Cheng Jin

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.

AttSiOff improves siRNA design by predicting inhibition and off-target effects using self-attention mechanisms, achieving better accuracy and generalization than existing methods.

DeepSipred predicts siRNA inhibition using sequence, thermodynamic, and expert knowledge features, achieving state-of-the-art performance on public datasets.