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 Video

Updated: Jun 19, 2026

Performing Data Mining And Integrative Analysis Of Biomarker in Breast Cancer Using Multiple Publicly Accessible Databases
07:41

Performing Data Mining And Integrative Analysis Of Biomarker in Breast Cancer Using Multiple Publicly Accessible Databases

Published on: May 17, 2019

Identification of functionally related genes using data mining and data integration: a breast cancer case study.

Ettore Mosca1, Gloria Bertoli, Eleonora Piscitelli

  • 1Istituto Tecnologie Biomediche, Consiglio Nazionale Ricerche, Via Fratelli Cervi 93, Segrate (MI), Italy. ettore.mosca@itb.cnr.it

BMC Bioinformatics
|October 16, 2009
PubMed
Summary

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

CRISPR/Cas9-mediated <i>PHOX2B</i> functional knock-out in IMR32 neuroblastoma cells impairs neuronal excitability through dysregulation of ion channels genes.

Frontiers in physiology·2026
Same author

How deep is deep enough? The importance of the detection limits of the CML minimal residual disease gDNA assay.

Cell death discovery·2026
Same author

Ferroptosis-induced oxidative stress in therapy-resistant glioblastoma.

Cell death discovery·2026
Same author

Hyperspectral imaging and healthy aging: an observational study using hand skin as surface for monitoring healthy aging processes.

Biogerontology·2026
Same author

TMEM230 and mitochondrial regulation in Alzheimer's disease.

International review of neurobiology·2026
Same author

Dual actionability of BMI1 activation and mitotic vulnerability defines adaptive Osimertinib resistance in EGFR-mutant NSCLC.

Cell death & disease·2026

This study introduces a novel method to identify interacting genes regulating cell function by analyzing gene expression profiles. Experimental validation confirmed predictions for TBX3 target genes, advancing our understanding of gene regulation.

Area of Science:

  • Computational Biology
  • Genomics
  • Molecular Biology

Background:

  • Understanding molecular pathways is key to cell function.
  • Identifying interacting genes is crucial for reconstructing regulatory pathways.
  • Publicly available transcriptome and proteome data offer valuable resources for biological research.

Purpose of the Study:

  • To develop and validate a computational approach for identifying functionally related genes.
  • To predict novel gene interactions and regulatory relationships.
  • To enhance the understanding of cell function through data mining and integration.

Main Methods:

  • Analysis of gene expression profile similarity across large datasets.
  • Identification of significant sample subsets with high gene correlation.

Related Experiment Videos

Last Updated: Jun 19, 2026

Performing Data Mining And Integrative Analysis Of Biomarker in Breast Cancer Using Multiple Publicly Accessible Databases
07:41

Performing Data Mining And Integrative Analysis Of Biomarker in Breast Cancer Using Multiple Publicly Accessible Databases

Published on: May 17, 2019

  • Exclusion of irrelevant samples in heterogeneous experimental conditions.
  • Characterization of gene partners using Gene Ontology terms and protein-protein interaction (PPI) data.

    • Main Results:

    • A strategy was developed to identify genes with similar expression profiles, excluding irrelevant samples.
    • The approach successfully predicted functional partners for Pyruvate Kinase and the transcription factor TBX3 using breast primary tumor expression data.
    • Integration of PPI data confirmed the functional relationships of predicted genes.
    • In vivo binding assays (X-ChIP) experimentally validated two predicted TBX3 target genes: GLI3 and GATA3.

    Conclusions:

    • The developed strategy effectively identifies genes with functional relationships by analyzing expression profiles and integrating public data.
    • This method aids in understanding gene regulation and cell function.
    • Experimental confirmation of TBX3 target genes GLI3 and GATA3 validates the approach's predictive power.