Crowdsourcing biomedical research: leveraging communities as innovation engines

被引:112
作者
Saez-Rodriguez, Julio [1 ,2 ]
Costello, James C. [3 ]
Friend, Stephen H. [4 ]
Kellen, Michael R. [4 ]
Mangravite, Lara [4 ]
Meyer, Pablo [5 ]
Norman, Thea [4 ]
Stolovitzky, Gustavo [5 ,6 ]
机构
[1] Rhein Westfal TH Aachen, Fac Med, Joint Res Ctr Computat Biomed, D-52074 Aachen, Germany
[2] Wellcome Trust Res Labs, EMBL EBI, Genome Campus, Hinxton CB10 1SD, England
[3] Univ Colorado, Dept Pharmacol, Anschutz Med Campus, Aurora, CO 80045 USA
[4] Sage Bionetworks, Seattle, WA 98109 USA
[5] IBM Thomas J Watson Res Ctr, Yorktown Hts, NY 10598 USA
[6] Icahn Sch Med Mt Sinai, Dept Genet & Genom Sci, New York, NY 10029 USA
关键词
COMPETITIVE ASSESSMENT; STRUCTURE PREDICTION; NETWORK; DESIGN; CHALLENGE; BIOLOGY; WISDOM; CROWDS; RECONSTRUCTION;
D O I
10.1038/nrg.2016.69
中图分类号
Q3 [遗传学];
学科分类号
071007 ; 090102 ;
摘要
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.
引用
收藏
页码:470 / 486
页数:17
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