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

Bayesian integrated functional analysis of microarray data.

Madhuchhanda Bhattacharjee1, Colin C Pritchard, Peter S Nelson

  • 1Rolf Nevanlinna Institute, Department of Mathematics and Statistics, University of Helsinki, P.O. Box 68, FIN 00014, Helsinki, Finland. mab@rni.helsinki.fi

Bioinformatics (Oxford, England)
|June 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

3D pathology-guided microdissection.

Nature methods·2026
Same author

Beyond Histology: A Unified Transcriptomic Atlas Defines Lung Cancer Biologic States and Subtypes.

bioRxiv : the preprint server for biology·2026
Same author

Establishing Adenoma Testing Guidelines for Diagnosing Mosaicism.

Gastroenterology·2026
Same author

Remote delivery of cancer genetic testing in veterans with metastatic prostate cancer: A Million Veteran Program pilot study.

Cancer·2026
Same author

3D pathology-guided microdissection.

bioRxiv : the preprint server for biology·2025
Same author

CHIMERA-DDR: A Machine Learning Framework for Classifying Heterogeneous Mismatch-Repair and Homologous-Recombination Deficiency Patterns in Prostate Cancer.

bioRxiv : the preprint server for biology·2025
Same journal

3DICE: Interpretable 3D Cross-Modal Learning for Drug-Target Interaction Prediction and Large-Scale Drug Discovery.

Bioinformatics (Oxford, England)·2026
Same journal

KASSPer: Kinase Active Site Structure Prediction using Protein and Ligand Language Models and Its Application to Virtual Screening.

Bioinformatics (Oxford, England)·2026
Same journal

IDR searcher: a search engine solution for public image resources.

Bioinformatics (Oxford, England)·2026
Same journal

KCFtools: Rapid alignment-free method for introgression screening and GWAS using k-mer profiles.

Bioinformatics (Oxford, England)·2026
Same journal

Meta2DB: Curated shotgun metagenomic feature sets and metadata for health state prediction.

Bioinformatics (Oxford, England)·2026
Same journal

conMItion: an R package adjusting confounding factors for associations in multi-omics.

Bioinformatics (Oxford, England)·2026
See all related articles

This study introduces a hierarchical Bayesian method to replace sequential microarray data analysis with a single joint analysis. This approach accounts for uncertainties and integrates functional data, improving statistical and biological results for more reliable gene categorization.

Area of Science:

  • Bioinformatics
  • Statistical Genomics
  • Computational Biology

Background:

  • Traditional microarray data analysis involves sequential steps, where errors can accumulate.
  • Current methods often fail to account for uncertainties in intermediate results, impacting final inferences.

Purpose of the Study:

  • To develop a novel statistical approach for microarray data analysis that addresses error accumulation.
  • To enhance the reliability of biological conclusions by integrating functional information.

Main Methods:

  • Application of hierarchical Bayesian methodology for a single joint analysis.
  • Systematic accounting for uncertainties throughout the analysis process.
  • Integration of functional information from biological databases.

Related Experiment Videos

Main Results:

  • The proposed method replaces sequential analysis with a unified approach, mitigating error propagation.
  • Functional data integration increases the robustness of biological interpretations.
  • Genes were successfully categorized based on biological relevance, demonstrating the method's utility.

Conclusions:

  • The hierarchical Bayesian approach offers significant statistical and biological advantages for microarray data analysis.
  • This method provides a more reliable framework for drawing biological conclusions and exploring gene functions.