Transformative AI for Deep Mining of Omics and Literature Data

About this Class

Dr. Fuhai Li, Associate Professor at the School of Medicine and Computer Science & Engineering, Washington University, will present novel approaches that combine large language models (LLMs) with graph-based AI to integrate and analyze vast omics datasets. These approaches enable the identification of disease targets, mapping of signaling pathways, and prediction of effective drug combinations. The key component of this novel AI system is the text-numeric graph (TNG), a structure in which graph entities and associations carry both textual and numeric attributes.

Dr. Li will introduce an AI multi-agent system developed to accelerate biomedical discovery by unifying: Omics data analysis, literature-based deep search, and reasoning capabilities for generating novel scientific hypotheses.

The presentation will showcase applications of these innovative AI tools through analysis of heterogeneous pharmacogenomics data for cancer research.