Mapping genetic traits of cardiovascular disease Identifying new genetic risk factors for cardiovascular disease through the development of new learning tools, to aid treatment
Modelling the heart's chemical signals Obtaining a more complete statistical description of the heart's chemical signals to improve medical treatment
Machine learning and large cryogenic electron microscopy data sets Applying machine learning techniques to identify interacting protein molecules in large cryogenic electron microscopy (cryo-EM) data
Blood related risk factors for cardiovascular disease Improving understanding of genetic, blood related cardiovascular risk factors through algorithmic analysis
Personalised risk management of cardiovascular disease Using cutting-edge statistical and machine learning methods to identify individual cardiovascular risks
Heart attack risk prediction and treatment management Using machine learning to predict risk and to inform patient treatment after heart attacks
Risk prediction in adult cardiac surgery Using machine learning to identify patients who are likely to not survive open heart surgery in the UK