Fangjiang Wu

SpatialOmicsLab provides an integrated environment with AI tools for spatial transcriptomics analysis, outperforming expert workflows in multiple tasks.

The paper introduces a Bayesian framework to map cell-cell interactions from spatial transcriptomics data, revealing new biological insights in cancer and immune responses.

This paper develops pMTnet-omni, an AI system predicting TCR-pMHC binding across species and MHC classes, achieving high accuracy and showing clinical utility in predicting immune-related adverse events.

This paper benchmarks 21 dimension reduction methods on CyTOF data, finding SAUCIE, SQuaD-MDS, and scvis as top performers with different strengths.