Yimin Fan

Navigo models mouse embryogenesis to predict cell fates and disease by learning developmental trajectories from scRNA-seq data.

GFETM improves scATAC-seq modeling by integrating genome foundation model embeddings, revealing cell-state specific TF activity and kidney diabetes epigenomic signatures.

This paper introduces MorphDiff, a transcriptome-guided diffusion model that predicts cell morphology changes under perturbations and improves MOA identification.

cfDecon improves cfDNA deconvolution accuracy and interpretability using deep learning, outperforming existing methods in simulation and clinical datasets.