| 1 | 7-Sep | Class overview | | What is Network Biology | pptx pdf | 1. Molecules of life 2. Topology of molecular networks | Profs. Roy/Gitter |
| 2 | 12-Sep | Representing and learning networks from data | Introductory concepts of graphs and PGMs | Representing gene regulatory networks | pptx pdf | (1) Friedman et al (2) Sparse candidate (3) Markowetz and Spang (optional) | Prof. Roy |
| 14-Sep | | Learning directed PGMs from data | Modeling time and prior knowledge for Gene regulatory networks | pptx pdf | (1) Cancer signaling and DBN (2) Werhli et al (optional) | Prof. Roy |
| 3 | 19-Sep | | Learning directed PGMs from data | Modeling prior knowledge for GRNs | pptx pdf | | Prof. Roy |
| 21-Sep | | Learning dependency networks from data | Linear and tree models for GRNs | pptx pdf | GENIE3 | Prof. Roy |
| 4 | 26-Sep | | Learning undirected models from data | GGMs and multi-task learning | pptx pdf | GNAT | Prof. Roy |
| 28-Sep | | Causal graph learning | Causality in GRNs | pptx pdf | Review Article | Prof. Roy |
| 5 | 3-Oct | Supervised deep learning in network biology | Graph neural networks | Predicting protein interfaces | pptx pdf | (1)Distill intros (2)Wu et al | Prof. Gitter |
| 5-Oct | | Graph neural network extensions | Predicting protein function | pptx pdf | Graph attention networks | Prof. Gitter |
| 6 | 10-Oct | | Graph transformers | Predicting chemical properties | pptx pdf | GraphGPS | Prof. Gitter |
| 12-Oct | | Graph transformers | | see 10-Oct | Attention examples (pdf) (ipynb) | Prof. Gitter |
| 7 | 17-Oct | | Equivariant graph neural networks | Predicting 3D chemical properties | pptx pdf | E(n) Equivariant GNN | Prof. Gitter |
| 19-Oct | | GANs and graph generative models | Drug discovery | pptx pdf | MolGAN | Prof. Gitter |
| 8 | 24-Oct | Graph topology and modules | Degree distributions and modules | Organizational properties of networks | pptx pdf | (1) Barabasi and Oltvai review (2) Girvan-Newman algorithm | Prof. Roy |
| 26-Oct | | Spectral and Louvain clustering | Modules on graphs | pptx pdf | (1) Louvain clustering (2) Module detection challenge | Prof. Roy |
| 9 | 31-Oct | | Representation learning | | pptx pdf | (1)Representation learning review(2)node2vec (3) OhmNet | Prof. Roy |
| 2-Nov | | | Representing graphs with attributes | pptx pdf | (1)Variational GraphAutoEncoder (2)Deep Graph Infomax | Prof. Roy |
| 10 | 7-Nov | Graph alignment | Spectral and matrix factorization based alignment | Aligning protein-protein interaction networks | pptx pdf | 1) IsoRank (2) FUSE | Prof. Roy |
| 9-Nov | | Graph alignment of single cell datasets | Aligning and integrating single cell omic datasets | pptx pdf | (1) SCANORAMA (2) LIGER | Prof. Roy |
| 11 | 14-Nov | Network-based data integration and interpretation | Graph kernels for node prioritization | Finding important genes of process/disease | pptx pdf | GeneWanderer | Prof. Gitter |
| 16-Nov | | Graph diffusion | Finding pathways in cancer | pptx pdf | HotNet | Prof. Gitter |
| 12 | 21-Nov | | Graph diffusion | Finding pathways in cancer | see 16-Nov | | Prof. Gitter |
| 23-Nov | Thanksgiving | | | | | |
| 13 | 28-Nov | | Data integration using networks: Steiner forests | Integrating data from few samples | pptx pdf | PCSF | Prof. Gitter |
| 30-Nov | | Data integration using networks: SNF | Integrating data from many samples | pptx pdf | SNF | Prof. Gitter |
| 14 | 5-Dec | Projects | | | | | |
| 7-Dec | Projects | | | | | |
| 15 | 12-Dec | Projects | | | | | |