# Transfer learning and uncertainty quantification in epigenetic clocks

> **NIH NIH R21** · RUTGERS BIOMEDICAL AND HEALTH SCIENCES · 2024 · $156,658

## Abstract

PROJECT SUMMARY / ABSTRACT
The objective of the proposed research is to develop a transfer learning framework to
refine existing DNA methylation (DNAm) clocks and provide uncertainty quantification
along with age prediction. A growing body of evidence has shown that the DNAm levels
at specific age-related CpG sites represent stable and reproducible biomarkers of age.
While numerous epigenetic clocks have been constructed to predict chronological age,
age acceleration, or age-related diseases, they may lack generality or adaptability as
they are mostly developed and validated from a certain subpopulation. There exists little
research that explores how to transfer knowledge learned from the existing epigenetic
clocks trained with adults when new data are collected from a different subpopulation
such as the children and adolescent cohorts. This project will develop an innovative
transfer learning approach to update existing epigenetic clocks without re-accessing
individual-level data in the original training data. In addition, most existing epigenetic
clocks provide only point predictions without uncertainty quantification. However,
reasonable accuracy on a fixed validation set is not enough, and this project will also
extend the conformal inference framework to construct prediction intervals with a
guaranteed confidence level. Such a predictive paradigm with uncertainty quantification
is important because it communicates better with the science community by providing a
set of plausible predicted outcomes that covers the ground truth with a certain
probability. Furthermore, this proposed work will allow for an integration of sequential
updating and adaptive calibration procedures that construct prediction intervals with the
availability of rich new DNAm datasets collected from diverse populations. The
completion of this proposal will greatly improve the generalizability, reliability, and
adaptability of existing epigenetic clocks to a new target population such as the children
and adolescent cohort.

## Key facts

- **NIH application ID:** 10933453
- **Project number:** 5R21AG083364-02
- **Recipient organization:** RUTGERS BIOMEDICAL AND HEALTH SCIENCES
- **Principal Investigator:** Lan Luo
- **Activity code:** R21 (R01, R21, SBIR, etc.)
- **Funding institute:** NIH
- **Fiscal year:** 2024
- **Award amount:** $156,658
- **Award type:** 5
- **Project period:** 2023-09-30 → 2026-05-31

## Primary source

NIH RePORTER: https://reporter.nih.gov/project-details/10933453

## Citation

> US National Institutes of Health, RePORTER application 10933453, Transfer learning and uncertainty quantification in epigenetic clocks (5R21AG083364-02). Retrieved via AI Analytics 2026-05-26 from https://api.ai-analytics.org/grant/nih/10933453. Licensed CC0.

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