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Nat Rev Genet. 2016 Jul 15;17(8):470-86. doi: 10.1038/nrg.2016.69.

Crowdsourcing biomedical research: leveraging communities as innovation engines.

Author information

1
RWTH Aachen University, Faculty of Medicine, Joint Research Centre for Computational Biomedicine, Aachen D-52074, Germany.
2
European Molecular Biology Laboratory-European Bioinformatics Institute (EMBL-EBI), Wellcome Trust Genome Campus, Hinxton CB10 1SD, UK.
3
Department of Pharmacology, University of Colorado, Anschutz Medical Campus, Aurora, Colorado 80045, USA.
4
Sage Bionetworks, Seattle, Washington 98109, USA.
5
IBM Thomas J. Watson Research Center, Yorktown Heights, New York 10598, USA.
6
Department of Genetics and Genomic Sciences, Icahn School of Medicine at Mount Sinai, New York, New York 10029, USA.

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.

PMID:
27418159
PMCID:
PMC5918684
DOI:
10.1038/nrg.2016.69
[Indexed for MEDLINE]
Free PMC Article

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