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Student Travel Support: ACM International Workshop on Big Data in Life Sciences, Seattle, WA, October 2, 2016

Student Travel Support: ACM International Workshop on Big Data in Life Sciences, Seattle, WA, October 2, 2016
学生旅行支持:ACM 国际生命科学大数据研讨会,华盛顿州西雅图,2016 年 10 月 2 日
批准号:
1638757
负责人:
Jaroslaw Zola
金额:
$1.0万
依托单位:
依托单位国家:
美国
项目类别:
Standard Grant
财政年份:
2016
资助国家:
美国
项目状态:
已结题
起止时间:
2016-07-01 至 2017-06-30

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英文摘要
The ACM International Workshop on Big Data in Life Sciences (BigLS) is a workshop series focusing on computational and data challenges in broadly defined life sciences. The workshop was initiated in 2013, and it is hosted together with the ACM Conference on Bioinformatics, Computational Biology and Health Informatics - the flagship conference of the Association for Computing Machinery Special Interest Group on Bioinformatics, Computational Biology, and Biomedical Informatics (SIGBio). The workshop brings together leading researchers and practitioners working on a diverse range of big data problems relating to biology and medicine, and engages them in a discussion about current big data questions, the state of computational tools and analytics, the challenges and the future trends within life sciences.This project will support up to ten students and postdoctoral researchers from US academic institutions to attend BigLS 2016 to be held on October 2, 2016 in Seattle, WA. Awardees will be selected via a widely advertised competitive process involving the submission of a travel grant application, and review by the program committee. Preference will be given to women and underrepresented minority groups, first-generation college students, and undergraduate researchers.Big data problems and challenges have become a reality in modern day computational biology, bioinformatics and biomedical informatics. The workshop will feature peer-reviewed papers and invited talks on five key research themes that underline big data research in life sciences: 1) scalable algorithms and techniques for big data analytics in molecular biology; 2) statistical and integrative approaches to big data biology; 3) emerging machine learning and AI techniques for big data biology; 4) high performance computing methods and software for big data biology; 5) software and hardware foundations for managing big data in biomedical informatics.
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