Area of research
Molecular Biology · Biophysics
Research interest
Research interests include Computer science, Computational biology, Spatial analysis, Omics, Robustness (evolution), and Data mining.
Stereopy: modeling comparative and spatiotemporal cellular heterogeneity via multi-sample spatial transcriptomics
Systematic inference of super-resolution cell spatial profiles from histology images
Bridging epigenomics and tumor immunometabolism: molecular mechanisms and therapeutic implications
Cancer therapy resistance from a spatial‐omics perspective
LINS: A general medical Q&A framework for enhancing the quality and credibility of LLM-generated responses
High-parameter spatial multi-omics through histology-anchored integration
Benchmarking spatial clustering methods with spatially resolved transcriptomics data
MENDER: fast and scalable tissue structure identification in spatial omics data
stMMR: accurate and robust spatial domain identification from spatially resolved transcriptomics with multimodal feature representation
SODB facilitates comprehensive exploration of spatial omics data
SPIRAL: integrating and aligning spatially resolved transcriptomics data across different experiments, conditions, and technologies
Spatial-ID: a cell typing method for spatially resolved transcriptomics via transfer learning and spatial embedding
SOTIP is a versatile method for microenvironment modeling with spatial omics data