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      Network and Systems Medicine: Position Paper of the European Collaboration on Science and Technology Action on Open Multiscale Systems Medicine


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          Introduction: Network and systems medicine has rapidly evolved over the past decade, thanks to computational and integrative tools, which stem in part from systems biology. However, major challenges and hurdles are still present regarding validation and translation into clinical application and decision making for precision medicine.

          Methods: In this context, the Collaboration on Science and Technology Action on Open Multiscale Systems Medicine (OpenMultiMed) reviewed the available advanced technologies for multidimensional data generation and integration in an open-science approach as well as key clinical applications of network and systems medicine and the main issues and opportunities for the future.

          Results: The development of multi-omic approaches as well as new digital tools provides a unique opportunity to explore complex biological systems and networks at different scales. Moreover, the application of findable, applicable, interoperable, and reusable principles and the adoption of standards increases data availability and sharing for multiscale integration and interpretation. These innovations have led to the first clinical applications of network and systems medicine, particularly in the field of personalized therapy and drug dosing. Enlarging network and systems medicine application would now imply to increase patient engagement and health care providers as well as to educate the novel generations of medical doctors and biomedical researchers to shift the current organ- and symptom-based medical concepts toward network- and systems-based ones for more precise diagnoses, interventions, and ideally prevention.

          Conclusion: In this dynamic setting, the health care system will also have to evolve, if not revolutionize, in terms of organization and management.

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          Most cited references146

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          The FAIR Guiding Principles for scientific data management and stewardship

          There is an urgent need to improve the infrastructure supporting the reuse of scholarly data. A diverse set of stakeholders—representing academia, industry, funding agencies, and scholarly publishers—have come together to design and jointly endorse a concise and measureable set of principles that we refer to as the FAIR Data Principles. The intent is that these may act as a guideline for those wishing to enhance the reusability of their data holdings. Distinct from peer initiatives that focus on the human scholar, the FAIR Principles put specific emphasis on enhancing the ability of machines to automatically find and use the data, in addition to supporting its reuse by individuals. This Comment is the first formal publication of the FAIR Principles, and includes the rationale behind them, and some exemplar implementations in the community.
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            The Cancer Genome Atlas Pan-Cancer analysis project.

            The Cancer Genome Atlas (TCGA) Research Network has profiled and analyzed large numbers of human tumors to discover molecular aberrations at the DNA, RNA, protein and epigenetic levels. The resulting rich data provide a major opportunity to develop an integrated picture of commonalities, differences and emergent themes across tumor lineages. The Pan-Cancer initiative compares the first 12 tumor types profiled by TCGA. Analysis of the molecular aberrations and their functional roles across tumor types will teach us how to extend therapies effective in one cancer type to others with a similar genomic profile.
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              The Danish National Patient Registry: a review of content, data quality, and research potential

              Background The Danish National Patient Registry (DNPR) is one of the world’s oldest nationwide hospital registries and is used extensively for research. Many studies have validated algorithms for identifying health events in the DNPR, but the reports are fragmented and no overview exists. Objectives To review the content, data quality, and research potential of the DNPR. Methods We examined the setting, history, aims, content, and classification systems of the DNPR. We searched PubMed and the Danish Medical Journal to create a bibliography of validation studies. We included also studies that were referenced in retrieved papers or known to us beforehand. Methodological considerations related to DNPR data were reviewed. Results During 1977–2012, the DNPR registered 8,085,603 persons, accounting for 7,268,857 inpatient, 5,953,405 outpatient, and 5,097,300 emergency department contacts. The DNPR provides nationwide longitudinal registration of detailed administrative and clinical data. It has recorded information on all patients discharged from Danish nonpsychiatric hospitals since 1977 and on psychiatric inpatients and emergency department and outpatient specialty clinic contacts since 1995. For each patient contact, one primary and optional secondary diagnoses are recorded according to the International Classification of Diseases. The DNPR provides a data source to identify diseases, examinations, certain in-hospital medical treatments, and surgical procedures. Long-term temporal trends in hospitalization and treatment rates can be studied. The positive predictive values of diseases and treatments vary widely (<15%–100%). The DNPR data are linkable at the patient level with data from other Danish administrative registries, clinical registries, randomized controlled trials, population surveys, and epidemiologic field studies – enabling researchers to reconstruct individual life and health trajectories for an entire population. Conclusion The DNPR is a valuable tool for epidemiological research. However, both its strengths and limitations must be considered when interpreting research results, and continuous validation of its clinical data is essential.

