Enabling Global Clinical Collaborations on Identifiable Patient Data: The Minerva Initiative

Nellåker, Christoffer and Alkuraya, Fowzan S. and Baynam, Gareth and Bernier, Raphael A. and Bernier, Francois P.J. and Boulanger, Vanessa and Brudno, Michael and Brunner, Han G. and Clayton-Smith, Jill and Cogné, Benjamin and Dawkins, Hugh J.S. and deVries, Bert B.A. and Douzgou, Sofia and Dudding-Byth, Tracy and Eichler, Evan E. and Ferlaino, Michael and Fieggen, Karen and Firth, Helen V. and FitzPatrick, David R. and Gration, Dylan and Groza, Tudor and Haendel, Melissa and Hallowell, Nina and Hamosh, Ada and Hehir-Kwa, Jayne and Hitz, Marc-Phillip and Hughes, Mark and Kini, Usha and Kleefstra, Tjitske and Kooy, R Frank and Krawitz, Peter and Küry, Sébastien and Lees, Melissa and Lyon, Gholson J. and Lyonnet, Stanislas and Marcadier, Julien L. and Meyn, Stephen and Moslerová, Veronika and Politei, Juan M. and Poulton, Cathryn C. and Raymond, F Lucy and Reijnders, Margot R.F. and Robinson, Peter N. and Romano, Corrado and Rose, Catherine M. and Sainsbury, David C.G. and Schofield, Lyn and Sutton, Vernon R. and Turnovec, Marek and Van Dijck, Anke and Van Esch, Hilde and Wilkie, Andrew O.M. (2019) Enabling Global Clinical Collaborations on Identifiable Patient Data: The Minerva Initiative. Frontiers in Genetics, 10. ISSN 1664-8021

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Abstract

The clinical utility of computational phenotyping for both genetic and rare diseases is increasingly appreciated; however, its true potential is yet to be fully realized. Alongside the growing clinical and research availability of sequencing technologies, precise deep and scalable phenotyping is required to serve unmet need in genetic and rare diseases. To improve the lives of individuals affected with rare diseases through deep phenotyping, global big data interrogation is necessary to aid our understanding of disease biology, assist diagnosis, and develop targeted treatment strategies. This includes the application of cutting-edge machine learning methods to image data. As with most digital tools employed in health care, there are ethical and data governance challenges associated with using identifiable personal image data. There are also risks with failing to deliver on the patient benefits of these new technologies, the biggest of which is posed by data siloing. The Minerva Initiative has been designed to enable the public good of deep phenotyping while mitigating these ethical risks. Its open structure, enabling collaboration and data sharing between individuals, clinicians, researchers and private enterprise, is key for delivering precision public health.

Item Type: Article
Subjects: Open Digi Academic > Medical Science
Depositing User: Unnamed user with email support@opendigiacademic.com
Date Deposited: 27 Feb 2023 09:29
Last Modified: 02 Sep 2024 12:33
URI: http://publications.journalstm.com/id/eprint/231

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