Kaiyuan Yang

SPgen predicts spatial protein expression across the proteome using multi-modal foundation models, improving accuracy and reducing noise in spatial proteomics data.

This study introduces DGP-AMIO, a graph attention network-based method that integrates multi-omics and gene interaction data to identify novel disease genes, achieving high performance across 20 disease datasets.

stFormer is a transformer model for spatial transcriptomics data that improves performance on cell clustering and gene function prediction compared to existing models.

GexMolGen generates hit-like molecules using gene expression signatures through a cross-modal approach, producing valid molecules from both in- and out-of-domain transcriptome data.