| 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 |
|
|
|
|
|