Matthew H. Ung

The paper develops a proteogenomic framework to identify dispensable genes for safer gene editing in hematopoietic therapies, identifying 237 candidates including LY75.

The paper constructs a single-cell DNA sequencing atlas and introduces GUMM, a computational workflow, to accurately infer gene editing allelism despite technical artifacts.

scGenAI improves single-cell state classification by integrating multimodal biological features and LLMs, outperforming traditional methods in cell type prediction and AML malignant cell state identification.

The paper develops a custom ENCODE annotation for cancer genomics, highlighting TF-RBP interactions and network rewiring during oncogenesis, and validates findings using experimental assays.