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.

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.

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

Singapore National Precision Medicine Aging Study

Introduction to machine learning with tidymodels

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

computational analysis of aging biomarkers at Harvard Aging Initiative

epigenetic clock calibration

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

Simple Epigenetic Clock

En este repositorio se encuentran los scripts utilizados para el desarrollo del Trabajo Fin de Master en Bioinformática de la Universitat de València.

Do methylation segments outperform sole CpGs? We test it on (1) epigenetic aging clocks on mouse blood with cross-dataset validation, (2) lean-vs-obese separation on human blood. We compare raw CpGs with DBSCAN, HDBSCAN, OPTICS, and ChromHMM-based clustering.