Discovering Interpretable Biological Concepts in Single-cell RNA-seq Foundation Models

Recent advancements in single-cell RNA-seq foundation models are paving the way for significant breakthroughs in biological discovery. While these models have shown impressive performance, their black-box nature has posed challenges for interpretation. However, new techniques like sparse dictionary learning are emerging, allowing researchers to extract meaningful biological concepts from these complex models. This is crucial as it enhances our understanding of biological sequences, ultimately leading to better applications in fields like biomedical imaging and protein modeling.