Peng Wang

Modeling cancer dependency with deep graph models

The paper introduces a deep learning framework called DepGPS to model tumor vulnerabilities using graph neural networks and transformers, showing improved prediction of context-specific vulnerabilities and utility in identifying synthetic lethal genes.

Identification of spatially variable genes with graph cuts

This paper introduces scGCO, a method for identifying spatially variable genes in single-cell data, showing superior performance and scalability.