Machine Learning for Precise Diagnostics and Therapeutics

Drug discoveries have been instrumental in improving global health over the last century, but the median drug now takes about 10 years to bring to market and costs over a billion dollars to develop. The work in our lab aims to expedite the development of precise diagnostics and therapeutics by applying machine learning. In this talk, I will outline two recent research directions. In the first part, we use single-cell multiomics to discover regulatory mechanisms governing the interaction between the epigenome, transcription factors, and target genes. In the second part, I will introduce the application of large language models to model protein interaction and function. These protein language models enable powerful new approaches to predicting and understanding protein-protein and protein-drug interactions. I will conclude with a prospective look at how similar methods may help answer foundational questions in both basic and translational science.