Transfer learning and uncertainty quantification in epigenetic clocks

表观遗传时钟中的迁移学习和不确定性量化

• 批准号: 10722417
• 负责人: Lan Luo
• 金额: $ 17.09万
• 依托单位: RUTGERS BIOMEDICAL AND HEALTH SCIENCES
• 依托单位国家: 美国
• 财政年份: 2023
• 资助国家: 美国
• 起止时间: 2023-09-30 至 2025-05-31
• 项目状态: 未结题
• 来源: https://reporter.nih.gov/project-details/10722417
• 关键词: Acceleration; Adolescent; Adult; Age; Aging; Algorithms; Biological Aging; Biological Markers; Biological Process; Birth; Calibration; Cells; Cessation of life; Child; Chronology; Communication; Communities; Computer software; Confidence Intervals; DNA Methylation; Data; Data Set; Data Sources; Decision Making; Disease; Educational Status; Elasticity; Environmental Risk Factor; Epigenetic Process; Evaluation; Exhibits; Exposure to; Goals; Health; Heterogeneity; Human; Impaired cognition; Individual; Intervention; Knowledge; Learning; Linear Regressions; Methods; Modeling; Outcome; Performance; Population Heterogeneity; Predisposition; Probability; Procedures; Process; Reproducibility; Research; Research Project Grants; Risk Factors; Science; Site; Specific qualifier value; Target Populations; Tissues; Training; Uncertainty; Update; Validation; Work; age related; clinical decision-making; cohort; design; epigenome; ethnic diversity; healthspan; heterogenous data; high dimensionality; improved; innovation; inter-individual variation; outcome prediction; predictive modeling; prevent; racial diversity; study population; transfer learning

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.

项目总结/摘要 本研究的目的是建立一个迁移学习框架， 完善现有的DNA甲基化（DNAm）时钟，并提供不确定性量化 沿着还有年龄预测。越来越多的证据表明， 在特定的年龄相关的CpG位点上的基因突变代表了稳定的和可重复的年龄生物标志物。 虽然已经构建了许多表观遗传时钟来预测实际年龄， 年龄加速，或与年龄有关的疾病，他们可能缺乏普遍性或适应性， 它们大多是从某个亚群中开发和验证的。几乎没有 探索如何转移从现有的表观遗传学中学到的知识的研究 当从不同的亚群收集新数据时，用成年人训练时钟 例如儿童和青少年群体。该项目将开发一种创新的 迁移学习方法更新现有的表观遗传时钟，而无需重新访问 原始训练数据中的个体水平数据。此外，大多数现有的表观遗传 时钟仅提供点预测而没有不确定性量化。然而，在这方面， 合理的精度在一个固定的验证集是不够的，这个项目也将 扩展共形推理框架以构建具有 保证置信水平。这种带有不确定性量化的预测范式 它之所以重要，是因为它通过提供一个 一组看似合理的预测结果，以一定的 概率此外，这项拟议的工作将允许整合的顺序 更新和自适应校准程序，使用 从不同人群中收集的丰富的新DNAm数据集的可用性。的 这一建议的完成将大大提高通用性，可靠性， 现有表观遗传时钟对新目标人群的适应性， 和青少年群体。