My research focuses on problems in gastrointestinal cancer prevention by utilising mathematical models of inflammatory bowel disease and Barrett's oesophagus.
Our work integrates multi-scale data with mathematical modelling to understand carcinogenesis and inform cancer screening guidelines.
The aims of our current research are to use mathematical mechanistic modelling to inform optimal cancer screening recommendations, to perform patient risk stratification, and to ultimately better prognostication.
Our methodologies integrate bioinformatics using Big Data, stochastic modelling, survival analysis using public health record data, and statistical inference to address cancer prevention problems such as early detection.
Translational projects aim to elicit direct patient benefit, in part by the use and validation of web-based risk prediction tools to help clinicians and patients alike better understand cancer risk, particularly in inflammatory bowel disease (IBD). The ultimate goal is to improve precision medicine with cancer evolution theory and modelling approaches.
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