Multi-Modal Learning for Complex Biological Systems: Challenges and Opportunities
Date:
Keynote address at the opening ceremony of national symposium on multi-modal biological data integration. Outlined the landscape of multi-modal learning challenges in modern biology, including integrating genomics, transcriptomics, proteomics, metabolomics, and imaging data. Discussed emerging architectures such as cross-modal transformers, multi-modal graph neural networks, and modality-specific encoders with shared latent spaces. Presented case studies on drug-cell interaction modeling, spatial transcriptomics analysis, and multi-omics disease prediction. Emphasized the need for standardized benchmarks, interpretability frameworks, and collaborative data sharing initiatives to advance the field.