CITATION.cff

cff-version: 1.2.0
title: "Closing the Gap: Epigenetic Age Prediction with ElasticNet and Public Data"
message: "If you use this work (paper, figures, code, or findings), please cite it as below."
type: software
authors:
  - family-names: "El Ouahabi"
    given-names: "Maher"
    email: "cofounders@echowi.ai"
    affiliation: "EchoAI Global"
repository-code: "https://github.com/maher-coder/epigenetic-clock"
url: "https://github.com/maher-coder/epigenetic-clock"
abstract: >-
  An epigenetic age clock trained on 11,328 publicly available DNA methylation
  samples from 17 GEO cohorts. Using an Optuna-tuned ElasticNet, we achieve
  mean absolute error (MAE) of 3.37 years on an independent EPIC v2 test
  cohort (GSE246337, n=500), closing 53% of the gap to DeepStrataAge — a
  40.7M-parameter deep neural network trained on 29,167 proprietary samples
  (MAE 2.45 years). We show that the remaining performance difference is
  attributable to training data volume rather than model architecture.
  Cross-validation: Leave-One-Cohort-Out across 17 cohorts. Three critical
  data-quality issues in public methylation datasets are documented.
keywords:
  - DNA methylation
  - epigenetic clock
  - biological age
  - ElasticNet
  - public data
  - GEO
  - cross-platform
license: CC-BY-4.0
license-code: MIT
version: "1.0.0"
date-released: "2026-04-13"
preferred-citation:
  type: article
  authors:
    - family-names: "El Ouahabi"
      given-names: "Maher"
      affiliation: "EchoAI Global"
  title: "Closing the Gap: Epigenetic Age Prediction with ElasticNet and Public Data"
  year: 2026
  month: 4
  url: "https://github.com/maher-coder/epigenetic-clock"