Fall 2026 Missouri Data Science and Informatics Symposium

AI for Biomedical Knowledge Discovery: Integrating Literature, Molecular, and Clinical Data


The rapid growth of biomedical data across scientific literature, molecular profiling, and clinical records presents both unprecedented opportunities and significant challenges for knowledge discovery. In this talk, I present a unified perspective on how artificial intelligence can help uncover meaningful insights across heterogeneous biomedical data modalities. Drawing on selected projects from my doctoral research, I highlight three examples spanning different scales of analysis. First, I discuss biomedical literature mining approaches that integrate image and text data to extract structured knowledge from pathway figures and publications. Second, I present recent work on fine-tuning foundation models for single-cell data to improve biological representation learning and cross-dataset generalization. Finally, I describe ongoing clinical NLP research that combines large language models with knowledge graphs to support reasoning over real-world clinical narratives. Together, these examples illustrate how AI methods can bridge fragmented biomedical information and support interdisciplinary discovery across research and clinical domains. I conclude with reflections on emerging opportunities for more interpretable and translational AI systems.