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PLoS One. 2019 Sep 27;14(9):e0222165. doi: 10.1371/journal.pone.0222165. eCollection 2019.

Advancing computational biology and bioinformatics research through open innovation competitions.

Author information

1
Laboratory for Innovation Science at Harvard, Harvard University, Cambridge, MA, United States of America.
2
Institute for Quantitative Social Science, Harvard University, Cambridge, MA, United States of America.
3
The Broad Institute, Cambridge, MA, United States of America.
4
Harvard Business School, Harvard University, Boston, MA, United States of America.
5
Department of Immunology and Microbial Science, The Scripps Research Institute, La Jolla, CA, United States of America.
6
Department of Integrative Structural and Computational Biology, The Scripps Research Institute, La Jolla, CA, United States of America.
7
National Bureau of Economic Research, Cambridge, MA, United States of America.

Abstract

Open data science and algorithm development competitions offer a unique avenue for rapid discovery of better computational strategies. We highlight three examples in computational biology and bioinformatics research in which the use of competitions has yielded significant performance gains over established algorithms. These include algorithms for antibody clustering, imputing gene expression data, and querying the Connectivity Map (CMap). Performance gains are evaluated quantitatively using realistic, albeit sanitized, data sets. The solutions produced through these competitions are then examined with respect to their utility and the prospects for implementation in the field. We present the decision process and competition design considerations that lead to these successful outcomes as a model for researchers who want to use competitions and non-domain crowds as collaborators to further their research.

PMID:
31560691
DOI:
10.1371/journal.pone.0222165
Free PMC Article

Conflict of interest statement

The authors have declared that no competing interests exist.

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