Multi-omics prediction of clinical outcomes for precision oncology applications

This multidisciplinary project develops, tests and implements novel practices of how to use artificial intelligence (AI) and machine learning (ML) models in translational and clinical studies. The modeling goal is to develop novel supervised learning approaches to select multi-omic features predictive of clinical outcomes for individual patients using efficient AI / ML models that maximize the accuracy of the predicted outcome, using minimal panel of predictive features. The medical question is how to improve treatment outcomes, and how to do this cost-effectively to minimize the burden on public health expenditure.

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