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Using graph theory to analyze biological networks

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Autor
Pavlopoulos, G. A.; Secrier, M.; Moschopoulos, C. N.; Soldatos, T. G.; Kossida, S.; Aerts, J.; Schneider, R.; Bagos, P. G.
Datum
2011
DOI
10.1186/1756-0381-4-10
Schlagwort
biological network clustering analysis
graph theory
node ranking
PROTEIN-INTERACTION NETWORKS
EVOLUTIONARY GENETICS ANALYSIS
TRANSCRIPTIONAL REGULATORY NETWORKS
ESCHERICHIA-COLI K-12
SMALL-WORLD
NETWORKS
METABOLIC NETWORKS
SACCHAROMYCES-CEREVISIAE
PATHWAY
ANALYSIS
MARKUP LANGUAGE
MICROARRAY DATA
Mathematical & Computational Biology
Zur Langanzeige
Zusammenfassung
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.
URI
http://hdl.handle.net/11615/32113
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  • Δημοσιεύσεις σε περιοδικά, συνέδρια, κεφάλαια βιβλίων κλπ. [19735]

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