Single-Cell Foundation Models and Cell Knowledge Base Construction
Date:
Research preview presentation on building foundation models for single-cell analysis and constructing comprehensive cell knowledge bases. Introduced the concept of cell-level language models trained on millions of single-cell transcriptomic profiles. Discussed pre-training strategies, including masked cell prediction, contrastive learning on cell embeddings, and multi-task learning for cell type annotation, trajectory inference, and perturbation prediction. Covered the development of a unified cell atlas integrating data from multiple tissues, species, and technologies. Explored applications in automated cell annotation, novel cell type discovery, and understanding cellular states in disease.