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

Discovering significant and interpretable patterns from multifactorial DNA microarray data with poor replication.

Ju Han Kim1, Dooil Jeoung, Seongeun Lee

  • 1Seoul National University Biomedical Informatics, Seoul 110-799, Republic of Korea.

Journal of Biomedical Informatics
|October 7, 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

How to Employ Trained Immunity and Trained Immunity-Based Vaccines to Inhibit Allergic Inflammation.

Vaccines·2026
Same author

Crosstalk Between Allergic Inflammation and Autophagy.

International journal of molecular sciences·2025
Same author

S1PR2-miR-212 feedback loop regulates allergic reactions.

Molecular immunology·2025
Same author

Circular RNAs as Targets for Developing Anticancer Therapeutics.

Cells·2025
Same author

Rocaglamide Suppresses Allergic Reactions by Regulating IL-4 Receptor Signaling.

Molecules (Basel, Switzerland)·2025
Same author

Regulating Immune Responses Induced by PEGylated Messenger RNA-Lipid Nanoparticle Vaccine.

Vaccines·2025
Same journal

Evaluation of temporal preservation in synthetic longitudinal patient data.

Journal of biomedical informatics·2026
Same journal

ARKE: An ontology-driven framework for automated mapping of local radiology procedure terms to the LOINC-RadLex playbook using large language model.

Journal of biomedical informatics·2026
Same journal

A validation-driven training controller for cross-lingual biomedical NER via reinforcement learning-based adaptive loss weighting.

Journal of biomedical informatics·2026
Same journal

ASP-HR: An Adaptive Spatial Perception and Hierarchical Reasoning mechanism for document-level biomedical relation extraction.

Journal of biomedical informatics·2026
Same journal

Beyond Accuracy: Safety-Centered guidelines for the evaluation of LLM-based therapy recommendation systems for chronic multimorbidity patients.

Journal of biomedical informatics·2026
Same journal

DeepEN: A deep reinforcement learning framework for personalized enteral nutrition in critical care.

Journal of biomedical informatics·2026
See all related articles

A novel genetic algorithm identifies significant gene expression profiles in complex biological data. This method simplifies interpretation and enables network analysis without costly replication, benefiting large-scale data integration.

Area of Science:

  • Bioinformatics
  • Systems Biology
  • Statistical Genetics

Background:

  • Multivariate analyses are powerful for testing multiple variables and interactions.
  • Factorial designs with many factors/levels often require costly replication.
  • Standard methods like ANOVA can yield complex interpretations for higher-order interactions.

Purpose of the Study:

  • To develop a method for identifying statistically significant gene expression profiles.
  • To enable straightforward biological interpretation of gene expression data.
  • To facilitate the analysis of complex biological data without replication.

Main Methods:

  • A genetic algorithm was developed to find factor-specific generative patterns (FSGPs) for gene expression profiles.
  • Distance measures between expression profiles and nearest FSGPs were calculated.

Related Experiment Videos

  • Permutation testing was used to identify statistically significant gene profiles.
  • Association networks were constructed using tripartite graphs.

    • Main Results:

    • The genetic algorithm successfully identified nearest FSGPs for gene expression profiles.
    • Permutation testing identified statistically significant gene profiles with straightforward biological interpretations.
    • Association networks revealed interpretable relations between genes, drugs, and cell lines.
    • The method was applied to a microarray experiment of gastric-cancer cell lines with a factorial design and no replication.

    Conclusions:

    • The proposed method provides a cost-effective approach to analyze complex gene expression data.
    • It enables the identification of significant biological patterns and facilitates network construction.
    • This approach is beneficial for analyzing heterogeneous expression data from public repositories.