Introduction to Biological Network Analysis III: Identifying Network Modules
summary
Covers concrete algorithms and results for finding modules in protein networks: procedural heuristics like ENCODE, probabilistic scoring via NetworkBLAST (likelihood-ratio against degree-preserving random graphs), global methods like MCL and Newman modularity, and path-finding with color-coding (fixed-parameter runtimes that make K≈10 practical). Compares methods on yeast data (NetworkBLAST/cFINDER win on mixed PPI, MCL on Y2H), integrates genetic and physical edges to distinguish between‑pathway vs within‑pathway explanations and achieves about 87% accuracy predicting genetic interactions.
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chapters (18)
- 0:00Intro
- 1:17Gene/Protein Modules
- 3:26Module finding vs. clustering
- 8:00Outline
- 12:09MCODE
- 14:02Details & limitations
- 17:07NetworkBLAST
- 27:11Markov Clustering (MCL)
- 29:42Modularity-based clustering
- 35:21Division into two groups (2)
- 36:30Division into two groups (3)
- 39:32Performance evaluation
- 41:29Performance comparison
- 43:45Going back to the sources
- 45:55Finding Simple Paths
- 47:58Color Coding AYZ 95
- 51:07Color Coding AYZ'95
- 53:24Integration: main idea
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