SOURCE-LINKED INTELLIGENCE

Vascular Inflammation early discoverieS through Transformative computational Approaches

CORDIS · observation · Publication date unknown

ing computational tools for early diagnosis and treatment, bridging the gap between preclinical research and clinical translation. VISTA integrates computational mechanics, chemo-mechano-biology, and machine learning to tackle the challenges of multifactorial diseases. By introducing groundbreaking in silico tools, it seeks to reshape our understanding of VI, advancing preventive and personalized cardiovascular medicine. Key challenges in VI include: (1) Current approaches struggle to synthetise the wealth of evidence on the interplay between biomechanics, biology, and systemic processes. (2) Translating causal insights into clinical applications requires linking evidence to precise mechanisms. (3) Clinical approaches and preclinical research remain rooted in traditional theories, lacking support from in silico tools that fail to incorporate real-world perspectives. VISTA tackles these challenges through three objectives: (1) Developing a graph-informed modelling approach to create virtual patient models by synthesizing evidence-based networks of multiscale interactions. (2) Uncove

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recordType

award

status

SIGNED

region

EU

value

1997500

unit

EUR