Lecture materials are organized according to the following high level topics. Please see detailed tentative syllabus here.
- Introduction to graph theory, probability theory, molecular networks
- Graph structure learning for network inference
- Dynamics and context-specificity of networks
- Deep learning in network biology
- Topological properties of networks
- Graph clustering, comparison and alignment
- Network-based data integration, prioritization and interpretation