AI Approaches for Multi-Modality Genomics and Image in Biomedicine

This seminar will investigate cutting-edge AI methodologies that integrate transcriptomics and histological imaging to advance biomedical research. Topics include approaches for inferring dynamic cell-state transitions from single-cell RNA sequencing, strategies for achieving single-cell resolution spatial transcriptomics by integrating transcriptomic and tissue morphological data, and foundational models linking histological images with transcriptomics to enable comprehensive tissue and molecular analysis. By addressing challenges in multi-stage cellular transitions, spatial resolution, and omics-image integration, these advancements demonstrate the transformative potential of AI in decoding complex biological systems and uncovering new insights into cellular organization, tissue heterogeneity, and disease mechanisms.