| 1 |
7-Sep |
Class overview |
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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 |
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14-Sep |
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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 |
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Learning directed PGMs from data |
Modeling prior knowledge for GRNs |
pptx pdf |
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Prof. Roy |
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21-Sep |
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Learning dependency networks from data |
Linear and tree models for GRNs |
pptx pdf |
GENIE3 |
Prof. Roy |
| 4 |
26-Sep |
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Learning undirected models from data |
GGMs and multi-task learning |
pptx pdf |
GNAT |
Prof. Roy |
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28-Sep |
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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 |
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5-Oct |
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Graph neural network extensions |
Predicting protein function |
pptx pdf |
Graph attention networks |
Prof. Gitter |
| 6 |
10-Oct |
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Graph transformers |
Predicting chemical properties |
pptx pdf |
GraphGPS |
Prof. Gitter |
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12-Oct |
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Graph transformers |
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see 10-Oct |
Attention examples (pdf) (ipynb) |
Prof. Gitter |
| 7 |
17-Oct |
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Equivariant graph neural networks |
Predicting 3D chemical properties |
pptx pdf |
E(n) Equivariant GNN |
Prof. Gitter |
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19-Oct |
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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 |
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26-Oct |
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Spectral and Louvain clustering |
Modules on graphs |
pptx pdf |
(1) Louvain clustering (2) Module detection challenge |
Prof. Roy |
| 9 |
31-Oct |
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Representation learning |
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pptx pdf |
(1)Representation learning review(2)node2vec (3) OhmNet |
Prof. Roy |
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2-Nov |
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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 |
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9-Nov |
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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 |
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16-Nov |
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Graph diffusion |
Finding pathways in cancer |
pptx pdf |
HotNet |
Prof. Gitter |
| 12 |
21-Nov |
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Graph diffusion |
Finding pathways in cancer |
see 16-Nov |
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Prof. Gitter |
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23-Nov |
Thanksgiving |
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| 13 |
28-Nov |
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Data integration using networks: Steiner forests |
Integrating data from few samples |
pptx pdf |
PCSF |
Prof. Gitter |
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30-Nov |
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Data integration using networks: SNF |
Integrating data from many samples |
pptx pdf |
SNF |
Prof. Gitter |
| 14 |
5-Dec |
Projects |
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7-Dec |
Projects |
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| 15 |
12-Dec |
Projects |
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