Transfer learning and uncertainty quantification in epigenetic clocks

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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.