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Using graph theory to analyze biological networks.

Pavlopoulos GA, Secrier M, Moschopoulos CN, Soldatos TG, Kossida S, Aerts J, Schneider R, Bagos PG - BioData Min (2011)

Bottom Line: 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.

View Article: PubMed Central - HTML - PubMed

Affiliation: Department of Computer Science and Biomedical Informatics, University of Central Greece, Lamia, 35100, Greece. pavlopou@embl.de.

ABSTRACT
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.

No MeSH data available.


Matching Index. V1 is connected with 5 nodes (V3, V4, V6, V7,V8). V2 is connected with 4 nodes (V3, V4, V5, V8). V3 is connected with 2 nodes (V1, V2). V4 is connected with 3 nodes (V1, V2). V5 is connected with 1 node (V2). V6 is connected with 1 node (V1). V7 is connected with 1 node (V1). V8 is connected with 2 nodes (V1, V5). Node V1 and V2 are connected with 3 common nodes (V3, V4, V8)and in total with 6 distinct neighbors (V3, V4, V8, V5, V6 , V7). The matching index will then be M1,2 = 3/6 = 0.5, thus V1 and V2 are functionally similar even though they are not connected.
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Figure 9: Matching Index. V1 is connected with 5 nodes (V3, V4, V6, V7,V8). V2 is connected with 4 nodes (V3, V4, V5, V8). V3 is connected with 2 nodes (V1, V2). V4 is connected with 3 nodes (V1, V2). V5 is connected with 1 node (V2). V6 is connected with 1 node (V1). V7 is connected with 1 node (V1). V8 is connected with 2 nodes (V1, V5). Node V1 and V2 are connected with 3 common nodes (V3, V4, V8)and in total with 6 distinct neighbors (V3, V4, V8, V5, V6 , V7). The matching index will then be M1,2 = 3/6 = 0.5, thus V1 and V2 are functionally similar even though they are not connected.

Mentions: or . An example is shown in Figure 9. The matching index is often used to cluster different components of a biological network according to some property. For instance, it has been used to describe spatial growth in brain networks during development [91] or to predict the connectivity of primate cortical networks [92].


Using graph theory to analyze biological networks.

Pavlopoulos GA, Secrier M, Moschopoulos CN, Soldatos TG, Kossida S, Aerts J, Schneider R, Bagos PG - BioData Min (2011)

Matching Index. V1 is connected with 5 nodes (V3, V4, V6, V7,V8). V2 is connected with 4 nodes (V3, V4, V5, V8). V3 is connected with 2 nodes (V1, V2). V4 is connected with 3 nodes (V1, V2). V5 is connected with 1 node (V2). V6 is connected with 1 node (V1). V7 is connected with 1 node (V1). V8 is connected with 2 nodes (V1, V5). Node V1 and V2 are connected with 3 common nodes (V3, V4, V8)and in total with 6 distinct neighbors (V3, V4, V8, V5, V6 , V7). The matching index will then be M1,2 = 3/6 = 0.5, thus V1 and V2 are functionally similar even though they are not connected.
© Copyright Policy - open-access
Related In: Results  -  Collection

License
Show All Figures
getmorefigures.php?uid=PMC3101653&req=5

Figure 9: Matching Index. V1 is connected with 5 nodes (V3, V4, V6, V7,V8). V2 is connected with 4 nodes (V3, V4, V5, V8). V3 is connected with 2 nodes (V1, V2). V4 is connected with 3 nodes (V1, V2). V5 is connected with 1 node (V2). V6 is connected with 1 node (V1). V7 is connected with 1 node (V1). V8 is connected with 2 nodes (V1, V5). Node V1 and V2 are connected with 3 common nodes (V3, V4, V8)and in total with 6 distinct neighbors (V3, V4, V8, V5, V6 , V7). The matching index will then be M1,2 = 3/6 = 0.5, thus V1 and V2 are functionally similar even though they are not connected.
Mentions: or . An example is shown in Figure 9. The matching index is often used to cluster different components of a biological network according to some property. For instance, it has been used to describe spatial growth in brain networks during development [91] or to predict the connectivity of primate cortical networks [92].

Bottom Line: 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.

View Article: PubMed Central - HTML - PubMed

Affiliation: Department of Computer Science and Biomedical Informatics, University of Central Greece, Lamia, 35100, Greece. pavlopou@embl.de.

ABSTRACT
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.

No MeSH data available.