Advancing computational biology and bioinformatics research through open innovation competitions


Autoři: Andrea Blasco aff001;  Michael G. Endres aff001;  Rinat A. Sergeev aff001;  Anup Jonchhe aff004;  N. J. Maximilian Macaluso aff004;  Rajiv Narayan aff004;  Ted Natoli aff004;  Jin H. Paik aff001;  Bryan Briney aff005;  Chunlei Wu aff006;  Andrew I. Su aff006;  Aravind Subramanian aff004;  Karim R. Lakhani aff001
Působiště autorů: Laboratory for Innovation Science at Harvard, Harvard University, Cambridge, MA, United States of America aff001;  Harvard Business School, Harvard University, Boston, MA, United States of America aff002;  Institute for Quantitative Social Science, Harvard University, Cambridge, MA, United States of America aff003;  The Broad Institute, Cambridge, MA, United States of America aff004;  Department of Immunology and Microbial Science, The Scripps Research Institute, La Jolla, CA, United States of America aff005;  Department of Integrative Structural and Computational Biology, The Scripps Research Institute, La Jolla, CA, United States of America aff006;  National Bureau of Economic Research, Cambridge, MA, United States of America aff007
Vyšlo v časopise: PLoS ONE 14(9)
Kategorie: Research Article
doi: 10.1371/journal.pone.0222165

Souhrn

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.

Klíčová slova:

Algorithms – Antibodies – Bioinformatics – Computational biology – Computer architecture – Gene expression – Gene mapping – Memory


Zdroje

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Článek vyšel v časopise

PLOS One


2019 Číslo 9

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