Min Jin Ha

This study identifies ROCK inhibition as a strategy to overcome metabolic adaptation in cancer cells to OXPHOS inhibition, showing synergistic anti-tumor effects.

This paper introduces a Bayesian framework to improve drug response prediction in cancer by integrating pharmacogenomic data across patients and cell lines.

CombPDX evaluates drug synergism in PDX models using tumor growth curves and provides statistical metrics and inference procedures for combination effects.

The paper develops TransPRECISE, a Bayesian network framework, to analyze pan-cancer patient and cell line interactomes, identifying pathway activities and drug sensitivities across multiple cancer types.

The paper develops a network-based method to predict drug sensitivity for cancer patients using cell line and omics data, identifying effective drug-patient matches.