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RAISE: Big Data Tools: From Bioinformatics To Materials Genomics

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
批准号:
1743418
负责人:
Sanguthevar Rajasekaran
金额:
$70.0万
依托单位:
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
项目状态:
已结题
起止时间:
2017-08-01 至 2022-07-31

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中文摘要
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英文摘要
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.
期刊论文(10)
专著(0)
科研奖励(0)
会议论文
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: --
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
DOI: --
发表时间: 2019
期刊: International Conference on Algorithms for Computational Biology
影响因子: --
作者: [P. Xiao, X. Cai]
通讯作者: P. Xiao, X. Cai
Efficient Algorithms for Finding the Closest l-mers in Biological Data
寻找生物数据中最接近的 l-mers 的有效算法
DOI: 10.1109/tcbb.2018.2843364
发表时间: 2018
期刊: IEEE/ACM Transactions on Computational Biology and Bioinformatics
影响因子: --
作者: [Cai, Xingyu, Mamun, Abdullah-Al, Rajasekaran, Sanguthevar]
通讯作者: Rajasekaran, Sanguthevar
9
    Ninth International Conference on Computational Advances in Bio & Medical Sciences (ICCABS)
    • 批准号:
      2005642
    • 项目类别:
      Standard Grant
    • 资助金额:
      $0.8万
    • 财政年份:
      2020
    • 负责人:
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    • 依托单位:
    Eighth International IEEE Conference on Computational Advances in Bio and Medical Sciences (ICCABS) - Travel Awards
    • 批准号:
      1853991
    • 项目类别:
      Standard Grant
    • 资助金额:
      $2.0万
    • 财政年份:
      2019
    • 负责人:
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    EAGER: Type II: Deep Learning and Combinatorial Algorithms for Inorganic Crystal Structure Prediction
    • 批准号:
      1843025
    • 项目类别:
      Standard Grant
    • 资助金额:
      $30.0万
    • 财政年份:
      2019
    • 负责人:
      Sanguthevar Rajasekaran
    • 依托单位:
    Seventh International IEEE Conference on Computational Advances in Bio and medical Sciences (ICCABS) - Travel Awards
    • 批准号:
      1747853
    • 项目类别:
      Standard Grant
    • 资助金额:
      $2.0万
    • 财政年份:
      2017
    • 负责人:
      Sanguthevar Rajasekaran
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