Single Cell Seminar Series
Building Foundation Models for Single-Cell Omics and Imaging
Seminar Series Details:
About Bo Wang (University Health Network, Canada)
Dr. Bo Wang is a tenured Associate Professor in the Departments of Computer Science and Laboratory Medicine & Pathobiology at the University of Toronto, and the inaugural Temerty Professor in AI Research and Education in Medicine. He serves as Senior Vice President and Head of Biomedical AI at Xaira Therapeutics, as well as Chief AI Scientist at the University Health Network, Canada’s largest research hospital. Dr. Wang also holds a CIFAR AI Chair at the Vector Institute. He received his PhD in Computer Science from Stanford University in 2017. His research lies at the intersection of machine learning, computational biology, and computer vision, with a focus on their applications in biomedicine. Dr. Wang’s pioneering work in these fields has been recognized through numerous prestigious honours, including the Gairdner Early Career Researcher Award and a Canada Research Chair.
About this Class
This talk delves into the innovative utilization of generative AI in propelling biomedical research forward. By harnessing single-cell sequencing data, we developed scGPT, a foundational model that extracts biological insights from an extensive dataset of over 33 million cells. Analogous to how words form text, genes define cells, effectively bridging the technological and biological realms. The strategic application of scGPT via transfer learning significantly boosts its efficacy in diverse applications such as cell-type annotation, multi-batch integration, and gene network inference.
Additionally, the talk will spotlight MedSAM, a state-of-the-art segmentation foundational model. Designed for universal application, MedSAM excels across various medical imaging tasks and modalities. It showcased unprecedented advancements in 30 segmentation tasks, outperforming existing models considerably. Notably, MedSAM possesses the unique ability for zero-shot and few-shot segmentation, enabling it to identify previously unseen tumor types and swiftly adapt to novel imaging modalities. Collectively, these breakthroughs emphasize the importance of developing versatile and efficient foundational models. These models are poised to address the expanding needs of imaging and omics data, thus driving continuous innovation in biomedical analysis.