                Author and article information

                Netw Syst Med
                Netw Syst Med
                Network and Systems Medicine
                Mary Ann Liebert, Inc., publishers (140 Huguenot Street, 3rd FloorNew Rochelle, NY 10801USA )
                July 2020
                06 July 2020
                : 3
                : 1
                : 67-90
                [ 1 ]Plateforme d'Exploration du Métabolisme, MetaboHUB Clermont, Université Clermont Auvergne, INRAE, UNH, Clermont-Ferrand, France.
                [ 2 ]TUM School of Life Sciences Weihenstephan (WZW), Technical University of Munich (TUM), Freising-Weihenstephan, Germany.
                [ 3 ]Holon Institute of Technology, Holon, Israel.
                [ 4 ]Institute of Vascular Biology and Thrombosis Research, Center for Physiology and Pharmacology, Medical University of Vienna, Vienna, Austria.
                [ 5 ]Medical Centre for Molecular Biology, Institute of Biochemistry, Faculty of Medicine, University of Ljubljana, Ljubljana, Slovenia.
                [ 6 ]Department of Clinical and Molecular Medicine, Norwegian University of Science and Technology, Trondheim, Norway.
                [ 7 ]The Cancer Clinic, St. Olav's University Hospital, Trondheim, Norway.
                [ 8 ]Digital Health Systems, Einsingen, Germany.
                [ 9 ]Department of Pharmacology and Personalised Medicine, Faculty of Health, Medicine and Life Science, Maastricht University, Maastricht, The Netherlands.
                [ 10 ]Department of Infection and Immunity, Luxembourg Institute of Health, Esch-sur-Alzette, Luxembourg.
                [ 11 ]Institute of Medical Microbiology, University Hospital Essen, University of Duisburg-Essen, Essen, Germany.
                [ 12 ]Department of Biology, Faculty of Natural Sciences, Norwegian University of Science and Technology, Trondheim, Norway.
                [ 13 ]Department of Computer Science and Systems Engineering, Universidad de La Laguna, Tenerife, Spain.
                [ 14 ]Centre for Functional Genomics and Bio-Chips, Institute of Biochemistry, Faculty of Medicine, University of Ljubljana, Ljubljana, Slovenia.
                [ 15 ]Department of Physiology and Biochemistry, Faculty of Medicine and Surgery, University of Malta, Msida, Malta.
                [ 16 ]CNR National Research Council, IAC Institute for Applied Computing, Rome, Italy.
                [ 17 ]GIGA-R Medical Genomics-BIO3, University of Liège, Liège, Belgium.
                [ 18 ]Georgetown University Medical Centre, Washington, District of Columbia, USA.
                [ 19 ]Northern Ireland Centre for Stratified Medicine, Ulster University, Londonderry, United Kingdom.
                [ 20 ]Department of Pharmacology and Personalised Medicine, Faculty of Health, Medicine and Life Science, MeHNS, Maastricht University, The Netherlands.
                Author notes
                [*] [ * ]Address correspondence to: Blandine Comte, PhD, Plateforme d'Exploration du Métabolisme, MetaboHUB Clermont, Université Clermont Auvergne, INRAE, UNH, Clermont-Ferrand F-63000, France, blandine.comte@ 123456inrae.fr
                © Blandine Comte et al. 2020; Published by Mary Ann Liebert, Inc.

                This Open Access article is distributed under the terms of the Creative Commons License ( http://creativecommons.org/licenses/by/4.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

                : Accepted May 18, 2020
                Page count
                Figures: 5, References: 150, Pages: 24
                Comprehensive Review

                big data,data integration,integrated health care,omics,systems medicine


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