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

  1. Introduction to graph theory, probability theory, molecular networks
  2. Graph structure learning for network inference
  3. Dynamics and context-specificity of networks
  4. Deep learning in network biology
  5. Topological properties of networks
  6. Graph clustering, comparison and alignment
  7. Network-based data integration, prioritization and interpretation