Generative AI for Modeling Single-cell State and Response
Data Science Seminar Series
October 16, 2024 | 11:00 AM – 12:00 PM
Virtual
In this upcoming webinar, hear Dr. Fabian Theis discuss how artificial intelligence (AI) is enabling researchers to model single-cell variation, potentially creating a single-cell foundation model.
He will:
- review deep learning approaches for identifying gene expression.
- outline applications for cell atlas building.
- address concerns (such as variations in drug responses and multiscale readouts).
- explain organism-wide cell type predictors.
- review the future of foundation models and their potential impact on spatial omics for modeling the cellular niche.
Thanks to advances in single-cell genomics, researchers can construct large-scale organ atlases, giving you more accurate ways to study genetic mutations and alterations related to drug responses and disease. These models create a unique opportunity for using AI to better understand cellular responses, using both multiomic and spatial data.
About the Speaker
Fabian Theis, Ph.D.
Dr. Theis is the director of the Helmholtz Munich Computational Health Center and scientific director of HelmholtzAI, as well as a professor at the Technical University of Munich. His research focuses on mathematical modeling of biological systems with applications to computational health and single-cell biology.
About the Data Science Seminar Series
CBIIT’s Data Science Seminar Series is dedicating its 2026 events to spotlighting the use of AI in cancer research and care. Brought to you by CBIIT and NCI's Division of Cancer Treatment and Diagnosis AI working group, the upcoming webinars will explore a variety of questions, such as the following:
- How can AI be used for diagnosis, treatment, or omics research?
- What are the related laws and ethical considerations for AI?
- How can we empower an AI-ready cancer research community through workforce development, collaborations, and funding?
To view upcoming speakers or recordings of past presentations, visit the Data Science Seminar Series page.