Schedule
| Week | Date | Major Topic | Computational Concepts | Biological Application | Slides | Reading | Lecturer |
|---|---|---|---|---|---|---|---|
| 1 | 4-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 | 9-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 |
| 11-Sep | Learning directed PGMs from data | Learning GRNs from expression | pptx, pdf (slightly updated) | Prof. Roy | |||
| 3 | 16-Sep | Dependency networks | Cycles and predictive relationships | pptx, pdf | GENIE3 | Prof. Roy | |
| 18-Sep | Dynamic and Causal graph learning | Dynamics in networks | pptx, pdf | DBN and cancer signaling | Prof. Roy | ||
| 4 | 23-Sep | Dynamic and Causal graph learning | Continue with DBNs and scRNA-seq | pptx, pdf | (1) SCODE; (2) SCRIBE | Prof. Roy | |
| 25-Sep | Causal graph learning | pptx, pdf | Review article | Prof. Roy | |||
| 5 | 30-Sep | Graph neural networks | Graph neural networks | Predicting protein interfaces | pptx, pdf | (1) Distill intros (2) Wu et al | Prof. Gitter |
| 2-Oct | Graph neural network extensions | Predicting protein function | pptx, pdf | Graph attention networks | Prof. Gitter | ||
| 6 | 7-Oct | Graph transformers | Predicting chemical properties | pptx, pdf | (1) Transformer (2) GraphGPS | Prof. Gitter | |
| 9-Oct | Graph transformers | Continued | Attention examples (pdf) (ipynb) | Prof. Gitter | |||
| 7 | 14-Oct | Graph neural network additional topics | Predicting protein mutation effects | pptx, pdf | (1) E(n) Equivariant GNN (2) MolGAN | Prof. Gitter | |
| 16-Oct | Analysis of graphs | Degree distribution; clusters/modules | Design principles of biological networks | pptx, pdf | (1) Barabasi and Oltvai review (2) Girvan-Newman algorithm | Prof. Roy | |
| 8 | 21-Oct | Graph clustering | Detecting modules on graphs | pptx, pdf | Module detection benchmark | Prof. Roy | |
| 23-Oct | Unsupervised Representation learning | Task agnostic graph analysis | pptx, pdf | (1) node2vec (2) OhmNet | Prof. Roy | ||
| 9 | 28-Oct | Continue unsupervised representation learning (RL) | Generalizing to new examples | Continue from last time; pptx, pdf | Variational GraphAutoEncoder | Prof. Roy | |
| 30-Oct | Graph comparison and alignment | Continue with unsupervised RL | Incorporating features | pptx, pdf | Deep Graph Infomax | Prof. Roy | |
| 10 | 4-Nov | Graph Alignment | pptx, pdf | FUSE | Prof. Roy | ||
| 6-Nov | Network-based integration and interpretation | Graph kernels for node prioritization | Prioritizing genetic variants | pptx, pdf | GeneWanderer | Prof. Gitter | |
| 11 | 11-Nov | Graph diffusion | Finding disease pathways associated with cancer | pptx, pdf | HotNet | Prof. Gitter | |
| 13-Nov | Graph diffusion | Finding disease pathways associated with cancer | Continued | Prof. Gitter | |||
| 12 | 18-Nov | Data integration using networks: Steiner tree | Finding pathways involved in viral infection | pptx, pdf | PCSF | Prof. Gitter | |
| 20-Nov | Demets lecture | AI Methods for Biomedicine | www | Prof. Gerstein | |||
| 13 | 25-Nov | Data integration using networks: SNF | Finding cancer subtypes | pptx, pdf | SNF | Prof. Gitter | |
| 27-Nov | Thanksgiving | ||||||
| 14 | 2-Dec | Projects | |||||
| 4-Dec | Projects | ||||||
| 15 | 9-Dec | Projects |