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

Using complexity for the estimation of Bayesian networks.

Peter Salzman1, Anthony Almudevar

  • 1University of Rochester. psalzman@bst.rochester.edu

Statistical Applications in Genetics and Molecular Biology
|October 20, 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

Inference on the Eigenvalues of the Normalized Precision Matrix.

Linear algebra and its applications·2024
Same author

Spectral Bayesian network theory.

Linear algebra and its applications·2023
Same author

The Replica Set Method is a Robust, Accurate, and High-Throughput Approach for Assessing and Comparing Lifespan in <i>C. elegans</i> Experiments.

Frontiers in aging·2022
Same author

Three Innate Cytokine Biomarkers Predict Presence of Acute Otitis Media and Relevant Otopathogens.

Biomarkers and applications·2022
Same author

SIRT3 promotes auditory function in young adult FVB/nJ mice but is dispensable for hearing recovery after noise exposure.

PloS one·2020
Same author

Statistical projection of post-vaccination antibody kinetics between dosing schedules.

Vaccine·2019
Same journal

Annealed variational mixtures for disease subtyping and biomarker discovery.

Statistical applications in genetics and molecular biology·2026
Same journal

Performance of the permutation test approach with base calling errors for detecting changes in variant allele frequencies in ctDNA for a single patient.

Statistical applications in genetics and molecular biology·2026
Same journal

BLOG: Bayesian longitudinal omics with group constraints.

Statistical applications in genetics and molecular biology·2026
Same journal

AI-driven risk prediction and categorization in cystic fibrosis leveraging AttentiveLSTM and Fox Wolf Optimizer.

Statistical applications in genetics and molecular biology·2026
Same journal

Perfect collinearity not created equal: measuring and visualizing the severity of multi-collinearity of modern omics data.

Statistical applications in genetics and molecular biology·2026
Same journal

Corrigendum to: Choice of baseline hazards in joint modeling of longitudinal and time-to-event cancer survival data.

Statistical applications in genetics and molecular biology·2025
See all related articles

This study introduces a method to improve Bayesian Information Criterion (BIC) scoring for biological network reconstruction, reducing spurious edges in gene regulatory networks.

Area of Science:

  • Computational Biology
  • Statistical Modeling
  • Bioinformatics

Background:

  • Graphical models are crucial for reconstructing biological networks, such as gene regulatory interactions.
  • Score-based Bayesian methods assess model uncertainty but often overestimate complexity, leading to spurious network features.
  • Existing scoring techniques like the Bayesian Information Criterion (BIC) can still result in overfitting.

Purpose of the Study:

  • To propose a novel adjustment to BIC-based scoring procedures to mitigate the overestimation of model complexity in graphical models.
  • To reduce the inference of spurious graphical features in biological network reconstruction.
  • To maintain or improve the identification of true network structures.

Main Methods:

  • A two-step adjustment to BIC scoring is proposed.

Related Experiment Videos

  • Step 1: Derive an independent estimate of the model's parametric complexity.
  • Step 2: Modify the BIC score to equate the posterior density's mean parametric complexity with the estimated value, using a Bayesian network model with binary responses.

    • Main Results:

    • The adjusted BIC scoring procedure was applied to test networks and yeast genome gene regulatory data.
    • The number of spurious graph edges inferred was significantly reduced.
    • The identification of true network edges was minimally affected, indicating preserved accuracy.

    Conclusions:

    • The proposed adjustment effectively reduces spurious edges in biological network inference without compromising the detection of true regulatory relationships.
    • This method offers a more reliable approach to statistical inference of graphical models for biological networks.
    • The findings suggest improved accuracy and interpretability of reconstructed biological networks.