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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项目由计算和信息科学与工程局信息和智能系统司的大数据辐条和BIGDATA计划联合资助;材料研究司的凝聚态物质和材料理论计划和多学科活动办公室在数学和物理科学局;和综合活动办公室。在科学和工程的每个领域都会产生大量的数据。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的努力，并加快其他领域的发现。该项目将促进将最初为生物基因组学开发的方法和工具转化为材料基因组学。为了最大限度地提高科学影响力以及在工业界和学术界的使用，将要构建的软件工具将传播给广泛的受众。妇女和其他代表性不足的群体的参与将通过利用与东北大数据中心和强大的现有机构计划的合作来促进，以鼓励康涅狄格大学的多样性。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，反过来，他们将成为训练有素的劳动力的核心，这对我们国家经济的先进产业至关重要。为生物数据开发的一些现有工具可能不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地修改它们。该项目将设计用于分析和发现材料的工具，以加速材料科学的研究。该项目将支持举办讲习班，将生物信息学和材料科学的科学家聚集在一起。来自这些领域的科学家之间的相互作用预计将导致大数据分析的跨领域进步，从而创造变革性的知识。东北枢纽以及材料科学辐条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）

FIRLA: a Fast Incremental Record Linkage Algorithm

DOI：
10.1016/j.jbi.2022.104094
发表时间：
2022-05-16
期刊：
JOURNAL OF BIOMEDICAL INFORMATICS
影响因子：
4.5
作者：
Soliman，Ahmed;Rajasekaran，Sanguthevar
通讯作者：
Rajasekaran，Sanguthevar

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
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