Multipartite network-based models for precision medicine

Project: Research project

Project Details

Description (abstract)

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.
Effective start/end date01/09/202031/08/2024

Fields of Science

  • 1182 Biochemistry, cell and molecular biology
  • 111 Mathematics
  • 112 Statistics and probability
  • 3111 Biomedicine