Hegang Chen

scE2TM improves single-cell embedding interpretability by integrating external knowledge, achieving better clustering and revealing biological perturbation signatures across datasets.

scConcept improves single-cell transcriptomic analysis by creating interpretable biological concepts from gene-level data, enhancing clustering and interpretability across multiple datasets.

InSTaPath integrates spatial transcriptomics and histopathology images using multimodal topic learning to improve spatial domain identification and uncover gene-morphology relationships.

This paper introduces FSM, a method to predict effective drug combinations by analyzing drug 'friendship' features, showing improved performance and lower toxicity.