| 1 | 8-Sep | Class overview | | What is Network Biology | pptx pdf molbio_graphtheory_background probability_background | 1. Molecules of life 2. Topology of molecular networks | Profs. Roy/Gitter |
| 2 | 13-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 |
| 15-Sep | | Learning directed PGMs from data | Gene network inference with and without priors | pptx pdf | (1) Werhli et al (2) Cancer signaling and DBN | Prof. Roy |
| 3 | 20-Sep | | Learning directed PGMs from data and priors | | pptx pdf | | Prof. Roy |
| 22-Sep | | Learning dependency networks from data | Linear and tree models for GRNs | pptx pdf | (1) GENIE3 (2) Inferelator | Prof. Roy |
| 4 | 27-Sep | | Learning undirected models from data | GGMs and multi-task learning | pptx pdf | GNAT | Prof. Roy |
| 29-Sep | | Recent advances in graph learning | Single cell GRNs and causality | pptx pdf | (1) PIDC (2) NOTEARS | Prof. Roy |
| 5 | 4-Oct | Deep learning in network biology | Network embedding and representation learning | Tissue-specific gene function | pptx pdf | (1)Representation learning review(2)node2vec | Prof. Gitter |
| 6-Oct | | Graph convolutional networks | Predicting protein interfaces | pptx pdf | (1)Distillintros(2)Wu et al | Prof. Gitter |
| 6 | 11-Oct | | Graph transformers | Predicting chemical properties | pptx pdf | GraphGPS | Prof. Gitter |
| 13-Oct | | Graph transformers | | | Attention examples (pdf) (ipynb) | Prof. Gitter |
| 7 | 18-Oct | | Graph transformers | | | | Prof. Gitter |
| 20-Oct | | GANs and graph generative models | Drug discovery | pptx pdf | MolGAN | Prof. Gitter |
| 8 | 25-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 |
| 27-Oct | | Spectral and Louvain clustering | Modules on graphs | pptx pdf | (1) Louvain clustering (2) Module detection challenge | Prof. Roy |
| 9 | 1-Nov | | GNN methods for module finding | Clustering graphs with attributes | pptx pdf | (1)DNR (2) Senet | Prof. Roy |
| 3-Nov | | Dynamic modules | Examining topology changes | pptx pdf | PisCES | Prof. Roy |
| 10 | 8-Nov | Network-based data integration and interpretation | Graph kernels for node prioritization | Finding important genes of process/disease | pptx pdf | GeneWanderer | Prof. Gitter |
| 10-Nov | | Graph diffusion | Finding pathways in cancer | pptx pdf | HotNet | Prof. Gitter |
| 11 | 15-Nov | | Data integration using networks: Steiner forests | Integrating data from few samples | pptx pdf | PCSF | Prof. Gitter |
| 17-Nov | | Data integration using networks: SNF | Integrating data from many samples | pptx pdf | SNF | Prof. Gitter |
| 12 | 22-Nov | Graph alignment | Spectal and matrix factorization based alignment | Aligning protein-protein interaction networks | pptx pdf | (1) IsoRank (2) FUSE | Prof. Roy |
| 24-Nov | Thanksgiving | | | | | |
| 13 | 29-Nov | | Graph alignment of single cell datasets | Aligning and integrating single cell omic datasets | pptx pdf | (1) SCANORAMA (2) LIGER | Prof. Roy |
| 1-Dec | | Dana Pe’er seminar | | | | |
| 14 | 6-Dec | Projects | | | | | |
| 8-Dec | Projects | | | | | |
| 15 | 13-Dec | Projects | | | | | |