RAISE: Big Data Tools: From Bioinformatics To Materials Genomics

RAISE：大数据工具：从生物信息学到材料基因组学

基本信息

批准号：
1743418
负责人：
Sanguthevar Rajasekaran
金额：
$ 70万
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：
2017
资助国家：
美国
起止时间：
2017-08-01 至 2022-07-31
项目状态：
已结题

来源：
https://www.nsf.gov/awardsearch/showAward?AWD_ID=1743418&HistoricalAwards=false
关键词：
RAISE Big Data Tools Bioinformatics

项目摘要

This RAISE project is jointly funded by the Big Data Spokes and BIGDATA Program in the Division of Information and Intelligent Systems in the Directorate for Computing and Information Science and Engineering; the Condensed Matter and Materials Theory Program in the Division of Materials Research and the Office of Multidisciplinary Activities in the Directorate for Mathematical and Physical Sciences; and the Office of Integrative Activities. Large amounts of data get generated in every field of science and engineering. Effective tools are needed to analyze these data and extract useful information. During the past two decades, much progress has been made in the domain of biological data analytics. Clearly, if we can translate this progress to other domains, we can avoid repetition of efforts and also speedup discoveries in the other domains. This project will promote translation of approaches and tools first developed for biological genomics to materials genomics. To maximize scientific impact and use in industry and academia, the software tools to be built will be disseminated to a wide audience. The participation of women and other underrepresented groups will be promoted by leveraging collaborations with the Northeast Big Data Hub and strong, existing institutional programs to encourage diversity at the University of Connecticut. The project will allow many students to gain significant classroom and research experience using the software tools, and they, in turn, will form the core of the highly trained workforce that is essential for the advanced industries critical to our nation's economy. Some of the existing tools developed for biological data may not be directly applicable for materials data. In such cases, novel algorithmic techniques will be developed to suitably modify them. This project will engineer tools for the analysis and discovery of materials to accelerate research in Materials Science. The project will support workshops to bring together scientists from bioinformatics and materials science. The interactions among scientists from these areas are expected to result in crosscutting advances in big data analytics and hence create transformative knowledge. The Northeast Hub as well as the Materials Science Spoke will participate in the project's dissemination effort.

该RAISE项目由计算与信息科学与工程局信息与智能系统司的Big Data Spokes和BIGDATA计划共同资助；材料研究部的凝聚态和材料理论项目以及数学和物理科学局的多学科活动办公室；和综合活动办公室。科学和工程的各个领域都会产生大量数据。需要有效的工具来分析这些数据并提取有用的信息。在过去的二十年中，生物数据分析领域取得了很大进展。显然，如果我们能够将这一进展转化到其他领域，我们就可以避免重复努力，并加速其他领域的发现。该项目将促进最初为生物基因组学开发的方法和工具向材料基因组学的转化。为了最大限度地提高科学影响和在工业界和学术界的使用，将要构建的软件工具将向广大受众传播。通过利用与东北大数据中心的合作以及康涅狄格大学现有的强有力的机构计划来鼓励多样性，将促进妇女和其他代表性不足群体的参与。该项目将使许多学生使用软件工具获得重要的课堂和研究经验，而他们反过来将成为训练有素的劳动力的核心，这对于对我们国家经济至关重要的先进行业至关重要。一些为生物数据开发的现有工具可能无法直接适用于材料数据。在这种情况下，将开发新的算法技术来适当修改它们。该项目将设计用于材料分析和发现的工具，以加速材料科学研究。该项目将支持举办研讨会，将生物信息学和材料科学领域的科学家聚集在一起。来自这些领域的科学家之间的互动预计将带来大数据分析的跨领域进步，从而创造变革性的知识。东北中心和材料科学中心将参与该项目的传播工作。

项目成果

期刊论文数量（10）

专著数量（0）

科研奖励数量（0）

会议论文数量（0）

专利数量（0）

AutoPrune: Automatic Network Pruning by Regularizing Auxiliary Parameters

AutoPrune：通过规范辅助参数自动网络修剪

DOI：
10.1016/j.triboint.2010.02.001
发表时间：
2024-09-14
期刊：
Tribology International
影响因子：
6.2
作者：
Xia Xiao;Zigeng Wang;S. Rajasekaran
通讯作者：
S. Rajasekaran

Efficient Randomized Feature Selection Algorithms

高效的随机特征选择算法

DOI：
10.1109/hpcc/smartcity/dss.2019.00117
发表时间：
2019-10
期刊：
21st IEEE International Conference on High Performance Computing and Communications (HPCC-2019
影响因子：
0
作者：
Wang, Zigeng;Rajasekaran, Sanguthevar
通讯作者：
Rajasekaran, Sanguthevar

Efficient Algorithms for Finding Edit-Distance Based Motifs

查找基于编辑距离的图案的有效算法