Max Schuran
In this project, a novel machine-learning algorithm will be developed to address shortcomings in the predictive performance of PRS.
Using machine learning to improve polygenic risk scores (PRSs) prediction of colorectal cancer
Max Schuran
Principal Supervisor’s name: Professor Enes Makalic
Co-supervisor’s names: Dr Gillian Dite, Dr Benjamin Goudey, Prof John Hopper
The predictive performance of PRS developed using standard methods can be limited for some conditions, such as colorectal cancer. Additionally, the predictive performance for people of non-European ancestry may be reduced, since most PRS have been developed in studies of people of European ancestry. In this project, a novel machine-learning algorithm will be developed to address both shortcomings.
Source of Funding: National Industry PhD program (NiPhD) Scholarship