| Literature DB >> 27418159 |
Julio Saez-Rodriguez1,2, James C Costello3, Stephen H Friend4, Michael R Kellen4, Lara Mangravite4, Pablo Meyer5, Thea Norman4, Gustavo Stolovitzky5,6.
Abstract
The generation of large-scale biomedical data is creating unprecedented opportunities for basic and translational science. Typically, the data producers perform initial analyses, but it is very likely that the most informative methods may reside with other groups. Crowdsourcing the analysis of complex and massive data has emerged as a framework to find robust methodologies. When the crowdsourcing is done in the form of collaborative scientific competitions, known as Challenges, the validation of the methods is inherently addressed. Challenges also encourage open innovation, create collaborative communities to solve diverse and important biomedical problems, and foster the creation and dissemination of well-curated data repositories.Entities:
Mesh:
Year: 2016 PMID: 27418159 PMCID: PMC5918684 DOI: 10.1038/nrg.2016.69
Source DB: PubMed Journal: Nat Rev Genet ISSN: 1471-0056 Impact factor: 53.242