AI & Machine Learning

LSP investigators leverage cutting-edge ML/AI approaches to enhance biological understanding of diseases, identify therapeutic targets, and improve clinical decision-making. 

LSP investigators harness the latest machine learning and artificial intelligence (ML/AI) methods to unravel complex biology and derive novel insights that can improve therapeutic development. The LSP approaches scientific questions from a systems biology framework. We develop and use novel imaging and high-throughput screening methods that yield rich biological datasets that can be challenging to interpret. Machine learning methods can help derive insights from these datasets, allowing LSP investigators to follow up on specific hypotheses for more rigorous evaluation.

For our tissue imaging datasets, we use human-in-the-loop ML/AI methods to derive sets of features associated with particular outcomes. We have leveraged these models to identify potential prognostic biomarkers for cancer progression and to understand (and predict) how tumors will respond to immunotherapy. 

We also use ML/AI tools to understand polypharmacology - the phenomenon of drugs interacting with multiple proteins - which plays an essential role in drug action, especially for multi-enzyme families like kinases and deubiquitinases. Our open-source methods can predict protein structure (OpenFold) and protein-drug docking interactions (KinCo), as well as extract and assemble mechanistic biological knowledge from published literature (INDRA). These efforts have been useful for understanding therapeutic resistance and sensitivity in cancer, developing novel analgesics, and identifying new approaches to treating neurodegenerative disease.