Search research articles

ABOUT JoVE

Overview Leadership Blog JoVE Help Center

AUTHORS

Publishing Process Editorial Board Scope & Policies Peer Review FAQ Submit

LIBRARIANS

Testimonials Subscriptions Access Resources Library Advisory Board FAQ

RESEARCH

JoVE Journal Methods Collections JoVE Encyclopedia of Experiments Archive

EDUCATION

JoVE Core JoVE Business JoVE Science Education JoVE Lab Manual Faculty Resource Center Faculty Site

Terms & Conditions of Use

Related Concept Videos

DNA Microarrays

DNA Microarrays

Microarrays are high-throughput and relatively inexpensive assays that can be automated to analyze large quantities of data at a time. They are used in genome-wide studies to compare gene or protein expression under two varied conditions, such as healthy and diseased states. Microarrays consist of glass or silica slides on which probe molecules are covalently attached through surface functionalization. Most commonly, the slides are prepared through the chemisorption of silanes to silica...

Epigenetic Regulation

Epigenetic Regulation

Epigenetic changes alter the physical structure of the DNA without changing the genetic sequence and often regulate whether genes are turned on or off. This regulation ensures that each cell produces only proteins necessary for its function. For example, proteins that promote bone growth are not produced in muscle cells. Epigenetic mechanisms play an essential role in healthy development. Conversely, precisely regulated epigenetic mechanisms are disrupted in diseases like cancer.
X-chromosome...

You might also read

Related Articles

Articles linked to this work by shared authors, journal, and citation graph.

Sort by

Same author

Quantitative Assessment of Brain Glucose Metabolism Using Dynamic Glucose-Enhanced Magnetic Resonance Fingerprinting (DGE-MRF).

Chemical & biomedical imaging·2026

Same author

Challenges and recommendations in establishing national human diversity genomic projects.

Nature methods·2026

Same author

Multidisciplinary eConsults to Optimize Safe Opioid Prescribing: A Pilot Program.

Journal of pain & palliative care pharmacotherapy·2026

Same author

Correction to: Searching for shared epigenetic clocks: evaluating ultra‑conserved markers in a de novo genome assembly of the albacore tuna.

GeroScience·2026

Same author

DNA methylation profiles at hospital admission are associated with subsequent severe COVID-19 outcomes.

Clinical epigenetics·2026

Same author

Weak solutions to the Bloch equations with distant dipolar field.

The Journal of chemical physics·2026

Same journal

Homebound status and glycemic control among older adults: results from a national survey.

GeroScience·2026

Same journal

Adipokines and satiety hormones and incidence of Alzheimer's disease and dementia in older adults: a nested case-control study.

GeroScience·2026

Same journal

Longitudinal lipidomic markers of cardiac aging and risk of coronary heart disease in American Indians: the Strong Heart Family Study.

GeroScience·2026

Same journal

The influence of aging on graded peripheral venous return and complex blood flow features through human veins.

GeroScience·2026

Same journal

Age-related differences in motor unit discharge rate modulation across contraction levels.

GeroScience·2026

Same journal

Study protocol for FAXAge: a randomized, controlled clinical trial of fasting and exercise to slow aging in humans.

GeroScience·2026

See all related articles

Search research articles

Related Experiment Video

Updated: Mar 10, 2026

Sample Preparation to Bioinformatics Analysis of DNA Methylation: Association Strategy for Obesity and Related Trait Studies

Sample Preparation to Bioinformatics Analysis of DNA Methylation: Association Strategy for Obesity and Related Trait Studies

Published on: May 6, 2022

MicroBayesAge: a maximum likelihood approach to predict epigenetic age using microarray data.

Nicole Nolan¹, Megan Mitchell², Lajoyce Mboning¹

¹Department of Chemistry and Biochemistry, University of California Los Angeles, Los Angeles, CA, 90095, USA.

|May 30, 2025

Summary

This summary is machine-generated.

We developed MicroBayesAge, a new framework for predicting chronological age using DNA microarray data. This method offers less biased and more accurate age predictions than linear models and previous BayesAge versions.

Keywords:

CpG dinucleotide DNA methylation MicroBayesAge

More Related Videos

Measuring Single-Cell Aging with an Imaging-based Biomarker of Chromatin and Epigenetic Aging

Measuring Single-Cell Aging with an Imaging-based Biomarker of Chromatin and Epigenetic Aging

Published on: January 30, 2026

Genome-Wide Analysis of DNA Methylation in Gastrointestinal Cancer

Genome-Wide Analysis of DNA Methylation in Gastrointestinal Cancer

Published on: September 18, 2020

Related Experiment Videos

Last Updated: Mar 10, 2026

Sample Preparation to Bioinformatics Analysis of DNA Methylation: Association Strategy for Obesity and Related Trait Studies

Sample Preparation to Bioinformatics Analysis of DNA Methylation: Association Strategy for Obesity and Related Trait Studies

Published on: May 6, 2022

Measuring Single-Cell Aging with an Imaging-based Biomarker of Chromatin and Epigenetic Aging

Measuring Single-Cell Aging with an Imaging-based Biomarker of Chromatin and Epigenetic Aging

Published on: January 30, 2026

Genome-Wide Analysis of DNA Methylation in Gastrointestinal Cancer

Genome-Wide Analysis of DNA Methylation in Gastrointestinal Cancer

Published on: September 18, 2020

Area of Science:

Epigenetics
Bioinformatics
Computational Biology

Background:

Epigenetic modifications like CpG methylation are reliable biomarkers for chronological age.
Previous BayesAge frameworks utilized LOWESS and MLE for age prediction from methylation or gene expression data.
Existing linear methods for age prediction from DNA microarray data can be biased.

Purpose of the Study:

Introduce MicroBayesAge, a maximum likelihood framework for age prediction using DNA microarray data.
Improve upon existing age prediction methods by reducing bias and enhancing accuracy.
Evaluate the performance of MicroBayesAge for sex-specific age prediction.

Main Methods:

Developed a maximum likelihood framework (MicroBayesAge) tailored for DNA microarray data.
Implemented a two-stage process for training and testing the model.
Subdivided input data into age-specific cohorts to refine predictions.
Assessed model performance for both general and sex-specific age prediction.

Main Results:

MicroBayesAge provides less biased age predictions compared to traditional linear methods.
The new framework enhances prediction accuracy over previous BayesAge versions.
Sex-specific prediction showed slight accuracy improvements in male patients.
No significant changes in prediction accuracy were observed for female patients.

Conclusions:

MicroBayesAge represents a significant advancement in epigenetic age prediction using DNA microarray data.
The framework's design, incorporating age-specific cohorts and a two-stage process, improves predictive performance.
Further research into sex-specific epigenetic aging patterns may yield additional insights.