Enabling Global Clinical Collaborations on Identifiable Patient Data: The Minerva Initiative - 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.; et al. (Frontiers in Genetics, 2019)
The clinical utility of computational phenotyping for genetic and rare diseases is increasingly appreciated, however, its true potential is yet to be fully realised. 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 healthcare, 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.