1
|
Rujano MA, Boiten JW, Ohmann C, Canham S, Contrino S, David R, Ewbank J, Filippone C, Connellan C, Custers I, van Nuland R, Mayrhofer MT, Holub P, Álvarez EG, Bacry E, Hughes N, Freeberg MA, Schaffhauser B, Wagener H, Sánchez-Pla A, Bertolini G, Panagiotopoulou M. Sharing sensitive data in life sciences: an overview of centralized and federated approaches. Brief Bioinform 2024; 25:bbae262. [PMID: 38836701 DOI: 10.1093/bib/bbae262] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/06/2024] [Revised: 04/19/2024] [Indexed: 06/06/2024] Open
Abstract
Biomedical data are generated and collected from various sources, including medical imaging, laboratory tests and genome sequencing. Sharing these data for research can help address unmet health needs, contribute to scientific breakthroughs, accelerate the development of more effective treatments and inform public health policy. Due to the potential sensitivity of such data, however, privacy concerns have led to policies that restrict data sharing. In addition, sharing sensitive data requires a secure and robust infrastructure with appropriate storage solutions. Here, we examine and compare the centralized and federated data sharing models through the prism of five large-scale and real-world use cases of strategic significance within the European data sharing landscape: the French Health Data Hub, the BBMRI-ERIC Colorectal Cancer Cohort, the federated European Genome-phenome Archive, the Observational Medical Outcomes Partnership/OHDSI network and the EBRAINS Medical Informatics Platform. Our analysis indicates that centralized models facilitate data linkage, harmonization and interoperability, while federated models facilitate scaling up and legal compliance, as the data typically reside on the data generator's premises, allowing for better control of how data are shared. This comparative study thus offers guidance on the selection of the most appropriate sharing strategy for sensitive datasets and provides key insights for informed decision-making in data sharing efforts.
Collapse
Affiliation(s)
- Maria A Rujano
- European Clinical Research Infrastructure Network (ECRIN), Boulevard Saint Jacques 30, 75014, Paris, France
| | - Jan-Willem Boiten
- Foundation Lygature, Jaarbeursplein 6, 3521 AL, Utrecht, The Netherlands
| | - Christian Ohmann
- European Clinical Research Infrastructure Network (ECRIN), Boulevard Saint Jacques 30, 75014, Paris, France
| | - Steve Canham
- European Clinical Research Infrastructure Network (ECRIN), Boulevard Saint Jacques 30, 75014, Paris, France
| | - Sergio Contrino
- European Clinical Research Infrastructure Network (ECRIN), Boulevard Saint Jacques 30, 75014, Paris, France
| | - Romain David
- European Research Infrastructure on Highly Pathogenic Agents (ERINHA AISBL), rue du Trône 98/Boîte 4B, 1050, Brussels, Belgium
| | - Jonathan Ewbank
- European Research Infrastructure on Highly Pathogenic Agents (ERINHA AISBL), rue du Trône 98/Boîte 4B, 1050, Brussels, Belgium
| | - Claudia Filippone
- European Research Infrastructure on Highly Pathogenic Agents (ERINHA AISBL), rue du Trône 98/Boîte 4B, 1050, Brussels, Belgium
| | - Claire Connellan
- European Research Infrastructure on Highly Pathogenic Agents (ERINHA AISBL), rue du Trône 98/Boîte 4B, 1050, Brussels, Belgium
| | - Ilse Custers
- Foundation Lygature, Jaarbeursplein 6, 3521 AL, Utrecht, The Netherlands
| | - Rick van Nuland
- Foundation Lygature, Jaarbeursplein 6, 3521 AL, Utrecht, The Netherlands
| | - Michaela Th Mayrhofer
- Biobanking and Biomolecular Resources Research Infrastructure (BBMRI-ERIC), Neue Stiftingtalstrasse 2/B/6, 8010, Graz, Austria
| | - Petr Holub
- Biobanking and Biomolecular Resources Research Infrastructure (BBMRI-ERIC), Neue Stiftingtalstrasse 2/B/6, 8010, Graz, Austria
