Chloe Wang

X-Cell predicts causal perturbation responses across diverse cellular contexts using a diffusion language model trained on a large CRISPRi Perturb-seq dataset.

Further pretraining a single-cell foundation model on 30 million spatial profiles, with a protocol-aware mixture-of-experts decoder and a neighbourhood objective, supports multi-slide integration, deconvolution and imputation.

A generative transformer pretrained on over 33 million cells can be fine-tuned for cell-type annotation, batch and multi-omic integration, perturbation prediction and gene network inference, claiming superior performance.

scFormer uses transformers to learn cell and gene embeddings for single-cell data, achieving state-of-the-art results on multiple tasks.