Transformative AI for Deep Mining of Omics and Literature Data

Are you combining data science with cancer research in your work? Join Dr. Fuhai Li, from Washington University in St. Louis, to learn about the transformative AI models his team developed to unlock insights from large-scale biomedical data. He’ll present novel approaches that combine large language models with graph-based AI to integrate and analyze omics data sets.

These methods will help you:

• identify key targets,
• map signaling pathways, and
• predict effective drug combinations.

The key component of this AI system is the text-numeric graph, where graph entities and associations carry both textual and numeric attributes. Dr. Li will also introduce you to an AI-multi-agent system for unifying omics data analysis, literature-based deep search, and reasoning to generate novel scientific hypotheses. He’ll demonstrate these tools in action by analyzing pharmacogenomics cancer data.

This webinar is part of the NIH Data Sharing and Reuse Seminar Series, which is hosted by the NIH Office of Data Science and Strategy. The monthly series highlights researchers who have taken existing data and found clever ways to reuse the data or generate new findings.