DPhil student
Research groups
Colleges
Ben Omega Petrazzini
BSc
DPhil student
Global cardiovascular health and artificial intelligence
Biography
After my BS in Biology at Uruguay’s Universidad de la Republica (UdelaR), I joined The Icahn School of Medicine at Mount Sinai to study cardiovascular disease (CVD) genetics and prediction using artificial intelligence (AI).
Now, as a DPhil student, I am studying the use of AI for individualized CVD risk estimations. My goal is to develop a tool to accurately predict CVD risk in any country using a simple blood test. Such a tool would enable CVD care outside hospitals with greater benefits for marginalized populations with limited access to healthcare and high CVD burden.
My work is co-supervised by Prof. Rahimi (Oxford), Dr. Rao (Oxford) and Prof. Di Angelntonio (Cambridge), and funded by the Clarendon Fund and Uruguay’s Agencia Nacional de Investigacion e Innovacion.
Additionally, I serve as the 2024/2025 Graduate Director of Innovation and Entrepreneurship at Reuben College.
Key publications
Exome sequence analysis identifies rare coding variants associated with a machine learning-based marker for coronary artery disease.
Journal article
Petrazzini BO. et al, (2024), Nat Genet, 56, 1412 - 1419
Development of a human genetics-guided priority score for 19,365 genes and 399 drug indications.
Journal article
Duffy Á. et al, (2024), Nat Genet, 56, 51 - 59
Machine learning-based marker for coronary artery disease: derivation and validation in two longitudinal cohorts.
Journal article
Forrest IS. et al, (2023), Lancet, 401, 215 - 225
Coronary Risk Estimation Based on Clinical Data in Electronic Health Records.
Journal article
Petrazzini BO. et al, (2022), J Am Coll Cardiol, 79, 1155 - 1166
Prediction of recessive inheritance for missense variants in human disease
Preprint
Petrazzini BO. et al, (2021)
Recent publications
Characterizing the Field of Mendelian Randomization Studies for Cardiovascular Disease.
Journal article
Petrazzini BO., (2026), Cardiology, 151, 54 - 56
Machine learning-based penetrance of genetic variants.
Journal article
Forrest IS. et al, (2025), Science, 389
Expanding drug targets for 112 chronic diseases using a machine learning-assisted genetic priority score.
Journal article
Chen R. et al, (2024), Nat Commun, 15
Comparison of blood-based liver fibrosis scores in the Mount Sinai Health System, MASLD Registry, and NHANES 2017-2020 study.
Journal article
Chen R. et al, (2024), Hepatol Commun, 8
Exome sequence analysis identifies rare coding variants associated with a machine learning-based marker for coronary artery disease.
Journal article
Petrazzini BO. et al, (2024), Nat Genet, 56, 1412 - 1419