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CAREER: Next-Generation Methods for Statistical Integration of High-Dimensional Disparate Data Sources

职业：高维不同数据源统计集成的下一代方法

基本信息

批准号：
2422478
负责人：
Irina Gaynanova
金额：
$ 40万
依托单位：
Regents of the University of Michigan - Ann Arbor
依托单位国家：
美国
项目类别：
Continuing Grant
财政年份：
2024
资助国家：
美国
起止时间：
2024-03-01 至 2026-05-31
项目状态：
未结题

来源：
https://www.nsf.gov/awardsearch/showAward?AWD_ID=2422478&HistoricalAwards=false
关键词：
CAREER Next Generation Methods Statistical

项目摘要

Multi-view data (collected on the same samples from multiple sources) are increasingly common with advances in multi-omics, neuroimaging and wearable technologies. For example, wearable devices such as physical activity trackers, continuous glucose monitors and ambulatory blood pressure monitors are worn concurrently to provide measurements of distinct subjects’ characteristics. There is enormous potential in integrating that concurrent information from the distinct vantages to better understand between-view associations and improve prediction of health outcomes. Existing tools for data integration are sensitive to outliers, and are not designed for mixed data types (e.g. continuous skewed glucose measurements, zero-inflated activity counts, binary indicators of sleep/wake). The PI will develop a more robust framework for multi-view data integration that is better able to account for outliers, better match the mixed types of data actually collected, and be more accurate in separating common from view-specific signals. The new methods will be implemented in open-source software accompanied by reproducible workflow examples, providing immediate and easy access for other researchers. The educational component centers on the development of structured research experiences (SRE) for students. SRE enhances students written communication, software development and reproducible research skills, all of which are lacking in traditional curriculum. This will improve students’ preparation for conducting research, and widen their STEM employment opportunities. The involvement of students from traditionally underrepresented groups will positively impact their retention rate and will broaden the participation of underrepresented groups in STEM.Popular dimension reduction methods, such as principal component analysis and discriminant analysis, are tailored for single-view data, and thus fail to discover coordinated multi-view signals on a global level. On the other hand, existing multi-view dimension reduction methods suffer from reliance on the Gaussianity assumption, an inability to capture joint functional signals, and a lack of theoretical guarantees. The PI will address these drawbacks by (i) developing a joint dimension reduction framework for skewed continuous, binary and zero-inflated view types; (ii) a joint dimension reduction framework for mixed functional multi-view data and (iii) a new paradigm for simultaneous extraction of signals across views based on hierarchical low-rank constraints. This work will lead to critically needed new statistical methods for data integration with direct relevance for researchers working with wearable monitors, microbiome and multi-omics data through interdisciplinary collaborations of the PI. The proposed structured research experiences will center on the design and reproducibility of simulations studies, and align with computational components of the proposed research, including direct students’ involvement in multiple simulation studies.This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

随着多组学、神经成像和可穿戴技术的进步，多视图数据（从多个来源收集的相同样本）越来越常见。例如，可穿戴设备（诸如身体活动跟踪器、连续葡萄糖监测器和动态血压监测器）被同时穿戴以提供不同对象的特性的测量。整合来自不同优势的并发信息以更好地理解视图间关联并改善健康结果的预测具有巨大的潜力。现有的数据整合工具对离群值敏感，并且不是针对混合数据类型（例如，连续偏斜葡萄糖测量、零膨胀活动计数、睡眠/清醒的二进制指标）设计的。PI将为多视图数据集成开发一个更强大的框架，该框架能够更好地解释离群值，更好地匹配实际收集的混合类型的数据，并更准确地分离常见的视图特定信号。新方法将在开源软件中实施，并伴有可重复的工作流程示例，为其他研究人员提供即时和轻松的访问。教育部分集中在学生的结构化研究经验（SRE）的发展。SRE提高了学生的书面交流，软件开发和可复制的研究技能，所有这些都是传统课程所缺乏的。这将改善学生进行研究的准备，并扩大他们的STEM就业机会。传统上代表性不足的群体的学生的参与将积极影响他们的留存率，并将扩大代表性不足的群体在STEM的参与。流行的降维方法，如主成分分析和判别分析，是专为单视图的数据，从而无法发现协调的多视图信号在全球范围内。另一方面，现有的多视图降维方法存在依赖于高斯性假设、无法捕获联合功能信号以及缺乏理论保证等问题。PI将通过以下方式解决这些缺点：（i）为偏斜连续、二进制和零膨胀视图类型开发联合降维框架;（ii）为混合功能多视图数据开发联合降维框架;以及（iii）基于分层低秩约束的跨视图同时提取信号的新范例。这项工作将导致迫切需要的新的统计方法，用于数据集成，与通过PI的跨学科合作使用可穿戴式监测器，微生物组和多组学数据的研究人员直接相关。建议的结构化研究经验将集中在模拟研究的设计和可重复性，并与建议研究的计算部分保持一致，包括直接让学生参与多个模拟研究。该奖项反映了NSF的法定使命，并通过使用基金会的智力价值和更广泛的影响审查标准进行评估，被认为值得支持。

项目成果

期刊论文数量（5）

专著数量（0）

科研奖励数量（0）

会议论文数量（0）

专利数量（0）

Gluformer: Transformer-based Personalized glucose Forecasting with uncertainty quantification

Gluformer：基于 Transformer 的个性化血糖预测，具有不确定性量化

DOI：
10.1109/icassp49357.2023.10096419
发表时间：
2023
期刊：
IEEE
影响因子：
0
作者：
Sergazinov, Renat;Armandpour, Mohammadreza;Gaynanova, Irina
通讯作者：
Gaynanova, Irina

A Case Study of Glucose Levels During Sleep Using Multilevel Fast Function on Scalar Regression Inference

使用标量回归推理上的多级快速函数进行睡眠期间血糖水平的案例研究

DOI：
10.1111/biom.13878
发表时间：
2023
期刊：
Biometrics
影响因子：
1.9
作者：
Sergazinov, Renat;Leroux, Andrew;Cui, Erjia;Crainiceanu, Ciprian;Aurora, R. Nisha;Punjabi, Naresh M.;Gaynanova, Irina
通讯作者：
Gaynanova, Irina

Sensing the impact of extreme heat on physical activity and sleep