Multiomics tools for improved atherosclerotic cardiovascular disease management
Sopic, Miron; Vilne, Baiba; Gerdts, Eva; Trindade, Fábio; Uchida, Shizuka; Khatib, Soliman; Wettinger, Stephanie Bezzina; Devaux, Yvan; Magni, Paolo
Journal article, Peer reviewed
Published version
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Åpne
Permanent lenke
https://hdl.handle.net/11250/3143972Utgivelsesdato
2023Metadata
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- Department of Clinical Science [2374]
- Registrations from Cristin [10149]
Sammendrag
Multiomics 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.