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

A Bayesian network classification methodology for gene expression data.

Paul Helman1, Robert Veroff, Susan R Atlas

  • 1Computer Science Department, University of New Mexico, Albuquerque, NM 87131, USA. helman@cs.unm.edu

Journal of Computational Biology : a Journal of Computational Molecular Cell Biology
|December 8, 2004
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

Information encoding in spherical DFT.

The Journal of chemical physics·2026
Same author

Challenges in the return of molecular tumor profiling results.

Journal of the National Cancer Institute·2025
Same author

Optimizing participant and community engagement in cancer genomic sequencing research.

Genetics in medicine : official journal of the American College of Medical Genetics·2025
Same author

Reply to: Accurate Determinants of Outcome in ALL.

Journal of clinical oncology : official journal of the American Society of Clinical Oncology·2024
Same author

Genomic Determinants of Outcome in Acute Lymphoblastic Leukemia.

Journal of clinical oncology : official journal of the American Society of Clinical Oncology·2024
Same author

Priorities to Promote Participant Engagement in the Participant Engagement and Cancer Genome Sequencing (PE-CGS) Network.

Cancer epidemiology, biomarkers & prevention : a publication of the American Association for Cancer Research, cosponsored by the American Society of Preventive Oncology·2023
Same journal

GMSA: A Graph Matching and Point Cloud Registration-Based Method for Spatial Transcriptomics Data Alignment.

Journal of computational biology : a journal of computational molecular cell biology·2026
Same journal

Investigations on Multiple Protein Scaffold Filling.

Journal of computational biology : a journal of computational molecular cell biology·2026
Same journal

Cell Type Prediction for Single-Cell RNA Sequencing Utilizing Unsupervised Domain Adaptation and Semi-Supervised Learning.

Journal of computational biology : a journal of computational molecular cell biology·2026
Same journal

PPIGAN: Prediction of Protein-Protein Interactions Using Generative Adversarial Networks.

Journal of computational biology : a journal of computational molecular cell biology·2026
Same journal

Deep Structure-Enhanced Cell Clustering Model for Single-Cell RNA Sequencing Data.

Journal of computational biology : a journal of computational molecular cell biology·2026
Same journal

Asymmetric Drug-Drug Interaction Prediction Based on Generative Adversarial Networks and Knowledge Graph.

Journal of computational biology : a journal of computational molecular cell biology·2026
See all related articles

We developed a novel Bayesian network framework for gene expression data classification, achieving over 90% accuracy. This approach simplifies learning by focusing on gene subnetworks relevant to clinical classes.

Area of Science:

  • Bioinformatics
  • Computational Biology
  • Machine Learning

Background:

  • Gene expression data classification is complex.
  • Existing Bayesian network models may not be optimal for this domain.

Purpose of the Study:

  • To present a new Bayesian network learning framework for gene expression data classification.
  • To develop techniques addressing the complexities of learning Bayesian networks in this context.

Main Methods:

  • Reduced the problem to learning multiple subnetworks (class label node and parent genes).
  • Employed greedy and gene selection algorithms for parent set identification.
  • Constructed classifiers from multiple Bayesian network hypotheses.
  • Utilized algorithmic methods for data normalization and binning.

Related Experiment Videos

  • Employed cross-validation and out-of-sample testing.

    • Main Results:

    • Achieved classification rates exceeding 90% on two public datasets.
    • Results are comparable or superior to other methods using stringent train-test protocols.
    • Demonstrated the model's appropriateness for the gene expression domain.

    Conclusions:

    • The proposed Bayesian network framework is effective for gene expression classification.
    • The model aligns with domain knowledge regarding gene-disease relationships.
    • The techniques offer robust classification performance and data handling.