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Leveraging crowdsourcing to accelerate global health solutions

Nature Biotechnology volume 37, pages 848–850 (2019)Cite this article

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To the Editor — Crowdsourced data science challenges can achieve in months what would take years through conventional research approaches. However, they remain largely untapped for underserved or critical biomedical challenges, such as treatment of malaria, in which computational modeling lags and accelerated innovation are urgently needed to combat the emergence of drug resistance.

The diversity and quality of computational solutions obtained in data science challenges1 combined with the rapid pace at which they are developed (typically weeks to months for a single challenge) could in principle accelerate research in fields in which data science expertise is highly underrepresented. Regarding disease-related research questions, data science crowdsourcing platforms have been used in only a limited way and for a very restricted number of diseases. For example, of the 19 ongoing challenges on Kaggle at the time of writing, only one is for a disease, cancer. Since the establishment of DREAM Challenges in 2007, not a single challenge has involved an infectious disease, whereas 15 have involved cancer. In this context, we believe that there is an untapped opportunity for the data science challenges model of innovation to expand beyond the areas to which it has been traditionally applied into new areas of need, such as neglected diseases. Here, we consider the case of emerging drug resistance to the anti-malarial drug artemisinin and present a call for participation in the Malaria DREAM Challenge for computational models of drug resistance.

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Fig. 1: Malaria DREAM Challenge.

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Acknowledgements

The data collection for the Malaria DREAM Challenge is funded by an NIH R21 grant to M.T.F. (AI103872-01AI). The Malaria DREAM Challenge has received funding support from the Bill & Melinda Gates Foundation. H3ABioNet was supported by the National Human Genome Research Institute (NHGRI) and the Office of The Director (OD), National Institutes of Health under award number U41HG006941. We are grateful to K. Davis (Center for Research Computing) for designing the figure presented in this paper.

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Authors and Affiliations

  1. Eck Institute for Global Health, Department of Biological Sciences, University of Notre Dame, Notre Dame, IN, USA

    Sage Davis, Katrina Button-Simons, Lisa Checkley, Gabriel J. Foster, Katelyn Vendrely, Michael T. Ferdig & Geoffrey H. Siwo

  2. Abdelmalek Essaadi University, ENSATg, System and Data Engineering Team, Tangier, Morocco

    Taoufik Bensellak & Ahmed Moussa

  3. Icahn School of Medicine at Mount Sinai, New York, NY, USA

    Eren Mehmet Ahsen

  4. National Institutes of Health, Bethesda, MD, USA

    Xinzhuan Su

  5. IBM Research Africa, Johannesburg, South Africa

    Darlington Mapiye

  6. Grand Valley State University, Grand Rapids, MI, USA

    Sok Kean Khoo

  7. Shoklo Malaria Research Unit, Mahidol-Oxford Tropical Medicine Research Unit, Mahidol University, Mae Sot, Thailand

    Francois Nosten

  8. Texas Biomedical Research Institute, San Antonio, TX, USA

    Timothy J. C. Anderson

  9. Sage Bionetworks, Seattle, WA, USA

    Julie Bletz & Thomas Yu

  10. Computational Biology Division (H3ABioNet), University of Cape Town, Cape Town, South Africa

    Sumir Panji & Nicola Mulder

  11. Institut Pasteur de Tunis, LR11IPT02, Tunis-Belvédère, Tunisia

    Amel Ghouila

  12. Bill & Melinda Gates Foundation, Seattle, WA, USA

    Thea Norman & Steven Kern

  13. IBM Thomas J. Watson Research Center, IBM, New York, NY, USA

    Pablo Meyer & Gustavo Stolovitzky

  14. Center for Research Computing, University of Notre Dame, Notre Dame, IN, USA

    Geoffrey H. Siwo

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  1. Sage Davis
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Contributions

G.H.S., M.T.F. and G.S. conceived the challenge. T.J.C.A., F.N. and X.S. provided malaria parasite isolates. S.D., K.B.-S., L.C., K.V., G.J.F. and S.K.K. collected transcriptional data for the challenge. G.H.S., P.M., G.S., M.T.F., K.B.-S., N.M., A.G., E.M.A., S.K., T.N., T.B., J.B., T.Y., A.M., S.P. and D.M. were all involved in organizing the challenge.

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Correspondence to Geoffrey H. Siwo.

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Davis, S., Button-Simons, K., Bensellak, T. et al. Leveraging crowdsourcing to accelerate global health solutions. Nat Biotechnol 37, 848–850 (2019). https://doi.org/10.1038/s41587-019-0180-5

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