Lecture materials are organized according to the following high level topics. Please see detailed syllabus here.

  1. Introduction to graph theory, probability theory, molecular networks
  2. Graph structure learning for network inference
  3. Integrative network inference
  4. Dynamics and context-specificity of networks
  5. Graph clustering to detect network modules
  6. Graph comparison and alignment
  7. Information flow on graphs for prioritization, integration and interpretation