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

Topology-based cancer classification and related pathway mining using microarray data.

Chun-Chi Liu1, Wen-Shyen E Chen, Chin-Chung Lin

  • 1Department of Computer Science, National Chung-Hsing University, Taichung, Taiwan, ROC.

Nucleic Acids Research
|August 18, 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

Artificial Intelligence Models for Classifying Wrist Ligament Injuries Using Synthetically-Generated Joint Proximity Maps from Finite Element Models.

bioRxiv : the preprint server for biology·2026
Same author

Methylation plus alpha-fetoprotein blood test for early detection of HCC in at-risk populations.

Hepatology communications·2026
Same author

Navigating luminal heterogeneity: etiology-based proteogenomic subtyping for targeted treatment strategies in breast cancer.

Molecular cancer·2026
Same author

Plasma signals of lung tumor promotion for molecular cancer prevention.

Cell·2026
Same author

The IMPACT framework for evaluating generative AI in critical care: development and multinational consensus validation.

Annals of intensive care·2026
Same author

Consensus statement on the application of artificial intelligence in osteoporosis screening and management: perspectives from the Asia-Pacific region.

Osteoporosis international : a journal established as result of cooperation between the European Foundation for Osteoporosis and the National Osteoporosis Foundation of the USA·2026

This study introduces a novel gene network framework for cancer classification and pathway analysis. Ordering networks improve classification accuracy and stability, offering new insights into cancer mechanisms.

Area of Science:

  • Bioinformatics
  • Computational Biology
  • Cancer Research

Background:

  • Cancer classification is crucial for personalized therapy.
  • Pathway analysis using microarray data aids in understanding cancer mechanisms.
  • Integrating molecular classification, pathway analysis, and gene networks remains underexplored.

Purpose of the Study:

  • To develop a novel framework for cancer classification and pathway analysis using cancer class-specific gene networks.
  • To introduce a new gene network construction method called ordering network.
  • To identify cancer class-specific pathways and elucidate tumorigenesis mechanisms.

Main Methods:

  • Developed a novel framework integrating cancer class-specific gene networks for classification and pathway analysis.

Related Experiment Videos

  • Constructed a new type of gene network, the 'ordering network', exhibiting power-law node-degree distribution.
  • Applied topology-based pathway analysis by integrating ordering networks, classification data, and pathway databases.

    • Main Results:

    • Ordering networks demonstrated superior accuracy and stability in cancer classification compared to correlation networks across five public datasets.
    • The topology-based pathway analysis successfully identified cancer class-specific pathways.
    • The framework effectively characterized biochemical pathways associated with subtle gene expression changes.

    Conclusions:

    • The developed topology-based classification technology accurately distinguishes cancer subclasses.
    • Topology-based pathway analysis provides insights into the biological significance of cancer and underlying molecular mechanisms of tumorigenesis.
    • This approach offers a novel method for understanding cancer development and identifying therapeutic targets.