AUTHOR=Nellåker Christoffer , Alkuraya Fowzan S. , Baynam Gareth , Bernier Raphael A. , Bernier Francois P.J. , Boulanger Vanessa , Brudno Michael , Brunner Han G. , Clayton-Smith Jill , Cogné Benjamin , Dawkins Hugh J.S. , deVries Bert B.A. , Douzgou Sofia , Dudding-Byth Tracy , Eichler Evan E. , Ferlaino Michael , Fieggen Karen , Firth Helen V. , FitzPatrick David R. , Gration Dylan , Groza Tudor , Haendel Melissa , Hallowell Nina , Hamosh Ada , Hehir-Kwa Jayne , Hitz Marc-Phillip , Hughes Mark , Kini Usha , Kleefstra Tjitske , Kooy R Frank , Krawitz Peter , Küry Sébastien , Lees Melissa , Lyon Gholson J. , Lyonnet Stanislas , Marcadier Julien L. , Meyn Stephen , Moslerová Veronika , Politei Juan M. , Poulton Cathryn C. , Raymond F Lucy , Reijnders Margot R.F. , Robinson Peter N. , Romano Corrado , Rose Catherine M. , Sainsbury David C.G. , Schofield Lyn , Sutton Vernon R. , Turnovec Marek , Van Dijck Anke , Van Esch Hilde , Wilkie Andrew O.M. , The Minerva Consortium TITLE=Enabling Global Clinical Collaborations on Identifiable Patient Data: The Minerva Initiative JOURNAL=Frontiers in Genetics VOLUME=10 YEAR=2019 URL=https://www.frontiersin.org/journals/genetics/articles/10.3389/fgene.2019.00611 DOI=10.3389/fgene.2019.00611 ISSN=1664-8021 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.