Dynamic and Integrative Biological Network Research of Aging
summary
Combines static PPI (HPRD and binary) with age-resolved expression to build 37 age-specific PPI networks, computes seven centralities per gene and flags hundreds of genes whose network positions shift with age, then develops dynamic graphlets spanning ordered snapshots (up to multiple events) to capture temporal motifs. Dynamic methods outperform static and static-temporal baselines in recovering known aging genes, produce hundreds of novel predictions, and eight of the top ten novel candidates find support in recent literature.
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chapters (16)
- 0:00Intro
- 0:59Networks are everywhere!
- 2:06Knowledge about human aging
- 6:19Static vs. dynamic networks
- 8:27Our approach
- 15:40Do nodes' local topologies change with age?
- 16:08Our aging-related predictions by dynamic network analysis
- 18:59Validations of aging-related predictions
- 19:44Functional and disease enrichment
- 20:30Manual literature validation of novel predictions
- 22:45More recent work: dynamic graphlets
- 26:29Accuracy of different methods on known aging-related knowledge
- 27:36Validation of different methods novel aging-related knowledge
- 28:18Dynamic graphlets: bottom line
- 29:09Global Network Alignment in the context of Aging
- 29:31Acknowledgements Complex Networks (CoNe) Lab (www.nd.edu/cone)
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