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.
Multiobjective optimization identifies cancer-selective combination therapies
PLoS Comput Biol, 16 (12), e1008538
Systematic mapping of cancer cell target dependencies using high-throughput drug screening in triple-negative breast cancer
Comput Struct Biotechnol J, 18, 3819-3832
Noncanonical effector functions of the T-memory-like T-PLL cell are shaped by cooperative TCL1A and TCR signaling
Blood, 136 (24), 2786-2802