Mingxuan Zhang

SHERLOCK is a deep generative framework for single-cell perturbation analysis that learns interpretable perturbation representations and enables causal analysis of downstream effects.

GREmLN is a foundation model that uses graph structure to improve gene embeddings in single-cell transcriptomics, outperforming baselines in cell type annotation and perturbation prediction.

Echidna quantifies gene dosage effects on phenotypic plasticity by integrating scRNA-seq and WGS data to separate genetic from cell non-autonomous mechanisms.

The paper identifies treatment-resistant Cancer Stem-Like Cells in breast cancer using single-cell RNA sequencing and validates a drug combination to target them effectively.