Machine Learning for Precise Diagnostics and Therapeutics

SNACKS:
Refreshments will be served at 10:15 AM.

ABSTRACT:
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. My work 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 multi-omics to discover regulatory mechanisms governing gene expression. This approach relies on methodological innovation, developing new Granger causal inference techniques to capitalize on the simultaneous but separate measures of cell state. 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 by suggesting some collaborative directions of computational work, originating from these biological applications.

SPEAKER BIO:
Dr. Rohit Singh is an Assistant Professor in the Departments of Biostatistics & Bioinformatics, Cell Biology, and Electrical and Computer Engineering at Duke Univ. Dr. Singh's research interests lie in computational biology, with a focus on leveraging machine learning for in-depth analysis of cellular systems and enhancing drug discovery efficacy. His laboratory's primary research directions include the application of single-cell genomics and large language models to dissect disease mechanisms, understand biomolecular interactions, and discover novel drug targets and compounds. He is the recipient of the Test of Time Award at RECOMB, MIT's George M. Sprowls Award for his PhD thesis in Computer Science, and Stanford's Christopher Stephenson Memorial Award for Masters Research in the same field. In addition to academia, he has experience in the industry.