Protein Signatures of APOE2 and Cognitive Aging

改善R 01下生成的数据的AI/ML准备：APOE 2和 AG 061844“APOE 2和认知衰老的蛋白质特征”，我们 正在生成一组百岁老人、他们的后代和无关的蛋白质组学和代谢组学数据， 对照组来自新英格兰百岁老人研究（NECS）。研究参与者的特征是 详细的病史、遗传概况以及身体和认知功能的纵向评估。的 亲本R 01的目标是验证APOE基因型的蛋白质组特征，并评估其价值 与代谢谱一起预测衰老个体中认知功能变化的模式。我们计划 通过阿尔茨海默病（AD）门户网站和新的极端长寿（EL）门户网站共享数据， 目前正在开发中。以无限制的方式共享数据是不可能的，因为它们包括 HIPAA标识符，特别是年龄>89。无限制的数据共享将是AI/ML的一个有吸引力的选择 调查员，这一行政补充申请的目的是 认知老化由NIA资助：R 01 使用高级机器学习 技术来生成在父节点中获得的数据的高保真、隐私保护、合成版本 机器学习方法已经出现，可以用来 使用在所述真实的数据中训练的模型来生成合成数据。该模型可用于生成 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实的人，但是 可以分析合成数据以产生类似于从原始数据导出的结果的结果。这 在过去的几年里，这种方法受到了很大的关注，它被用来妥协 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（N3 C）。我们组建了一个由Syntegra公司的数据科学家和合作伙伴组成的团队 R 01，因此它们可以不受限制地共享。M , 以生成并验证与利用父R 01生成的数据相匹配的合成数据集。我们 该提案有三个目标。在目标1中，我们将与Syntegra共享来自NECS的真实的数据，包括 蛋白质组学和代谢组学、遗传变量和患者特征，包括 认知功能该真实的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 制定综合数据集验证方案，包括 忠实于各种 机器学习分析的结果 以及评估数据去识别化的度量。在目标3中， 在真实的数据集和合成数据集上进行分析并比较结果。 冲击这是一个高风险， 但潜在的高回报提案。如果这种方法有效，我们将能够生成可以广泛使用的数据。 与社区分享。该方法也将适用于其他几项关于衰老的研究， 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.