Sara is a second year Machine Learning PhD student at Queen Mary University of London, where she previously graduated with a BSc in Biomedical Sciences. In her final year, she worked on a data analysis project focused on understanding the behaviour of a population of cells crucial in immunity. During her time at university she also independently learned Python and R as she became more and more interested in the data generated by her own project.
Sara’s current research focuses on better understanding the immune response in Trauma patients. Trauma is currently a leading cause of death worldwide and it is often characterised by immune system dysregulation, recurrent infections and Multiple Organ Dysfunction Syndrome (MODS). Correctly recognising patients at high risk of adverse outcomes at admission would improve patients’ survival and reduce the chances of complications.
During her project, she will apply Machine Learning (ML) such as Ensemble based models and Deep Learning models to omics data to provide insights into the hyper-acute window phase that follows critical injuries. This will help identifying biomarkers of adverse outcomes as well as molecular and transcriptomic signatures that promote specific immune cells phenotypes and different disease trajectories. Importantly, the identification of transcriptomic signatures that associate with differential disease trajectories will advise on diagnostic gene-sets that predict adverse clinical complications. Identifying such patient sub-groups will enable stratified patient care.