Multipartite network-based models for precision medicine

From the drug discovery perspective, combination therapy is recommended in cancer due to efficiency and safety compared to the common cytotoxic and single-targeted monotherapies. However, identifying effective drug combinations is time and cost consuming. Here, I present a novel strategy of predicting potential drug combination and patient subclasses by constructing multipartite networks using drug response data. This project involves network pharmacology modeling, flow cytometry-based drug response, and thermal proteomics to provide the mechanism of action of drugs and drug combinations for a systems-level understanding of cancer.