| | - Eva García Álvarez
- Biobanking and Biomolecular Resources Research Infrastructure (BBMRI-ERIC), Neue Stiftingtalstrasse 2/B/6, 8010, Graz, Austria
| | - Emmanuel Bacry
- Health Data Hub (HDH), rue Georges Pitard 9, 75015, Paris, France
| | - Nigel Hughes
- Janssen Research and Development, Antwerpseweg 15, 2340, Beerse, Belgium
| | - Mallory A Freeberg
- European Molecular Biology Laboratory (EMBL), European Bioinformatics Institute (EBI), Wellcome Genome Campus, CB10 1SD, Hinxton, Cambridgeshire, United Kingdom
| | - Birgit Schaffhauser
- Department of Clinical Neurosciences, Centre Hospitalier Universitaire Vaudois (CHUV), Rue du Bugnon 21, 1011, Lausanne, Switzerland
| | - Harald Wagener
- Center for Digital Health, BIH@Charité University Medicine, Anna-Louisa-Karsch-Straße 2, 10178, Berlin, Germany
| | - Alex Sánchez-Pla
- Department of Genetics, Microbiology and Statistics, Universitat de Barcelona, Diagonal 643, 08028, Barcelona, Spain
| | - Guido Bertolini
- Laboratory of Clinical Epidemiology, Istituto di Ricerche Farmacologiche Mario Negri IRCCS, Via GB Camozzi 3, 24020, Ranica (Bergamo), Italy
| | - Maria Panagiotopoulou
- European Clinical Research Infrastructure Network (ECRIN), Boulevard Saint Jacques 30, 75014, Paris, France
| |
Collapse
|
2
|
Brancato V, Esposito G, Coppola L, Cavaliere C, Mirabelli P, Scapicchio C, Borgheresi R, Neri E, Salvatore M, Aiello M. Standardizing digital biobanks: integrating imaging, genomic, and clinical data for precision medicine. J Transl Med 2024; 22:136. [PMID: 38317237 PMCID: PMC10845786 DOI: 10.1186/s12967-024-04891-8] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/28/2023] [Accepted: 01/14/2024] [Indexed: 02/07/2024] Open
Abstract
Advancements in data acquisition and computational methods are generating a large amount of heterogeneous biomedical data from diagnostic domains such as clinical imaging, pathology, and next-generation sequencing (NGS), which help characterize individual differences in patients. However, this information needs to be available and suitable to promote and support scientific research and technological development, supporting the effective adoption of the precision medicine approach in clinical practice. Digital biobanks can catalyze this process, facilitating the sharing of curated and standardized imaging data, clinical, pathological and molecular data, crucial to enable the development of a comprehensive and personalized data-driven diagnostic approach in disease management and fostering the development of computational predictive models. This work aims to frame this perspective, first by evaluating the state of standardization of individual diagnostic domains and then by identifying challenges and proposing a possible solution towards an integrative approach that can guarantee the suitability of information that can be shared through a digital biobank. Our analysis of the state of the art shows the presence and use of reference standards in biobanks and, generally, digital repositories for each specific domain. Despite this, standardization to guarantee the integration and reproducibility of the numerical descriptors generated by each domain, e.g. radiomic, pathomic and -omic features, is still an open challenge. Based on specific use cases and scenarios, an integration model, based on the JSON format, is proposed that can help address this problem. Ultimately, this work shows how, with specific standardization and promotion efforts, the digital biobank model can become an enabling technology for the comprehensive study of diseases and the effective development of data-driven technologies at the service of precision medicine.
