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

## TL;DR

This paper introduces MicroBayesAge, a new method for predicting age from DNA microarray data that improves accuracy compared to previous methods.

## Contribution

MicroBayesAge introduces a two-stage training process and age-specific cohorts to enhance age prediction accuracy using microarray data.

## Key findings

- MicroBayesAge provides less biased age predictions compared to linear methods.
- The two-stage process improves prediction accuracy over previous BayesAge versions.
- Sex-specific predictions show improved accuracy for males but not for females.

## Abstract

Certain epigenetic modifications, such as the methylation of CpG sites, can serve as biomarkers for chronological age. Previously, we introduced the BayesAge frameworks for accurate age prediction through the use of locally weighted scatterplot smoothing (LOWESS) to capture the nonlinear relationship between methylation or gene expression and age, and maximum likelihood estimation (MLE) for bulk bisulfite and RNA sequencing data. Here, we introduce MicroBayesAge, a maximum likelihood framework for age prediction using DNA microarray data that provides less biased age predictions compared to commonly used linear methods. Furthermore, MicroBayesAge enhances prediction accuracy relative to previous versions of BayesAge by subdividing input data into age-specific cohorts and employing a new two-stage process for training and testing. Additionally, we explored the performance of our model for sex-specific age prediction which revealed slight improvements in accuracy for male patients, while no changes were observed for female patients.

The online version contains supplementary material available at 10.1007/s11357-025-01716-4.