The DOI matches its Crossref record; 17 of the 17 references it cites were found in a registry or an index.
Using graph theory to analyze biological networks
Open accessAbstract
Understanding complex systems often requires a bottom-up analysis towards a systems biology approach. The need to investigate a system, not only as individual components but as a whole, emerges. This can be done by examining the elementary constituents individually and then how these are connected. The myriad components of a system and their interactions are best characterized as networks and they are mainly represented as graphs where thousands of nodes are connected with thousands of vertices. In this article we demonstrate approaches, models and methods from the graph theory universe and we discuss ways in which they can be used to reveal hidden properties and features of a network. This network profiling combined with knowledge extraction will help us to better understand the biological significance of the system.
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Doncheva, N. T., Assenov, Y., Domingues, F. S., & Albrecht, M. (2012) · Nature Protocols · doi:10.1038/nprot.2012.004 · Cited by 503
Learning from Co-expression Networks: Possibilities and Challenges
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Large-scale network analysis reveals the sequence space architecture of antibody repertoires
Miho, E., Roškar, R., Greiff, V., & Reddy, S. T. (2019) · Nature Communications · doi:10.1038/s41467-019-09278-8 · Cited by 138
Overcoming Catastrophic Forgetting in Graph Neural Networks
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Metadata
- Authors
- Pavlopoulos, G. A., Secrier, M., Moschopoulos, C. N., Soldatos, T. G., Kossida, S., Aerts, J., Schneider, R., & Bagos, P. G.
- Venue
- BioData Mining
- Published
- 2011-04-28
- Volume
- 4
- Issue
- 1
- Pages
- 10-10
- ISSN
- 1756-0381 (ISSN-L)
- OpenAlex
- W2106474670
- Cited by
- 791
- References
- 17
- FWCI
- 7.99
- Type
- journal article
- Language
- en
- Open access
- gold
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- Pavlopoulos, G. A., Secrier, M., Moschopoulos, C. N., Soldatos, T. G., Kossida, S., Aerts, J., Schneider, R., & Bagos, P. G. (2011). Using graph theory to analyze biological networks. BioData Mining. https://doi.org/10.1186/1756-0381-4-10
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