Collapse
Affiliation(s)
| | - Giuseppina Esposito
- Bio Check Up S.R.L, 80121, Naples, Italy
- Department of Advanced Biomedical Sciences, University of Naples Federico II, 80131, Naples, Italy
| | | | | | - Peppino Mirabelli
- UOS Laboratori di Ricerca e Biobanca, AORN Santobono-Pausilipon, Via Teresa Ravaschieri, 8, 80122, Naples, Italy
| | - Camilla Scapicchio
- Academic Radiology, Department of Translational Research, University of Pisa, via Roma, 67, 56126, Pisa, Italy
| | - Rita Borgheresi
- Academic Radiology, Department of Translational Research, University of Pisa, via Roma, 67, 56126, Pisa, Italy
| | - Emanuele Neri
- Academic Radiology, Department of Translational Research, University of Pisa, via Roma, 67, 56126, Pisa, Italy
| | | | | |
Collapse
|
3
|
Miao J, Thongprayoon C, Suppadungsuk S, Krisanapan P, Radhakrishnan Y, Cheungpasitporn W. Chain of Thought Utilization in Large Language Models and Application in Nephrology. MEDICINA (KAUNAS, LITHUANIA) 2024; 60:148. [PMID: 38256408 PMCID: PMC10819595 DOI: 10.3390/medicina60010148] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 12/13/2023] [Revised: 12/31/2023] [Accepted: 01/11/2024] [Indexed: 01/24/2024]
Abstract
Chain-of-thought prompting enhances the abilities of large language models (LLMs) significantly. It not only makes these models more specific and context-aware but also impacts the wider field of artificial intelligence (AI). This approach broadens the usability of AI, increases its efficiency, and aligns it more closely with human thinking and decision-making processes. As we improve this method, it is set to become a key element in the future of AI, adding more purpose, precision, and ethical consideration to these technologies. In medicine, the chain-of-thought prompting is especially beneficial. Its capacity to handle complex information, its logical and sequential reasoning, and its suitability for ethically and context-sensitive situations make it an invaluable tool for healthcare professionals. Its role in enhancing medical care and research is expected to grow as we further develop and use this technique. Chain-of-thought prompting bridges the gap between AI's traditionally obscure decision-making process and the clear, accountable standards required in healthcare. It does this by emulating a reasoning style familiar to medical professionals, fitting well into their existing practices and ethical codes. While solving AI transparency is a complex challenge, the chain-of-thought approach is a significant step toward making AI more comprehensible and trustworthy in medicine. This review focuses on understanding the workings of LLMs, particularly how chain-of-thought prompting can be adapted for nephrology's unique requirements. It also aims to thoroughly examine the ethical aspects, clarity, and future possibilities, offering an in-depth view of the exciting convergence of these areas.
Collapse
Affiliation(s)
- Jing Miao
- Division of Nephrology and Hypertension, Department of Medicine, Mayo Clinic, Rochester, MN 55905, USA; (J.M.); (S.S.)
| | - Charat Thongprayoon
- Division of Nephrology and Hypertension, Department of Medicine, Mayo Clinic, Rochester, MN 55905, USA; (J.M.); (S.S.)
| | - Supawadee Suppadungsuk
- Division of Nephrology and Hypertension, Department of Medicine, Mayo Clinic, Rochester, MN 55905, USA; (J.M.); (S.S.)
- Chakri Naruebodindra Medical Institute, Faculty of Medicine Ramathibodi Hospital, Mahidol University, Samut Prakan 10540, Thailand
| | - Pajaree Krisanapan
- Division of Nephrology and Hypertension, Department of Medicine, Mayo Clinic, Rochester, MN 55905, USA; (J.M.); (S.S.)
- Division of Nephrology, Department of Internal Medicine, Faculty of Medicine, Thammasat University, Pathum Thani 12120, Thailand
- Division of Nephrology, Department of Internal Medicine, Thammasat University Hospital, Pathum Thani 12120, Thailand
| | - Yeshwanter Radhakrishnan
- Division of Nephrology and Hypertension, Department of Medicine, Mayo Clinic, Rochester, MN 55905, USA; (J.M.); (S.S.)
| | - Wisit Cheungpasitporn
- Division of Nephrology and Hypertension, Department of Medicine, Mayo Clinic, Rochester, MN 55905, USA; (J.M.); (S.S.)
