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

Improved scoring of functional groups from gene expression data by decorrelating GO graph structure.

Adrian Alexa1, Jörg Rahnenführer, Thomas Lengauer

  • 1Max-Planck-Institute for Informatics Stuhlsatzenhausweg 85, D-66123 Saarbrücken, Germany. alexa@mpi-sb.mpg.de

Bioinformatics (Oxford, England)
|April 12, 2006
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

Ensuring Quality in Preclinical Research: The Importance of Being Human.

Biometrical journal. Biometrische Zeitschrift·2026
Same author

A review of machine learning in toxicology: current practices and reporting gaps.

Archives of toxicology·2026
Same author

Stage-dependent effects of systemic ASBT inhibition in a cholestasis-induced cholemic nephropathy mouse model.

JHEP reports : innovation in hepatology·2025
Same author

Geno2pheno: recombination detection for HIV-1 and HEV subtypes.

NAR molecular medicine·2025
Same author

Elimination of CDX2 restricts intestinal hybrid differentiation signatures in stem cell-derived hepatocyte-like cells.

Stem cell research & therapy·2025
Same author

Adjustment for day-to-day variability in the estimation of effective concentrations for the assessment of mixture toxicity.

Archives of toxicology·2025

We developed new algorithms to improve gene expression analysis by integrating Gene Ontology (GO) term relationships. These methods better identify relevant biological processes from microarray data compared to existing approaches.

Area of Science:

  • Bioinformatics
  • Computational Biology
  • Genomics

Background:

  • Microarray experiments generate extensive gene expression data requiring interpretation.
  • Current methods score predefined functional gene groups, like those in Gene Ontology (GO), for biological insights.
  • Integrating GO term relationships can enhance the explanatory power of functional enrichment analysis.

Purpose of the Study:

  • To develop novel algorithms for improved GO group scoring in gene expression analysis.
  • To leverage the graph topology of GO to enhance statistical significance calculations.
  • To increase the biological interpretability of microarray data.

Main Methods:

  • Development of two novel algorithms for GO group scoring.
  • Integration of GO term relationships (graph topology) into statistical significance calculations.

Related Experiment Videos

  • Evaluation using real and simulated gene expression datasets.

    • Main Results:

    • The novel algorithms effectively utilize GO graph topology for improved scoring.
    • Both methods successfully eliminate local dependencies between GO terms.
    • New areas within the GO graph, previously undetected, are identified.
    • Simulation studies confirm superior detection of relevant biological terms compared to state-of-the-art methods.

    Conclusions:

    • The developed algorithms offer a significant advancement in analyzing gene expression data.
    • Integrating GO term relationships enhances the identification of biologically relevant processes.
    • These methods provide more accurate and comprehensive functional insights from microarray experiments.