Area of research
Radiology, Nuclear Medicine and Imaging · Oncology
Research interest
Research interests include Computer science, Biology, Computational biology, Artificial intelligence, Deep learning, and Graph.
Systematic evaluation of computational methods for cell segmentation
Identifying T cell antigen at the atomic level with graph convolutional network
Charting the spatial transcriptome of the human cerebral cortex at single-cell resolution
TriCLFF: a multi-modal feature fusion framework using contrastive learning for spatial domain identification
Deciphering cell–cell communication at single-cell resolution for spatial transcriptomics with subgraph-based graph attention network
The Deep Learning Framework iCanTCR Enables Early Cancer Detection Using the T-cell Receptor Repertoire in Peripheral Blood
Dimension reduction, cell clustering, and cell–cell communication inference for single-cell transcriptomics with DcjComm
Functional requirement of alternative splicing in epithelial-mesenchymal transition of pancreatic circulating tumor
Identification of shared characteristics in tumor-infiltrating T cells across 15 cancers
DeepST: identifying spatial domains in spatial transcriptomics by deep learning
SDN2GO: An Integrated Deep Learning Model for Protein Function Prediction
DeepMiR2GO: Inferring Functions of Human MicroRNAs Using a Deep Multi-Label Classification Model