Network Propagation as a Tool for Deciphering Disease Mechanisms

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" summary

Per-sample binary mutation and differential-expression calls are propagated across a 150,000‑edge HIPPI network (random walk with restart) to create propagated matrices; logistic regression on features like mean, variance and the minimum propagation score boosts AUC by ~0.2 for breast cancer and glioblastoma when recovering pathway genes and yields survival‑significant top hits. A second pipeline computes empirical p‑values against random priors, then applies ILP optimal clustering to find disease modules; on ~100 diseases (≈8,000 associations) the method produces ≈50% enriched clusters versus 36% (PRINCE) and 30% (HotNet2); a Cytoscape app Propagate is provided.

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" chapters (20)

1. 0:00Network propagation as a tool for deciphering disease mechanisms
2. 0:40Guilt by association
3. 2:29The propagation score function
4. 2:59Propagation in network biology
5. 3:27Outline
6. 3:57Motivation
7. 5:54The effect of propagation (BRCA)
8. 9:13The computational workflow
9. 10:09Performance evaluation
10. 10:49Mutations vs. expression
11. 15:50Summary (part I)
12. 23:40Associating diseases with complexes
13. 24:43The general workflow
14. 26:14Statistical scoring
15. 27:32Finding dense clusters
16. 29:00Data sets
17. 30:35external validation
18. 31:59using information from similar diseases
19. 33:12Case analysis - epilepsy syndrome
20. 34:01Summary (part II)

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