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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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          Abstract

          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 references152

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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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              Chronic inflammation in the etiology of disease across the life span

              Although intermittent increases in inflammation are critical for survival during physical injury and infection, recent research has revealed that certain social, environmental and lifestyle factors can promote systemic chronic inflammation (SCI) that can, in turn, lead to several diseases that collectively represent the leading causes of disability and mortality worldwide, such as cardiovascular disease, cancer, diabetes mellitus, chronic kidney disease, non-alcoholic fatty liver disease and autoimmune and neurodegenerative disorders. In the present Perspective we describe the multi-level mechanisms underlying SCI and several risk factors that promote this health-damaging phenotype, including infections, physical inactivity, poor diet, environmental and industrial toxicants and psychological stress. Furthermore, we suggest potential strategies for advancing the early diagnosis, prevention and treatment of SCI.

                Author and article information

                Journal
                Netw Syst Med
                Netw Syst Med
                nsm
                Network and Systems Medicine
                Mary Ann Liebert, Inc., publishers (140 Huguenot Street, 3rd FloorNew Rochelle, NY 10801USA )
                2690-5949
                July 2020
                2020
                06 July 2020
                : 3
                : 1
                : 67-90
                Affiliations
                [ 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
                Article
                10.1089/nsm.2020.0004
                10.1089/nsm.2020.0004
                7500076
                32954378
                d0b7d00c-c49b-44b6-8a49-d43c1b62b720
                © 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.

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

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

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