| |
Collapse
|
4
|
Stott J, Wright T, Holmes J, Wilson J, Griffiths-Jones S, Foster D, Wright B. A systematic review of non-coding RNA genes with differential expression profiles associated with autism spectrum disorders. PLoS One 2023; 18:e0287131. [PMID: 37319303 PMCID: PMC10270643 DOI: 10.1371/journal.pone.0287131] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/16/2023] [Accepted: 05/30/2023] [Indexed: 06/17/2023] Open
Abstract
AIMS To identify differential expression of shorter non-coding RNA (ncRNA) genes associated with autism spectrum disorders (ASD). BACKGROUND ncRNA are functional molecules that derive from non-translated DNA sequence. The HUGO Gene Nomenclature Committee (HGNC) have approved ncRNA gene classes with alignment to the reference human genome. One subset is microRNA (miRNA), which are highly conserved, short RNA molecules that regulate gene expression by direct post-transcriptional repression of messenger RNA. Several miRNA genes are implicated in the development and regulation of the nervous system. Expression of miRNA genes in ASD cohorts have been examined by multiple research groups. Other shorter classes of ncRNA have been examined less. A comprehensive systematic review examining expression of shorter ncRNA gene classes in ASD is timely to inform the direction of research. METHODS We extracted data from studies examining ncRNA gene expression in ASD compared with non-ASD controls. We included studies on miRNA, piwi-interacting RNA (piRNA), small NF90 (ILF3) associated RNA (snaR), small nuclear RNA (snRNA), small nucleolar RNA (snoRNA), transfer RNA (tRNA), vault RNA (vtRNA) and Y RNA. The following electronic databases were searched: Cochrane Library, EMBASE, PubMed, Web of Science, PsycINFO, ERIC, AMED and CINAHL for papers published from January 2000 to May 2022. Studies were screened by two independent investigators with a third resolving discrepancies. Data was extracted from eligible papers. RESULTS Forty-eight eligible studies were included in our systematic review with the majority examining miRNA gene expression alone. Sixty-four miRNA genes had differential expression in ASD compared to controls as reported in two or more studies, but often in opposing directions. Four miRNA genes had differential expression in the same direction in the same tissue type in at least 3 separate studies. Increased expression was reported in miR-106b-5p, miR-155-5p and miR-146a-5p in blood, post-mortem brain, and across several tissue types, respectively. Decreased expression was reported in miR-328-3p in bloods samples. Seven studies examined differential expression from other classes of ncRNA, including piRNA, snRNA, snoRNA and Y RNA. No individual ncRNA genes were reported in more than one study. Six studies reported differentially expressed snoRNA genes in ASD. A meta-analysis was not possible because of inconsistent methodologies, disparate tissue types examined, and varying forms of data presented. CONCLUSION There is limited but promising evidence associating the expression of certain miRNA genes and ASD, although the studies are of variable methodological quality and the results are largely inconsistent. There is emerging evidence associating differential expression of snoRNA genes in ASD. It is not currently possible to say whether the reports of differential expression in ncRNA may relate to ASD aetiology, a response to shared environmental factors linked to ASD such as sleep and nutrition, other molecular functions, human diversity, or chance findings. To improve our understanding of any potential association, we recommend improved and standardised methodologies and reporting of raw data. Further high-quality research is required to shine a light on possible associations, which may yet yield important information.
Collapse
Affiliation(s)
- Jon Stott
- Child Oriented Mental Health Intervention Collaborative (COMIC), University of York in Collaboration with Leeds and York Partnership NHS Foundation Trust, York, United Kingdom
- Tees, Esk & Wear Valleys NHS Foundation Trust, Foss Park Hospital, York, United Kingdom
| | - Thomas Wright
- Manchester Centre for Genomic Medicine, Clinical Genetics Service, Saint Mary’s Hospital, Manchester University NHS Foundation Trust, Manchester, United Kingdom
- Division of Evolution, Infection and Genomics, School of Biological Sciences, Faculty of Biology, Medicine and Health, University of Manchester, Manchester, United Kingdom
| | - Jannah Holmes
- Child Oriented Mental Health Intervention Collaborative (COMIC), University of York in Collaboration with Leeds and York Partnership NHS Foundation Trust, York, United Kingdom
- Hull York Medical School, University of York, Heslington, York, United Kingdom
| | - Julie Wilson
- Department of Mathematics, University of York, Heslington, York, United Kingdom
| | - Sam Griffiths-Jones
- Division of Evolution, Infection and Genomics, School of Biological Sciences, Faculty of Biology, Medicine and Health, University of Manchester, Manchester, United Kingdom
| | - Deborah Foster
- Tees, Esk & Wear Valleys NHS Foundation Trust, Foss Park Hospital, York, United Kingdom
| | - Barry Wright
- Child Oriented Mental Health Intervention Collaborative (COMIC), University of York in Collaboration with Leeds and York Partnership NHS Foundation Trust, York, United Kingdom
- Hull York Medical School, University of York, Heslington, York, United Kingdom
| |
Collapse
|