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 Video

Updated: May 17, 2026

Quantifying the Brain Metastatic Tumor Micro-Environment using an Organ-On-A Chip 3D Model, Machine Learning, and Confocal Tomography
09:53

Quantifying the Brain Metastatic Tumor Micro-Environment using an Organ-On-A Chip 3D Model, Machine Learning, and Confocal Tomography

Published on: August 16, 2020

Variational Bayes procedure for effective classification of tumor type with microarray gene expression data.

Takeshi Hayashi1

  • 1National Agricultural Research Center.

Statistical Applications in Genetics and Molecular Biology
|November 2, 2012
PubMed
Summary

This study introduces a computationally efficient Bayesian shrinkage regression (BSR) method for tumor classification using gene expression data. The approach accurately identifies tumor types by creating sparse models, outperforming other methods.

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

Haplotype bias detection using pedigree-based transmission simulation: traces of selection that occurred in apple breeding.

Horticulture research·2026
Same author

Giant Intrasellar Aneurysm Presenting with Hypopituitarism.

Internal medicine (Tokyo, Japan)·2026
Same author

Genomic Profile and Resistance to Anti-Epidermal Growth Factor Receptor Antibody in RAS-Amplified Colorectal Cancer: A Study Based on a Japanese Cancer Genome Database.

JCO precision oncology·2026
Same author

Elevated liver enzyme trajectories in early adulthood and persistently high levels predict type 2 diabetes risk in Japanese adults.

Scientific reports·2025
Same author

Codeine as a Potential Drug Against Motor Symptoms in Parkinson's Disease: Report of Two Cases.

Cureus·2025
Same author

Bioinformatic Analysis of Bovine Uterine Extracellular Vesicles miRNA for Detection of Stable Internal Control.

Animal science journal = Nihon chikusan Gakkaiho·2025

Area of Science:

  • Bioinformatics
  • Computational Biology
  • Genomics

Background:

  • Microarrays enable simultaneous measurement of thousands of gene expression levels.
  • Accurate tumor classification requires sparse models to prevent overfitting, especially when sample size is smaller than the number of genes.
  • Bayesian shrinkage estimation offers a method for creating sparse models by zeroing out irrelevant gene effects.

Purpose of the Study:

  • To develop a computationally effective Bayesian shrinkage regression (BSR) method for tumor classification using microarray gene expression data.
  • To incorporate multiple hierarchical structures into the BSR model.
  • To reduce the computational burden of Bayesian estimation through variational approximation.

Main Methods:

  • Bayesian shrinkage regression (BSR) with multiple hierarchical structures.

More Related Videos

Predicting Treatment Response to Image-Guided Therapies Using Machine Learning: An Example for Trans-Arterial Treatment of Hepatocellular Carcinoma
04:09

Predicting Treatment Response to Image-Guided Therapies Using Machine Learning: An Example for Trans-Arterial Treatment of Hepatocellular Carcinoma

Published on: October 10, 2018

Related Experiment Videos

Last Updated: May 17, 2026

Quantifying the Brain Metastatic Tumor Micro-Environment using an Organ-On-A Chip 3D Model, Machine Learning, and Confocal Tomography
09:53

Quantifying the Brain Metastatic Tumor Micro-Environment using an Organ-On-A Chip 3D Model, Machine Learning, and Confocal Tomography

Published on: August 16, 2020

Predicting Treatment Response to Image-Guided Therapies Using Machine Learning: An Example for Trans-Arterial Treatment of Hepatocellular Carcinoma
04:09

Predicting Treatment Response to Image-Guided Therapies Using Machine Learning: An Example for Trans-Arterial Treatment of Hepatocellular Carcinoma

Published on: October 10, 2018

  • Variational approximation method for approximating posterior distributions.
  • Application to microarray gene expression data for tumor classification.

    • Main Results:

    • A computationally efficient BSR procedure was developed.
    • The method constructs a properly sparse model for accurate and rapid tumor classification.
    • Tumor classification accuracy was demonstrated to be equivalent to or better than support vector machine and partial least squares.

    Conclusions:

    • The proposed computationally efficient BSR method enables accurate and rapid tumor classification.
    • Variational approximation significantly reduces computational load in Bayesian estimation.
    • This approach provides a valuable tool for analyzing high-dimensional gene expression data in cancer research.