Lecture materials are organized according to the following high level topics. Please see detailed syllabus here.
- Introduction to graph theory, probability theory, molecular networks
- Graph structure learning for network inference
- Integrative network inference
- Dynamics and context-specificity of networks
- Graph clustering to detect network modules
- Graph comparison and alignment
- Information flow on graphs for prioritization, integration and interpretation