| 1 |
5-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 |
10-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 |
|
12-Sep |
|
Learning directed PGMs from data |
|
Continue from Sep 10th |
|
Prof. Roy |
| 3 |
17-Sep |
|
Extensions to directed PGMs |
Modeling time and prior knowledge |
pdf pptx |
DBN and Cancer signaling |
Prof. Roy |
|
19-Sep |
|
Dependency networks |
Predictive relationships and cycles |
pdf pptx |
GENIE3 |
Prof. Roy |
| 4 |
24-Sep |
|
Continue with dependency networks |
|
|
|
Prof. Roy |
|
26-Sep |
|
Causal graph learning |
Causal GRNs |
pdf pptx |
Review article |
Prof. Roy |
| 5 |
1-Oct |
Deep learning in network biology |
Graph neural networks |
Predicting protein interfaces |
pptx pdf |
(1)Distill intros (2)Wu et al |
Prof. Gitter |
|
3-Oct |
|
Graph neural networks |
Predicting protein function |
pptx pdf |
Graph attention networks |
Prof. Gitter |
| 6 |
8-Oct |
|
Graph transformers |
Predicting chemical properties |
pptx pdf |
GraphGPS |
Prof. Gitter |
|
10-Oct |
|
Graph transformers |
|
Continued |
Attention examples (pdf) (ipynb) |
Prof. Gitter |
| 7 |
15-Oct |
|
Graph transformers |
|
Continued |
Transformer |
Prof. Gitter |
|
17-Oct |
Analysis of graphs |
Degree distribution; clusters/modules |
Design principles of biological networks |
pdf pptx |
(1) Barabasi and Oltvai review (2) Girvan-Newman algorithm (notebook) |
Spencer Halberg & Erika Lee |
| 8 |
22-Oct |
|
Graph clustering and motifs; evaluation |
|
pdf pptx |
Module detection benchmark |
Prof. Roy |
|
24-Oct |
|
Unsupervised Representation learning |
Task agnostic graph analysis |
pdf pptx |
(1)node2vec (2) OhmNet |
Prof. Roy |
| 9 |
29-Oct |
|
Continue unsupervised representation learning (RL) |
|
see oct24 slides |
|
Prof. Roy |
|
31-Oct |
Graph comparison and alignment |
Wrap up deep RL for features;MF for alignment |
Aligning protein-protein interactions |
(1) RLpdf pptx; (2)Alignment: pdf pptx |
(1) Variational GraphAutoEncoder; (2) FUSE |
Prof. Roy |
| 10 |
5-Nov |
|
Alignment continued |
Aligning single cell omic datasets |
pdf pptx |
(1) MNN correct; (2) LIGER |
Prof. Roy |
|
7-Nov |
Network-based integration and interpretation |
Graph kernels for node prioritization |
Finding important genes of a process/disease |
pptx pdf |
GeneWanderer |
Prof. Gitter |
| 11 |
12-Nov |
|
Graph diffusion |
Finding patient/disease pathways associated with cancer |
pptx pdf |
HotNet |
Prof. Gitter |
|
14-Nov |
|
Graph diffusion |
Finding patient/disease pathways associated with cancer |
See 12-Nov |
|
Prof. Gitter |
| 12 |
19-Nov |
|
Data integration with few samples |
Integrating data from small samples |
pptx pdf |
PCSF |
Prof. Gitter |
|
21-Nov |
|
Data integration with many samples |
Integrating data from large samples |
pptx pdf |
SNF |
Prof. Gitter |
| 13 |
26-Nov |
|
Integrating complementary networks |
Protein function prediction and module learning |
pptx pdf |
BIONIC |
Prof. Gitter |
|
28-Nov |
Thanksgiving |
|
|
|
|
|
| 14 |
3-Dec |
Projects |
|
|
|
|
|
|
5-Dec |
Projects |
|
|
|
|
|
| 15 |
10-Dec |
Projects |
|
|
|
|
|