Profiling epigenetic age in single cells

This program analyzes methylation levels at six CpG sites in the genome of blood cells to produce a prediction of an individual's biological age, using different machine learning and deep learning models.

Code associated with the findings in Duran-Ferrer, Nat Cancer 2020.

Regression models for "epigenetic clock" estimation of canine chronological age

AntiEntropy models aging as stochastic entropy drift in CpG methylation state space. It integrates ElasticNetCV clocks, site-wise Shannon entropy 𝐻 ( 𝛽 ) H(β), PCA spectral decomposition, and HRF-based resonance to quantify negentropy gradients and simulate control-driven reversal toward low-entropy attractors.

Epigenetic age prediction from DNA methylation data using elastic net regression (Horvath clock implementation)

Introduction to machine learning with tidymodels

An R package of placental epigenetic clock to estimate aging by DNA-methylation-based gestational age

We present PathwayAge, a biologically informed, machine learning–based epigenetic clock that integrates pathway-level biological information to predict biological age and quantify disease-related aging acceleration.

Singapore National Precision Medicine Aging Study

Simple Epigenetic Clock

Poster presentation at the (American Society of Human Genetics) ASHG Virtual Meeting, 2021.

epigenetic clock calibration