权益分类	功能权益	普通用户	{{item.name}}会员
{{category.name}}	{{benefitItem.name}}

Correlated Graphical Models for High-Dimensional Heterogeneous Data: Theory, Optimization, and Applications

高维异构数据的相关图形模型：理论、优化和应用

基本信息

批准号：
2015481
负责人：
Yuping Zhang
金额：
$ 10万
依托单位：
University of Connecticut
依托单位国家：
美国
项目类别：
Standard Grant
财政年份：
2020
资助国家：
美国
起止时间：
2020-08-01 至 2023-07-31
项目状态：
已结题

来源：
https://www.nsf.gov/awardsearch/showAward?AWD_ID=2015481&HistoricalAwards=false
关键词：
Correlated Graphical Models Dimensional Heterogeneous

项目摘要

This project is motivated by the pressing need for analyzing modern high-dimensional heterogeneous data from multiple sources. Driven by high-throughput biotechnologies, it is increasingly common to have multiple types of measurements on the same set of subjects. Integration of heterogeneous data types is the key to gaining fundamental knowledge on biological processes. Complex characteristics of modern biological data also result in challenges for data analysis and statistical modeling. This project will contribute to theoretical and methodological development through novel graphical models suitable for fusing correlated and mixed data from static and dynamic conditions. These approaches have great potential to translate rich data into meaningful knowledge. The new statistical methods and theories will also advance modern statistical science. The resulting products of this project will provide valuable software tools to scientific communities. This research will promote curriculum development, student training, and educational outreach.Multimodal data from different experimental platforms have different properties and characteristics. In many systems, including biological processes, regulation is modularized and temporally dynamic in nature. Common regulatory principles exist in certain related biological conditions. This project will focus on the development of new data integration methods and theories through novel correlated graphical models from a frequentist inference perspective. The methods will be based on exponential Markov random fields beyond the traditional Gaussian assumption. The new methods will allow for network discovery for high-dimensional heterogeneous data from multiple static and dynamic conditions. This project also involves the development of efficient algorithms for complex optimization problems incorporating the structure-inducing regularization mechanism.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.

该项目的动机是分析来自多个来源的现代高维异构数据的迫切需要。在高通量生物技术的推动下，对同一组受试者进行多种类型的测量变得越来越普遍。异构数据类型的集成是获得生物过程基础知识的关键。现代生物数据的复杂特征也给数据分析和统计建模带来了挑战。该项目将通过适合融合静态和动态条件下的相关数据和混合数据的新型图形模型，为理论和方法的发展做出贡献。这些方法具有将丰富的数据转化为有意义的知识的巨大潜力。新的统计方法和理论也将推动现代统计科学的发展。该项目的最终产品将为科学界提供有价值的软件工具。这项研究将促进课程开发、学生培训和教育推广。来自不同实验平台的多模态数据具有不同的属性和特征。在许多系统中，包括生物过程，调节本质上是模块化的和时间动态的。某些相关的生物条件中存在共同的监管原则。该项目将重点从频率推理的角度通过新颖的相关图形模型开发新的数据集成方法和理论。这些方法将基于超越传统高斯假设的指数马尔可夫随机场。新方法将允许从多个静态和动态条件下发现高维异构数据的网络。该项目还涉及开发针对复杂优化问题的有效算法，并结合结构诱导正则化机制。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。