Carolina H. Chung

This paper develops a framework combining machine learning and metabolic modeling to predict synergistic cancer therapies influenced by microbiome composition in colorectal cancer.

The paper develops a transfer learning framework to predict drug interactions for under-studied pathogens and validates synergistic combinations against antibiotic-resistant bacteria.

This study shows licochalcone A suppresses breast cancer growth by targeting SREBP1-dependent lipogenesis and inflammation.

The paper develops a flux-based machine learning model to predict drug interactions in E. coli, accounting for metabolic heterogeneity and growth conditions, identifying 24 synergistic combinations.