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

Minerva Consortium

Research output: Contribution to journalArticle

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.

Original languageEnglish
Article number611
Number of pages9
JournalFrontiers in Genetics
Volume10
DOIs
Publication statusPublished - 29 Jul 2019

Cite this

@article{bcfe5df74bb9478c8a132a3a91f255b8,
title = "Enabling Global Clinical Collaborations on Identifiable Patient Data: The Minerva Initiative",
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.",
keywords = "data sharing, phenotyping, patient information, data protection, rare disease, Faces, DE-NOVO MUTATIONS, FACIAL DYSMORPHOLOGY, MATCHMAKER EXCHANGE, RARE, PHENOTYPE, RECOGNITION, INDIVIDUALS, GENOTYPE, THERAPY, GENOMES",
author = "{Minerva Consortium} and Christoffer Nellaker and Alkuraya, {Fowzan S.} and Gareth Baynam and Bernier, {Raphael A.} and Bernier, {Francois P. J.} and Vanessa Boulangerw and Michael Brudno and Brunner, {Han G.} and Jill Clayton-Smith and Benjamin Cogne and Dawkins, {Hugh J. S.} and deVries, {Bert B. A.} and Sofia Douzgou and Tracy Dudding-Byth and Eichler, {Evan E.} and Michael Ferlaino and Karen Fieggen and Helen Firth and FitzPatrick, {David R.} and Dylan Gration and Tudor Groza and Melissa Haende and Nina Hallowel and Ada Hamosh and Jayne Hehir-Kwa and Marc-Phillip Hitz and Mark Hughes and Usha Kini and Tjitske Kleefstra and Kooy, {R. Frank} and Peter Krawitz and Sebastien Kury and Melissa Lees and Lyon, {Gholson J.} and Stanislas Lyonnet and Marcadier, {Julien L.} and Stephen Meyn and Veronika Moslerova and Politei, {Juan M.} and Poulton, {Cathryn C.} and Raymond, {F. Lucy} and Reijnders, {Margot R. F.} and Robinson, {Peter N.} and Corrado Romano and Rose, {Catherine M.} and Sainsbury, {David C. G.} and Lyn Schofield and Sutton, {Vernon R.} and Marek Tumovec and {Van Dijck}, Anke and {Van Esch}, Hilde and Witkie, {Andrew O. M.}",
year = "2019",
month = "7",
day = "29",
doi = "10.3389/fgene.2019.00611",
language = "English",
volume = "10",
journal = "Frontiers in Genetics",
issn = "1664-8021",
publisher = "Frontiers Media SA",

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

In: Frontiers in Genetics, Vol. 10, 611, 29.07.2019.

Research output: Contribution to journalArticle

TY - JOUR

T1 - Enabling Global Clinical Collaborations on Identifiable Patient Data

T2 - The Minerva Initiative

AU - Minerva Consortium

AU - Nellaker, Christoffer

AU - Alkuraya, Fowzan S.

AU - Baynam, Gareth

AU - Bernier, Raphael A.

AU - Bernier, Francois P. J.

AU - Boulangerw, Vanessa

AU - Brudno, Michael

AU - Brunner, Han G.

AU - Clayton-Smith, Jill

AU - Cogne, Benjamin

AU - Dawkins, Hugh J. S.

AU - deVries, Bert B. A.

AU - Douzgou, Sofia

AU - Dudding-Byth, Tracy

AU - Eichler, Evan E.

AU - Ferlaino, Michael

AU - Fieggen, Karen

AU - Firth, Helen

AU - FitzPatrick, David R.

AU - Gration, Dylan

AU - Groza, Tudor

AU - Haende, Melissa

AU - Hallowel, Nina

AU - Hamosh, Ada

AU - Hehir-Kwa, Jayne

AU - Hitz, Marc-Phillip

AU - Hughes, Mark

AU - Kini, Usha

AU - Kleefstra, Tjitske

AU - Kooy, R. Frank

AU - Krawitz, Peter

AU - Kury, Sebastien

AU - Lees, Melissa

AU - Lyon, Gholson J.

AU - Lyonnet, Stanislas

AU - Marcadier, Julien L.

AU - Meyn, Stephen

AU - Moslerova, Veronika

AU - Politei, Juan M.

AU - Poulton, Cathryn C.

AU - Raymond, F. Lucy

AU - Reijnders, Margot R. F.

AU - Robinson, Peter N.

AU - Romano, Corrado

AU - Rose, Catherine M.

AU - Sainsbury, David C. G.

AU - Schofield, Lyn

AU - Sutton, Vernon R.

AU - Tumovec, Marek

AU - Van Dijck, Anke

AU - Van Esch, Hilde

AU - Witkie, Andrew O. M.

PY - 2019/7/29

Y1 - 2019/7/29

N2 - 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.

AB - 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.

KW - data sharing

KW - phenotyping

KW - patient information

KW - data protection

KW - rare disease

KW - Faces

KW - DE-NOVO MUTATIONS

KW - FACIAL DYSMORPHOLOGY

KW - MATCHMAKER EXCHANGE

KW - RARE

KW - PHENOTYPE

KW - RECOGNITION

KW - INDIVIDUALS

KW - GENOTYPE

KW - THERAPY

KW - GENOMES

U2 - 10.3389/fgene.2019.00611

DO - 10.3389/fgene.2019.00611

M3 - Article

VL - 10

JO - Frontiers in Genetics

JF - Frontiers in Genetics

SN - 1664-8021

M1 - 611

ER -