Multiomics tools for improved atherosclerotic cardiovascular disease management

EU-AtheroNET COST Action CA21153, Miron Sopic, Baiba Vilne, Eva Gerdts, Yvan Devaux (Corresponding Author), Paolo Magni (Corresponding Author)

Research output: Contribution to journalReview articlepeer-review

7 Citations (Scopus)
8 Downloads (Pure)

Abstract

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.

Original languageEnglish
Pages (from-to)983-995
Number of pages13
JournalTrends in Molecular Medicine
Volume29
Issue number12
Early online dateJun 2023
DOIs
Publication statusPublished - Dec 2023

Keywords*

  • artificial intelligence
  • atherosclerotic cardiovascular disease
  • data integration
  • machine learning
  • multiomics

Field of Science*

  • 3.3 Health sciences
  • 1.2 Computer and information sciences

Publication Type*

  • 1.1. Scientific article indexed in Web of Science and/or Scopus database

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