CANCELED – CompMed Seminar

Presenter: Sara Mostafavi, Ph.D.

Associate Professor in the Paul G. Allen School of Computer Science and Engineering at the University of Washington

Talk Title: “Deep Learning of Immune Cell Differentiation”

Abstract: The mammalian genome contains several million cis-regulatory elements, whose differential activity marked by open chromatin determines cellular differentiation. While the growing availability of functional genomics assays allows us to systematically identify cis-regulatory elements across varied cell types, how the DNA sequence of cis-regulatory elements is decoded and orchestrated on the genome-scale to determine cellular differentiation is beyond our grasp. In this talk, I’ll present recent work using machine learning as a tool to derive an understanding of the relationship between regulatory sequence and cellular function in the context of immune cell differentiation. In particular, I’ll present our deep learning approach (AI-TAC) to combining a large and granular compendium of epigenomic data and will describe approaches to robustly interpreting complex, black-box models in order to uncover mechanistic insights into immune gene regulation (Yoshida et al., Cell 2019; Maslova et al., PNAS 2020).  Our work shows that a deep learning approach to genome-wide chromatin accessibility can uncover patterns of immune transcriptional regulators that are directly coded in the DNA sequence, and thus providing a powerful in-silico framework (an in-silico assay of sorts) to mechanistically probe the relationship between a regulatory sequence and its function.

Bio: Since the fall of 2020, Sara Mostafavi is an Associate Professor in the Paul G. Allen School of Computer Science and Engineering, at the University of Washington (UW). Prior to joining UW, she was a faculty member in the Department of Statistics and Medical Genetics at the University of British Columbia (UBC), Canada. At UBC, she also held a Canada Research Chair in Computational Biology and was a recipient of a CIFAR AI Chair. She completed her Ph.D. in the Department of Computer Science at the University of Toronto and performed her postdoctoral research at Stanford University. Her research develops and applies machine learning and statistical methods for understanding the molecular basis of cellular function and human disease.