ArticleFrontiers in genetics2019
Enabling Global Clinical Collaborations on Identifiable Patient Data: The Minerva Initiative.
Article in Frontiers in genetics, 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 12 papers, 1 of them a synthesis that pooled it.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
12 citing papers in PubMed, 1 synthesis or guideline pooled it, 19 citations in OpenAlex.
- The ethical and legal landscape of brain data governance.PloS one · 2022Pooled it
- A scoping review of the governance of federated learning in healthcare.NPJ digital medicine · 2025Article
- GestaltMatcher Database - A global reference for facial phenotypic variability in rare human diseases.medRxiv : the preprint server for health sciences · 2024Article
- GestaltMatcher Database - A global reference for facial phenotypic variability in rare human diseases.Research square · 2024Article
- Genomic Data Sharing: Navigating The Ethical Issues In Collaborative Research Relationships Between The Global North-South.African journal of bioethics · 2024Article
- Democratising or disrupting diagnosis? Ethical issues raised by the use of AI tools for rare disease diagnosis.SSM. Qualitative research in health · 2023Article
- "I don't think people are ready to trust these algorithms at face value": trust and the use of machine learning algorithms in the diagnosis of rare disease.BMC medical ethics · 2022Article
- Quantitative facial phenotyping for Koolen-de Vries and 22q11.2 deletion syndrome.European journal of human genetics : EJHG · 2021Article
- Is Sharing Datasets the Answer to the New Challenges of Reproductive Biology Research?Reproductive sciences (Thousand Oaks, Calif.) · 2021Article
- The case for open science: rare diseases.JAMIA open · 2020Review
- Towards standardization guidelines for in silico approaches in personalized medicine.Journal of integrative bioinformatics · 2020Article
- Digital/computational phenotyping: What are the differences in the science and the ethics?Big data & societyArticle
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
53 authors at 20 institutions in 12 countries.
Funding
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.
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What OpenQuestion holds
Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.