Diffusion-Based Metrics for Biological Network Analysis

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Defines diffusion state distance as the L1 norm of difference between expected visit-count vectors from K-step random walks (converging as K→∞ and computable via eigen/inverse methods), which downweights paths through high-degree hubs. Code runs on BioGRID and STRING; complexity is dominated by matrix inversion. Replacing shortest-path similarity with DSD raises yeast function-prediction accuracy from ~50% (majority vote) to ~60%–71% when using K≈10, edge confidences and curated pathway boosts, and yields larger gains on sparse networks. Public code and PLoS ONE paper available.

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" chapters (17)

1. 0:00Introduction
2. 1:57Social Networks
3. 5:14Distance Metric
4. 5:53Diffusion Distance Metric
5. 14:24Classical Function Label Prediction
6. 15:11Classical Function Prediction Methods
7. 15:47References
8. 16:11Experimental Results
9. 17:52Yeast Interaction
10. 18:22Majority Vote
11. 19:49Summary
12. 28:14Previous Results
13. 28:28Confidence
14. 28:53Highway Trick
15. 29:15Results
16. 30:28Collaborators
17. 31:00Hit Time