single-cell cross-modality prediction
leverage large-scale public datasets and well-characterized data modalities to enhance insights into under-characterized modalities using deep learning
Cellular function are coordinated through dynamic interactions among diverse molecular entities, including genes, proteins, and regulatory DNA elements. However, experimental technologies typically capture only partial, static snapshots of this complex regulatory landscape. Our lab develops deep learning methods that leverage large-scale public datasets to reconstruct multimodal cellular profiles and infer the dynamic relationships between molecular modalities, enabling a more comprehensive understanding of gene regulation and cellular state.