Multiomics tools for improved atherosclerotic cardiovascular disease management

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dc.contributor.authorEU-AtheroNET COST Action CA21153
dc.contributor.authorSopic, Miron
dc.contributor.authorVilne, Baiba
dc.contributor.authorGerdts, Eva
dc.contributor.authorDevaux, Yvan
dc.contributor.authorMagni, Paolo
dc.contributor.institutionBioinformatics Group
dc.date.accessioned2023-12-01T02:05:01Z
dc.date.available2023-12-01T02:05:01Z
dc.date.issued2023-12
dc.descriptionFunding Information: This article is based upon work from EU-AtheroNET COST Action CA21153 funded by European Cooperation in Science and Technology (COST). M.S. is supported by the Ministry of Education, Science and Technological Development , Republic of Serbia through Grant Agreement with University of Belgrade-Faculty of Pharmacy No: 451-03-9/2021-14/200161, European Union (HORIZON-MSCA-2021-SE-01-01 - MSCA Staff Exchanges 2021 CardioSCOPE 101086397, HORIZON-MSCA-2021-PF- MAACS 101064175). E.G. is supported by the Research Council of Norway co-founding of the European Union AtheroNET COST Action CA21153. F.T. is supported by a postdoctoral research grant by the Portuguese Foundation for Science and Technology (FCT) through Cardiovascular R&D Center (UnIC, UIDP/00051/2020). S.K. is supported by a Tel-Hai college fellowship and the Israel Innovation Authority. S.B.W. is supported by the MCST COVID-19 R&D Fund 2020 COV.RD.2020-11: TargetID, HORIZON-WIDERA-2022-TALENTS-01 Project 101086768 BioGeMT and HORIZON EIC 2022 PATHFINDER CHALLENGE CARDIOGENOMICS Project 101114924 TargetMI, the latter two being funded by the European Union . However, views and opinions expressed are those of the author only and do not necessarily reflect those of the European Union or of the granting authorities. Neither the European Union nor the granting authorities can be held responsible for them. Y.D. has received funding from the EU Horizon 2020 project COVIRNA (grant agreement # 101016072 ), the National Research Fund (grants # C14/BM/8225223 , C17/BM/11613033 and COVID-19/2020-1/14719577/miRCOVID ), the Ministry of Higher Education and Research, and the Heart Foundation-Daniel Wagner of Luxembourg. P.M. is supported by the European Union (AtheroNET COST Action CA21153; HORIZON-MSCA-2021-SE-01-01 - MSCA Staff Exchanges 2021 CardioSCOPE 101086397). Publisher Copyright: © 2023 The Authors
dc.description.abstractMultiomics studies offer accurate preventive and therapeutic strategies for atherosclerotic cardiovascular disease (ASCVD) beyond traditional risk factors. By using artificial intelligence (AI) and machine learning (ML) approaches, it is possible to integrate multiple ‘omics and clinical data sets into tools that can be utilized for the development of personalized diagnostic and therapeutic approaches. However, currently multiple challenges in data quality, integration, and privacy still need to be addressed. In this opinion, we emphasize that joined efforts, exemplified by the AtheroNET COST Action, have a pivotal role in overcoming the challenges to advance multiomics approaches in ASCVD research, with the aim to foster more precise and effective patient care.en
dc.description.statusPeer reviewed
dc.format.extent13
dc.format.extent709838
dc.identifier.citationEU-AtheroNET COST Action CA21153, Sopic, M, Vilne, B, Gerdts, E, Devaux, Y & Magni, P 2023, 'Multiomics tools for improved atherosclerotic cardiovascular disease management', Trends in Molecular Medicine, vol. 29, no. 12, pp. 983-995. https://doi.org/10.1016/j.molmed.2023.09.004
dc.identifier.doi10.1016/j.molmed.2023.09.004
dc.identifier.issn1471-4914
dc.identifier.otherMendeley: b29dcd76-c179-3886-850a-3fe005866d2f
dc.identifier.urihttps://dspace.rsu.lv/jspui/handle/123456789/15019
dc.identifier.urlhttp://www.scopus.com/inward/record.url?scp=85173765046&partnerID=8YFLogxK
dc.identifier.urlhttp://www.ncbi.nlm.nih.gov/pubmed/37806854
dc.identifier.urlhttps://www.mendeley.com/catalogue/b29dcd76-c179-3886-850a-3fe005866d2f/
dc.language.isoeng
dc.relation.ispartofTrends in Molecular Medicine
dc.rightsinfo:eu-repo/semantics/openAccess
dc.subjectartificial intelligence
dc.subjectatherosclerotic cardiovascular disease
dc.subjectdata integration
dc.subjectmachine learning
dc.subjectmultiomics
dc.subject3.3 Health sciences
dc.subject1.2 Computer and information sciences
dc.subject1.1. Scientific article indexed in Web of Science and/or Scopus database
dc.subjectMolecular Medicine
dc.subjectMolecular Biology
dc.subjectSDG 3 - Good Health and Well-being
dc.titleMultiomics tools for improved atherosclerotic cardiovascular disease managementen
dc.type/dk/atira/pure/researchoutput/researchoutputtypes/contributiontojournal/systematicreview

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