| 1 | 3-Sep | Class overview | | What is Network Biology | pptx, pdf | 1. Molecules of life 2. Topology of molecular networks 3. Current and Future Directions in Network Biology | Profs. Roy/Gitter |
| 2 | 8-Sep | Learning graphs from data | Introductory concepts of molecular networks, graphs and PGMs | Representing gene regulatory networks | pptx, pdf | (1) Friedman et al (2) Sparse candidate (3) Markowetz and Spang (optional) | Prof. Roy |
| 10-Sep | | Bayesian networks | Learning GRNs from expression | pptx, pdf | | Prof. Roy |
| 3 | 15-Sep | | Bayesian networks contd/Dependency networks | | pptx, pdf | GENIE3 | Prof. Roy |
| 17-Sep | | Dependency networks/Dynamic Bayesian Networks | Cycles and predictive relationships, Dynamics | pptx, pdf | DBN and cancer signaling | Prof. Roy |
| 4 | 22-Sep | | Dynamic and Causal graph learning | Dynamics in networks, GRNs | pptx, pdf | (1) SCODE; (2) SCRIBE | Prof. Roy |
| 24-Sep | | Dynamic and Causal graph learning | Causal GRNs | pptx, pdf | Review article | Prof. Roy |
| 5 | 29-Sep | Graph structure properties and clustering | Intro Lin Algebra, Connected components, clustering | Topological properties of graphs | pptx, pdf | (1) Barabasi and Oltvai review (2) Girvan-Newman algorithm | Prof. Roy |
| 1-Oct | | Spectral and louvain clustering | Detecting modules on graphs | pptx, pdf, demo | (1) Module detection benchmark (2) Louvain clustering algorithm | Prof. Roy |
| 6 | 6-Oct | Shallow and deep representation learning on graphs | Node embedding, random walk, node2vec | Function prediction in multi-layer networks | pptx, pdf | (1) node2vec (2) representation learning | Prof. Gitter |
| 8-Oct | | Graph neural networks | Predicting protein interfaces | pptx, pdf | (1) Distill intros (2) Wu et al | Prof. Gitter |
| 7 | 13-Oct | | Graph neural networks | | pptx, pdf | Graph attention networks | Prof. Gitter |
| 15-Oct | | Experimental design and evaluation for graph ML | Protein-protein interaction prediction | pptx, pdf | (1) ML pitfalls (2) avoid data leakage | Guest lecture (Dr. Dsouza) |
| 8 | 20-Oct | | Graph transformers | Predicting chemical properties | pptx, pdf | (1) Transformer (2) GraphGPS | Prof. Gitter |
| 22-Oct | | Graph transformers | | Continued | Attention examples (pdf) (ipynb) | Prof. Gitter |
| 9 | 27-Oct | | Variational Graph AutoEncoders | Generalizing to new examples | | | Prof. Roy |
| 29-Oct | Graph comparison and alignment | Graph Alignment | Aligning protein-protein interaction networks | | | Prof. Roy |
| 10 | 3-Nov | | Graph Alignment | | | | Prof. Roy |
| 5-Nov | Network applications: prioritization, integration, interpretation | Graph kernels for node prioritization | Finding important genes of a process/disease | | | Prof. Gitter |
| 11 | 10-Nov | | Graph diffusion | Finding disease pathways associated with cancer | | | Prof. Gitter |
| 12-Nov | | Graph diffusion | More integration | | | Prof. Gitter |
| 12 | 17-Nov | | Data integration using networks: Steiner tree | Integrating data from small samples | | | Prof. Gitter |
| 19-Nov | | Data integration using networks: SNF | Integrating data from large samples | | | Prof. Gitter |
| 13 | 24-Nov | | Data integration using networks: BIONIC | Integrating complementary networks | | | Prof. Gitter |
| 26-Nov | Thanksgiving | | | | | |
| 14 | 1-Dec | Projects | | | | | |
| 3-Dec | Projects | | | | | |
| 15 | 8-Dec | Projects | | | | | |