Kang‐Lin Hsieh

PerturbLDM predicts single-cell transcriptional responses to perturbations using a latent diffusion model, outperforming existing methods in drug-dose-cell line combinations and capturing key biological programs.

scEMB is a transformer-based model that learns gene embeddings from single-cell transcriptomics data, outperforming existing models in tasks like clustering and cell type annotation.