Same journal

Workshop Introduction: Advances of AI Methods in Single Cell Spatial Omics.

Pacific Symposium on Biocomputing. Pacific Symposium on Biocomputing·2026
Same journal

DRIVE-KG: Enhancing variant-phenotype association discovery in understudied complex diseases using heterogeneous knowledge graphs.

Pacific Symposium on Biocomputing. Pacific Symposium on Biocomputing·2026
See all related articles

We developed conserved gene expression motifs (XMOTIFs) to analyze gene expression data. These motifs identify patterns across samples, effectively distinguishing between different cancer types and disease outcomes.

Area of Science:

  • Bioinformatics
  • Computational Biology
  • Genomics

Background:

  • Gene expression data analysis is crucial for understanding biological processes and disease states.
  • Identifying patterns in gene expression across samples can reveal underlying biological mechanisms.
  • Current methods may not fully capture conserved expression patterns relevant to disease classification.

Purpose of the Study:

  • To introduce a novel representation for gene expression data called conserved gene expression motifs (XMOTIFs).
  • To develop a computational technique for discovering large conserved gene motifs.
  • To demonstrate the utility of XMOTIFs in distinguishing between different classes in biological data, such as disease outcomes.

Main Methods:

  • Defining conserved gene expression as consistent expression levels across samples.

Related Experiment Videos

  • Defining conserved gene expression motifs (XMOTIFs) as subsets of genes conserved across sample subsets.
  • Developing and applying a computational algorithm to discover XMOTIFs covering all samples and classes.

    • Main Results:

    • The algorithm successfully identifies large conserved gene motifs.
    • When applied to cancer and disease outcome datasets, the discovered XMOTIFs effectively differentiate between classes.
    • XMOTIFs provide a powerful new way to analyze and interpret gene expression data.

    Conclusions:

    • Conserved gene expression motifs (XMOTIFs) offer a robust representation for gene expression data.
    • The developed computational technique is effective in discovering biologically relevant motifs.
    • XMOTIFs show significant potential for disease classification and biomarker discovery in genomics.