Project Details
Description
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.
| Status | Finished |
|---|---|
| Effective start/end date | 07/1/16 → 06/30/17 |
Funding
- National Science Foundation: $10,000.00
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