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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.


Average linkage hierarchical clustering example. The expression of 44 genes was measured in 4 experiments (E1, E2, E3, E4). The genes were classified according to their coexpression levels. The Pearson Correlation Coefficient was used (r-value) to analyze gene set signal values. Genes were clustered according to the r-value correlation matrix using the Average Linkage Hierarchical clustering method. The tree on the left clusters the expressions of the genes whereas the tree on top of the figure clusters the profiles of the experiments. Thus experiments E2 and E3 are similar and closely related.
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Figure 10: Average linkage hierarchical clustering example. The expression of 44 genes was measured in 4 experiments (E1, E2, E3, E4). The genes were classified according to their coexpression levels. The Pearson Correlation Coefficient was used (r-value) to analyze gene set signal values. Genes were clustered according to the r-value correlation matrix using the Average Linkage Hierarchical clustering method. The tree on the left clusters the expressions of the genes whereas the tree on top of the figure clusters the profiles of the experiments. Thus experiments E2 and E3 are similar and closely related.

Mentions: Hierarchical clustering is a method of cluster analysis which seeks to build a hierarchy of clusters. There are two different strategies to organize data. These are the agglomerative and the divisive: Agglomerative: It is a "bottom-up" approach. Each observation starts in its own cluster, and pairs of clusters are merged as one moves up the hierarchy. Divisive: This is a "top-down" approach. In this case, all of the observations start by forming one cluster, and then split recursively as one moves down the hierarchy. Some of the most common tree based clustering algorithms that organize data in hierarchies are the Unweighted Pair Group Method with Arithmetic Mean (UPGMA) [117,118], Neighbor Joining [112,119] and Hierarchical Clustering [120,121], all of which represent their clusters as tree structures. The results of hierarchical clustering are usually presented in a dendrogram. Figure 10 shows an example of how genes can be clustered.


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)

Average linkage hierarchical clustering example. The expression of 44 genes was measured in 4 experiments (E1, E2, E3, E4). The genes were classified according to their coexpression levels. The Pearson Correlation Coefficient was used (r-value) to analyze gene set signal values. Genes were clustered according to the r-value correlation matrix using the Average Linkage Hierarchical clustering method. The tree on the left clusters the expressions of the genes whereas the tree on top of the figure clusters the profiles of the experiments. Thus experiments E2 and E3 are similar and closely related.
© Copyright Policy - open-access
Related In: Results  -  Collection

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

Figure 10: Average linkage hierarchical clustering example. The expression of 44 genes was measured in 4 experiments (E1, E2, E3, E4). The genes were classified according to their coexpression levels. The Pearson Correlation Coefficient was used (r-value) to analyze gene set signal values. Genes were clustered according to the r-value correlation matrix using the Average Linkage Hierarchical clustering method. The tree on the left clusters the expressions of the genes whereas the tree on top of the figure clusters the profiles of the experiments. Thus experiments E2 and E3 are similar and closely related.
Mentions: Hierarchical clustering is a method of cluster analysis which seeks to build a hierarchy of clusters. There are two different strategies to organize data. These are the agglomerative and the divisive: Agglomerative: It is a "bottom-up" approach. Each observation starts in its own cluster, and pairs of clusters are merged as one moves up the hierarchy. Divisive: This is a "top-down" approach. In this case, all of the observations start by forming one cluster, and then split recursively as one moves down the hierarchy. Some of the most common tree based clustering algorithms that organize data in hierarchies are the Unweighted Pair Group Method with Arithmetic Mean (UPGMA) [117,118], Neighbor Joining [112,119] and Hierarchical Clustering [120,121], all of which represent their clusters as tree structures. The results of hierarchical clustering are usually presented in a dendrogram. Figure 10 shows an example of how genes can be clustered.

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.