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Network for implementing multiomic approaches in atherosclerotic cardiovascular disease prevention and research

Description of the Action

AtheroNET aims to consolidate and connect experts from different fields into European and international network that will focus on the use of multiple omics technologies and data integration through machine learning/artificial intelligence ML/AI approach to bring novel paradigms in prevention, diagnosis, and treatment of atherosclerotic cardiovascular disease (ASCVD).

The latest epidemiological data suggest that cardiovascular diseases (CVD) are still the leading cause of morbidity and mortality worldwide. In order to improve the CVD outcomes, we need new strategies that incorporate the complex interplay of different driving forces behind atherosclerosis pathophysiology in addition to the traditional risk factors.

Meet the Working Groups

Pathophysiological mechanisms in atherosclerosis

WG1 (Co)-Leaders:

  • Dimitris Kardassis
  • Tijana Mitić
  • Nuria Amigo

Personalized models for clinical applications in ASCVD

WG2 (Co)-Leaders:

  • Alberico Catapano
  • Luis Masana
  • Liv Tybjærg Nordestgaard

Standardization and harmonization of research

WG3 (Co)-Leaders:

  • Marie Mardal
  • David de Gonzalo Calvo
  • Fabio Trindade

Data integration and ML/AI

WG4 (Co)-Leaders:

  • Aleksandra Gruca
  • Artemis Hatzigeorgiou
  • Baiba Vilne

Dissemination and communication

WG5 (Co)-Leaders:

  • Georgios Kararigas
  • Brooke Wolford
  • Zulema Rodriguez Hernandez

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