DNA gathered from a used coffee mug could lead to serious breaches of a person's genetic privacy. The UK government's Human Genetics Commission now recommends making theft of genetic information a criminal offence, writes Michael Gross.
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http://dx.doi.org/10.1016/s0960-9822(02)00906-5 | DOI Listing |
Eur J Hum Genet
January 2025
Institute for Genomic Statistics and Bioinformatics, Bonn, NRW, Germany.
The facial gestalt (overall facial morphology) is a characteristic clinical feature in many genetic disorders that is often essential for suspecting and establishing a specific diagnosis. Therefore, publishing images of individuals affected by pathogenic variants in disease-associated genes has been an important part of scientific communication. Furthermore, medical imaging data is also crucial for teaching and training deep-learning models such as GestaltMatcher.
View Article and Find Full Text PDFDiagnostics (Basel)
January 2025
Cybersecurity Laboratory, Luleå University of Technology, 97187 Luleå, Sweden.
Alzheimer's disease (AD) leads to severe cognitive impairment and functional decline in patients, and its exact cause remains unknown. Early diagnosis of AD is imperative to enable timely interventions that can slow the progression of the disease. This research tackles the complexity and uncertainty of AD by employing a multimodal approach that integrates medical imaging and demographic data.
View Article and Find Full Text PDFOne of the major challenges in genomic data sharing is protecting participants' privacy in collaborative studies and when genomic data is outsourced to perform analysis tasks, e.g., genotype imputation services and federated collaborations genomic analysis.
View Article and Find Full Text PDFCurr Res Transl Med
January 2025
Department of Research and Innovation, Medway NHS Foundation Trust, Gillingham ME7 5NY, United Kingdom; Faculty of Medicine, Health and Social Care, Canterbury Christ Church University, United Kingdom.
This narrative review examines the transformative role of Artificial Intelligence (AI) and Machine Learning (ML) in organ retrieval and transplantation. AI and ML technologies enhance donor-recipient matching by integrating and analyzing complex datasets encompassing clinical, genetic, and demographic information, leading to more precise organ allocation and improved transplant success rates. In surgical planning, AI-driven image analysis automates organ segmentation, identifies critical anatomical features, and predicts surgical outcomes, aiding pre-operative planning and reducing intraoperative risks.
View Article and Find Full Text PDFJAMA
January 2025
Department of Obstetrics and Gynecology, Center for Bioethics and Social Sciences in Medicine, University of Michigan Medical School, Ann Arbor.
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