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

Searching for differentially expressed gene combinations.

Marcel Dettling1, Edward Gabrielson, Giovanni Parmigiani

  • 1Department of Oncology, Johns Hopkins Medical Institutions, Baltimore, MD 21205, USA. dettling@jhu.edu

Genome Biology
|October 7, 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

Benchmarking reliability and calibration of LLMs for multi-cancer early detection test communication.

JAMIA open·2026
Same author

Pan-Cancer Genomic Scars of Alternative End Joining and Single-Strand Annealing.

bioRxiv : the preprint server for biology·2026
Same author

Multivariate causal effects: a Bayesian causal regression factor model.

Biometrics·2026
Same author

A Longitudinal Comprehensive Biospecimen and Clinical Data Repository for Cancer Early Detection: The InAdvance Study.

Cancer epidemiology, biomarkers & prevention : a publication of the American Association for Cancer Research, cosponsored by the American Society of Preventive Oncology·2026
Same author

A Gene Expression Tumor Signature Optimizing Partial Area-Under-the-Curve (pAUC) to Improve Specificity for Indolent Prostate Cancer.

The Prostate·2026
Same author

Web-Based User Interface for Fam3PRO: A Multigene, Multicancer Risk Prediction Model for Families With Cancer History.

JCO clinical cancer informatics·2026
Same journal

Integrated lipidomic and transcriptomic profiling of the host response in human malaria.

Genome biology·2026
Same journal

Centromeric satellite expansion drives genome evolution in the snowy owl.

Genome biology·2026
Same journal

Mapping the landscape of allele-specific expression in porcine genomes.

Genome biology·2026
Same journal

Genomic sequence evolution underlying human neocortical interareal diversification.

Genome biology·2026
Same journal

Regulatory mechanisms driven by functional 3'-UTR variants in alcohol use disorder and related traits.

Genome biology·2026
Same journal

A longitudinal single-nucleus transcriptomic atlas of bovine placentation reveals dynamic cellular hierarchies and regulatory programs.

Genome biology·2026
See all related articles

We introduce CorScor, a new method to find gene pairs with joint differential expression, revealing hidden phenotype-gene interactions. This approach effectively identifies dependencies crucial for understanding complex biological systems.

Area of Science:

  • Genomics
  • Bioinformatics
  • Systems Biology

Background:

  • Identifying gene interactions is crucial for understanding complex diseases.
  • Current methods may miss genes with joint but not marginal differential expression.
  • Phenotype-gene relationships require advanced analytical approaches.

Purpose of the Study:

  • To introduce CorScor, a novel computational method.
  • To identify gene pairs exhibiting joint differential expression.
  • To detect phenotype-related gene dependencies and interactions.

Main Methods:

  • CorScor quantifies joint differential expression.
  • It assesses phenotype discrimination in bivariate versus marginal gene distributions.
  • The approach is designed for scalability with high-dimensional microarray data.

Related Experiment Videos

Main Results:

  • CorScor successfully identifies gene pairs with joint differential expression.
  • The method demonstrates effectiveness in detecting phenotype-gene dependencies.
  • Promising results were observed across multiple cancer gene-expression datasets.

Conclusions:

  • CorScor offers a novel and interpretable approach for gene interaction analysis.
  • The method is scalable and applicable to large-scale gene expression data.
  • CorScor aids in uncovering complex biological relationships relevant to cancer research.