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III: Small: Novel Geometric Algorithms for Learning from Big Biomedical Data

III：小：从生物医学大数据中学习的新型几何算法

基本信息

批准号：
1910492
负责人：
Jinhui Xu
金额：
$ 50万
依托单位：
SUNY at Buffalo
依托单位国家：
美国
项目类别：
Standard Grant
财政年份：
2019
资助国家：
美国
起止时间：
2019-09-01 至 2023-08-31
项目状态：
已结题

来源：
https://www.nsf.gov/awardsearch/showAward?AWD_ID=1910492&HistoricalAwards=false
关键词：
III Small Novel Geometric Algorithms

项目摘要

Big biomedical data has become a key resource for discovery in biomedicine. However, due to their distributed nature and the existence of sensitive and noisy data, they are not utilized to their full potential. For example, lots of biomedical data are generated and stored at different sites such as hospitals, pharmacies, and biomedical labs, numerous challenges could arise when gathering, integrating, and utilizing them. Thus, more suitable and effective techniques are urgently needed to integrate and analyze them. This project aims to design a set of novel algorithmic tools for several fundamental data analytics problems commonly encountered in big biomedical data. This project could potentially increase the ability of gathering and integrating big biomedical data, effectively exploiting such data, and handling unreliable and sensitive biomedical data. Specifically, the project will focus on four data analysis problems: distributed truth discovery, distributed classification, distributed clustering, and differentially private learning. Each of these problems has already been recognized as critical tools in data analysis and information integration. The project addresses a number of challenging issues (such as communication cost, data reliability, robustness, computational cost, and privacy-preserving), and aim to achieve highly efficient and quality guaranteed solutions for each of these problems. These four research aims will be evaluated based on their effectiveness, efficiency, and the practicality on specific biomedical datasets. Furthermore, this research will provide educational and research opportunities to both graduate and undergraduate students including students from underrepresented groups.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.

生物医学大数据已成为生物医学发现的重要资源。然而，由于它们的分布式特性以及敏感和噪声数据的存在，它们没有充分发挥其潜力。例如，在医院、药房和生物医学实验室等不同地点生成和存储大量生物医学数据，在收集、集成和利用这些数据时可能会出现许多挑战。因此，迫切需要更合适和有效的技术来整合和分析它们。本项目旨在为生物医学大数据中常见的几个基本数据分析问题设计一套新颖的算法工具。该项目有可能提高收集和整合生物医学大数据、有效利用这些数据以及处理不可靠和敏感的生物医学数据的能力。具体来说，该项目将专注于四个数据分析问题：分布式真相发现、分布式分类、分布式聚类和差异私有学习。这些问题中的每一个都已经被认为是数据分析和信息集成的关键工具。该项目解决了许多具有挑战性的问题（如通信成本、数据可靠性、鲁棒性、计算成本和隐私保护），并旨在为每个问题实现高效和有质量保证的解决方案。这四个研究目标将根据它们在特定生物医学数据集上的有效性、效率和实用性进行评估。此外，这项研究将为研究生和本科生提供教育和研究机会，包括来自代表性不足群体的学生。该奖项反映了美国国家科学基金会的法定使命，并通过使用基金会的知识价值和更广泛的影响审查标准进行评估，被认为值得支持。