Protein Signatures of APOE2 and Cognitive Aging

改进根据R01生成的数据的AI/ML准备情况：APOE2和蛋白质签名 AG061844《APOE2的蛋白质特征与认知老化》，WE 在一群百岁老人及其后代中产生蛋白质组和代谢组学数据 来自新英格兰百岁老人研究(NECS)的对照。研究参与者的特征是 详细的病史、基因特征，以及对身体和认知功能的纵向评估。这个 亲本R01的目的是验证APOE基因类型的蛋白质组特征，并评估其价值 结合代谢特征预测老年个体认知功能变化的模式。我们计划 通过阿尔茨海默病(AD)门户和新的极长寿(EL)门户共享数据 目前正在开发中。不可能以不受限制的方式共享数据，因为它们包括 HIPAA标识符，特别是AGE&>89。对于AI/ML来说，无限制地共享数据将是一个有吸引力的选择 调查人员，这项行政补充请求的目的是 认知老化。由NIA资助：R01 使用高级机器学习 生成从父代获得的数据的高保真、保护隐私的合成版本的技术 机器学习方法已经出现，可以用来 使用在真实数据中训练的模型生成合成数据。此模型可用于生成 合成数据集，其中没有单个数据点对应于原始数据集中的真人，但 可以对合成数据进行分析，以产生与原始数据类似的结果。这 在过去的几年里，这种方法受到了很大的关注，并已被采用来折衷 数据共享和隐私之间的关系，包括为国家COVID队列生成合成数据 协作型(N3C)。我们已经组建了一个由数据科学家和来自Syntiga公司的合作伙伴组成的团队 R01，因此可以不受限制地共享它们。M ， 生成并验证与父R01生成的数据相匹配的合成数据集。我们的 提案由三个目标构成。在目标1中，我们将与Syntiga共享来自NECS的真实数据，包括 蛋白质组学和代谢组学，遗传变量和患者的特征，包括评估 认知功能。这些真实数据将用于训练数据生成模型并创建合成数据集。 在目标2中，我们将 制定一项用于验证合成数据集的协议，包括 忠于各种 机器学习分析的结果 以及用于评估数据识别的指标。在《目标3》中，我们将 在真实数据集和合成数据集中进行分析，并比较结果。 冲击力。这是一种高风险， 但潜在的高回报提议。如果这种方法奏效，我们将能够生成可广泛使用的数据 与社区共享。这一方法也将适用于其他几项关于衰老的斗争研究 数据共享的问题，以加强科学研究，同时保护隐私。

Improving AI/ML readiness of data generated under the R01: Protein signatures of APOE2 and AG061844 “Protein signatures of APOE2 and cognitive aging”, we are generating proteomic and metabolomics data in a cohort of centenarians, their offspring, and unrelated controls from the New England Centenarian Study (NECS). Study participants have been characterized with detailed medical history, genetic profiles, and longitudinal assessment of physical and cognitive functions. The goal of the parent R01 is to validate a proteomic signature of APOE genotypes, and to evaluate its value together with metabolic profiles to predict patterns of cognitive function change in aging individuals. We plan to share data through the Alzheimer’s disease (AD) portal, and the new extreme longevity (EL) portal that is currently under development. Sharing the data in an unrestricted manner is not possible because they include HIPAA identifiers, particularly age >89. Unrestricted sharing of data would be an attractive option for AI/ML investigators, and the goal of this request for administrative supplement is to cognitive aging. Funded by the NIA: R01 use advanced machine learning techniques to generate high-fidelity, privacy-preserving, synthetic versions of the data obtained in the parent achine learning methods have emerged that can be used to generate synthetic data using a model that is trained in the real data. This model can be used to generate a synthetic data set in which no single data point corresponds to a real person in the original data set, but the synthetic data can be analyzed to produce results that are like those derived from the original data. This approach has received substantial attention in the past few years, and it has been adopted to compromise between data sharing and privacy, including generation of synthetic data for the National COVID Cohort Collaborative (N3C). We have put together a team of data scientists and partners from the company Syntegra R01 so they can be shared without restriction. M , to generate and validate a synthetic data set that matches the data generated with the parent R01. Our proposal is structured in three aims. In Aim 1, we will share with Syntegra real data from the NECS that include proteomics and metabolomics, genetic variables and patients’ characteristics including assessment of cognitive function. This real data will be used to train the data generation model and create synthetic data sets. In Aim 2 we will d evelop a protocol for validation of the synthetic data sets that includes fidelity to a variety of results of machine learning analyses and metrics to assess the deidentification of data. In Aim 3 we will conduct the analysis in the real and synthetic data sets and compare the results. Impact. This is a high risk, but potentially high return proposal. If the approach works, we will be able to generate data that can be widely shared with the community. The approach will also be applicable to several other studies of aging that struggle with the issues of data sharing to enhance scientific research while preserving privacy.