Haotian Cui

A reasoning LLM combining ESM3 embeddings with GO-GPT term predictions produces protein function annotations that experts rated tie-or-better than UniProt in 79% of cases (SFT model).

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

LUMI-lab autonomously designs ionizable lipids for mRNA delivery, identifying brominated tails and achieving 20.3% gene editing efficacy in mice.

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.

MethylGPT is a transformer-based model trained on human DNA methylation data that accurately predicts methylation values and improves age and disease prediction.

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.

DeepVelo estimates cell-specific transcriptional kinetics in scRNA-seq data using GCNs, enabling analysis of complex differentiation processes with time-varying rates.

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