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 Concept Videos

You might also read

Related Articles

Articles linked to this work by shared authors, journal, and citation graph.

Sort by
Same author

Spatially defined microenvironmental niches are associated with clinical outcome and tumor ecosystem diversity in head and neck cancer.

Med (New York, N.Y.)·2026
Same author

Combined Analysis of Bulk and Single-Cell Transcriptomic Data Reveals Dormancy-Associated Genes in Colorectal Cancer.

International journal of molecular sciences·2026
Same author

Identification and Characterization of Cancer-Related Risk Metabolic Subpathways Reveal Their Functional Significance in Cancer.

International journal of molecular sciences·2026
Same author

<i>SF3B1</i> <sup>K700E</sup>-driven transcriptional alterations in hematopoietic progenitors underlie blood cancer pathophysiology.

Genes & diseases·2026
Same author

Association of Intratumoral Microbiota with Prognosis in Head and Neck Squamous Cell Carcinoma.

Clinical cancer research : an official journal of the American Association for Cancer Research·2026
Same author

Charting cell-type-specific positive genetic interaction at single-cell resolution for lung adenocarcinoma.

NPJ precision oncology·2026

Related Experiment Video

Updated: Dec 30, 2025

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

9.4K

Network-based integration method for potential breast cancer gene identification.

Yue Zhang1, Wan Li1, Yihua Zhang1

  • 1College of Bioinformatics Science and Technology, Harbin Medical University, Harbin, Heilongjiang, China.

Journal of Cellular Physiology
|January 17, 2020
PubMed
Summary
This summary is machine-generated.

A new network-based method effectively identifies potential breast cancer genes. This approach aids in understanding the genetic basis of breast cancer for improved diagnosis and treatment.

Keywords:
breast cancergene prioritization algorithmnetwork with weighted vertexes and edgesnetwork-based integration methodpotential breast cancer genes

More Related Videos

Integration of Bioinformatics Approaches and Experimental Validations to Understand the Role of Notch Signaling in Ovarian Cancer
09:08

Integration of Bioinformatics Approaches and Experimental Validations to Understand the Role of Notch Signaling in Ovarian Cancer

Published on: January 12, 2020

7.1K
Discovery of Driver Genes in Colorectal HT29-derived Cancer Stem-Like Tumorspheres
06:52

Discovery of Driver Genes in Colorectal HT29-derived Cancer Stem-Like Tumorspheres

Published on: July 22, 2020

6.9K

Related Experiment Videos

Last Updated: Dec 30, 2025

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

9.4K
Integration of Bioinformatics Approaches and Experimental Validations to Understand the Role of Notch Signaling in Ovarian Cancer
09:08

Integration of Bioinformatics Approaches and Experimental Validations to Understand the Role of Notch Signaling in Ovarian Cancer

Published on: January 12, 2020

7.1K
Discovery of Driver Genes in Colorectal HT29-derived Cancer Stem-Like Tumorspheres
06:52

Discovery of Driver Genes in Colorectal HT29-derived Cancer Stem-Like Tumorspheres

Published on: July 22, 2020

6.9K

Area of Science:

  • Genomics
  • Bioinformatics
  • Cancer Research

Background:

  • Breast cancer is a leading cause of cancer-related death globally, particularly among women.
  • Understanding the genetic underpinnings of breast cancer is crucial for developing effective diagnostic and therapeutic strategies.
  • Existing methods for identifying cancer-related genes require refinement for enhanced accuracy and robustness.

Purpose of the Study:

  • To propose and validate a novel network-based integration method for identifying potential breast cancer genes.
  • To compare the efficacy of the proposed gene prioritization algorithm against existing tools like ToppGene and ToppNet.
  • To identify robust candidate genes that can accurately classify tumor and normal samples.

Main Methods:

  • A network-based integration method was employed, utilizing a gene prioritization algorithm.
  • Gene prioritization involved transferring disease risks across a network with weighted vertices and edges.
  • Candidate genes were identified based on their robustness across multiple prioritization runs.

Main Results:

  • The proposed prioritization algorithm demonstrated superior performance compared to ToppGene and ToppNet.
  • Twenty potential breast cancer genes were identified as common candidates with robust prioritization.
  • These identified genes showed high accuracy in classifying tumor and normal samples across multiple datasets.
  • Eighteen of the identified genes were validated through existing literature, with two novel genes proposed for further investigation.

Conclusions:

  • The developed network-based method is effective and robust for identifying potential breast cancer genes.
  • The identified genes contribute to a deeper understanding of breast cancer's genetic architecture.
  • This research offers valuable insights for the diagnosis and treatment of breast cancer.