Reference : Using graph theory to analyze biological networks
Scientific journals : Article
Life sciences : Biochemistry, biophysics & molecular biology
Using graph theory to analyze biological networks
Pavlopoulos, Georgios A. [> >]
Secrier, Maria [> >]
Moschopoulos, Charalampos N. [> >]
Soldatos, Theodoros G. [> >]
Kossida, Sophia [> >]
Aerts, Jan [> >]
Schneider, Reinhard mailto [University of Luxembourg > Luxembourg Centre for Systems Biomedicine (LCSB) > >]
Bagos, Pantelis G. [> >]
BioData Mining
BioMed Central Ltd.
Yes (verified by ORBilu)
[en] biological network clustering analysis ; graph theory ; node ranking
[en] 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.
Luxembourg Centre for Systems Biomedicine (LCSB): Bioinformatics Core (R. Schneider Group)

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