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

Singapore National Precision Medicine Aging Study

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.

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

Simple Epigenetic Clock

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

epigenetic clock calibration

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.