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Dynamic Networks from Hierarchical Bayesian Graph Clustering
Open accessAbstract
Biological networks change dynamically as protein components are synthesized and degraded. Understanding the time-dependence and, in a multicellular organism, tissue-dependence of a network leads to insight beyond a view that collapses time-varying interactions into a single static map. Conventional algorithms are limited to analyzing evolving networks by reducing them to a series of unrelated snapshots.Here we introduce an approach that groups proteins according to shared interaction patterns through a dynamical hierarchical stochastic block model. Protein membership in a block is permitted to evolve as interaction patterns shift over time and space, representing the spatial organization of cell types in a multicellular organism. The spatiotemporal evolution of the protein components are inferred from transcript profiles, using Arabidopsis root development (5 tissues, 3 temporal stages) as an example.The new model requires essentially no parameter tuning, out-performs existing snapshot-based methods, identifies protein modules recruited to specific cell types and developmental stages, and could have broad application to social networks and other similar dynamic systems.
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Although the Bayesian modeling approach is not new to network analysis in general, it remains relatively rare.
“Although the Bayesian modeling approach is not new to network analysis in general [25], [26], it remains relatively rare.”
From Spatial Guilds in the Serengeti Food Web Revealed by a Bayesian Group Model (2011) · cites this paper as [26]
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Metadata
- Authors
- Park, Y., Moore, C., & Bader, J. S.
- Affiliations
- Johns Hopkins University (US)
- Santa Fe Institute (US)
- Corresponding author
- Bader, Joel S.
- Venue
- PLoS ONE
- Publisher
- Public Library of Science (PLoS)
- Published
- 2010-01-11
- Volume
- 5
- Issue
- 1
- Pages
- e8118
- ISSN
- 1932-6203 (ISSN-L)
- PMC
- PMC2799515
- OpenAlex
- W2084529304
- Cited by
- 34
- References
- 28
- Citations by year
- 2025: 1 · 2024: 1 · 2023: 1 · 2021: 1 · 2020: 1 · 2018: 1 · 2017: 2 · 2016: 4 · 2015: 3 · 2014: 4 · 2013: 4 · 2012: 6
- FWCI
- 1.96
- Type
- journal article
- Language
- en
- Open access
- gold
- doi.org
- Locations
- PLoS ONE (publishedVersion, cc-by)
- Europe PMC (PubMed Central) (publishedVersion, cc-by)
- PubMed Central (publishedVersion, cc-by)
- PubMed (publishedVersion)
- RePEc: Research Papers in Economics (submittedVersion)
- DOAJ (DOAJ: Directory of Open Access Journals) (submittedVersion, cc-by-sa)
- Funders
- National Science Foundation
- Santa Fe Institute
- Topics
- Life Sciences › Biochemistry, Genetics and Molecular Biology › Molecular Biology › Bioinformatics and Genomic Networks
- Life Sciences › Biochemistry, Genetics and Molecular Biology › Molecular Biology › Gene Regulatory Network Analysis
- Physical Sciences › Physics and Astronomy › Statistical and Nonlinear Physics › Complex Network Analysis Techniques
- Keywords
- dynamic networks, protein-protein interaction network, protein modules, spatiotemporal evolution, transcript profiles, tissue-specific gene expression, protein recruitment, social networks
- MeSH terms
- Algorithms, Bayes Theorem, Cluster Analysis, TATA-Box Binding Protein
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