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

2019; Frontiers Media; Volume: 10; Linguagem: Inglês

10.3389/fgene.2019.00611

1664-8021

Christoffer Nellåker, Fowzan S. Alkuraya, Gareth Baynam, Raphael Bernier, François P. Bernier, Vanessa Boulanger, Michael Brudno, Han G. Brunner, Jill Clayton‐Smith, Benjamin Cogné, Hugh Dawkins, B. deVries, Sofia Douzgou, Tracy Dudding‐Byth, Evan E. Eichler, Michael Ferlaino, Karen Fieggen, Helen V. Firth, David Fitzpatrick, Dylan Gration, Tudor Groza, Melissa Haendel, Nina Hallowell, Ada Hamosh, Jayne Y. Hehir‐Kwa, Marc‐Phillip Hitz, Mark Hughes, Usha Kini, Tjitske Kleefstra, R. Frank Kooy, Peter Krawitz, Sébastien Küry, Melissa Lees, Gholson J. Lyon, Stanislas Lyonnet, Julien L. Marcadier, M. Stephen Meyn, Veronika Moslerová, Juan Politei, Cathryn Poulton, F. Lucy Raymond, Margot R.F. Reijnders, Peter N. Robinson, Corrado Romano, Catherine M. Rose, David Sainsbury, Lyn Schofield, V. Reid Sutton, Marek Turnovec, Anke Van Dijck, Hilde Van Esch, Andrew O.M. Wilkie,

Pancreatic and Hepatic Oncology Research

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.