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
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
Muesli Intake May Protect Against Coronary Artery Disease: Mendelian Randomization on 13 Dietary Traits.
Journal article
Park JK. et al, (2024), JACC Adv, 3