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Identifying common components across biological network graphs using a bipartite data model.

Baker E, Culpepper C, Philips C, Bubier J, Langston M, Chesler E - BMC Proc (2014)

Bottom Line: In addition, we illustrate how results from maximal bipartite discovery can be decomposed into hierarchical relationships, allowing shared pathway components to be mapped through various parent-child relationships to help visualization and discovery of emergent kernel driven relationships.Interrogating common relationships among biological networks and conventional GeneWeaver gene lists will increase functional specificity and reliability of the shared biological components.This approach enables self-organization of biological processes through shared biological networks.

View Article: PubMed Central - HTML - PubMed

Affiliation: Department of Computer Science, Baylor University, Waco, TX, USA.

ABSTRACT
The GeneWeaver bipartite data model provides an efficient means to evaluate shared molecular components from sets derived across diverse species, disease states and biological processes. In order to adapt this model for examining related molecular components and biological networks, such as pathway or gene network data, we have developed a means to leverage the bipartite data structure to extract and analyze shared edges. Using the Pathway Commons database we demonstrate the ability to rapidly identify shared connected components among a diverse set of pathways. In addition, we illustrate how results from maximal bipartite discovery can be decomposed into hierarchical relationships, allowing shared pathway components to be mapped through various parent-child relationships to help visualization and discovery of emergent kernel driven relationships. Interrogating common relationships among biological networks and conventional GeneWeaver gene lists will increase functional specificity and reliability of the shared biological components. This approach enables self-organization of biological processes through shared biological networks.

No MeSH data available.


Shared sub-graphs at the intersection of distinct Pathway Commons resources. Maximal biclique analysis can rapidly isolate sub-pathways that are common between differing data sources, such as the BMP signaling pathway in this example. The Reactome BMP signaling pathway is shown along with the sub-graph in common with the HumanCyc
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Figure 5: Shared sub-graphs at the intersection of distinct Pathway Commons resources. Maximal biclique analysis can rapidly isolate sub-pathways that are common between differing data sources, such as the BMP signaling pathway in this example. The Reactome BMP signaling pathway is shown along with the sub-graph in common with the HumanCyc

Mentions: In other cases, the maximal biclique can occur in edges between pathways from different pathway repositories. [Figure 5] demonstrates one such common sub-pathway shared by Reactome and the HumanCyc data set. The sub-pathway is specific to BMP signaling and aligns both pathways based on the maximal biclique shared between them. [Figure 6] illustrates a similar example between the HumanCyc and curated NCI PID data sets. Here, the maximal intersection between the HumanCyc prostanoid biosynthesis pathway and the PID prostaglandin biosynthesis pathway represents the complete PID prostaglandin biosynthesis pathway. The HumanCyc pathway contains two addition gene components.


Identifying common components across biological network graphs using a bipartite data model.

Baker E, Culpepper C, Philips C, Bubier J, Langston M, Chesler E - BMC Proc (2014)

Shared sub-graphs at the intersection of distinct Pathway Commons resources. Maximal biclique analysis can rapidly isolate sub-pathways that are common between differing data sources, such as the BMP signaling pathway in this example. The Reactome BMP signaling pathway is shown along with the sub-graph in common with the HumanCyc
© Copyright Policy - open-access
Related In: Results  -  Collection

License 1 - License 2
Show All Figures
getmorefigures.php?uid=PMC4202189&req=5

Figure 5: Shared sub-graphs at the intersection of distinct Pathway Commons resources. Maximal biclique analysis can rapidly isolate sub-pathways that are common between differing data sources, such as the BMP signaling pathway in this example. The Reactome BMP signaling pathway is shown along with the sub-graph in common with the HumanCyc
Mentions: In other cases, the maximal biclique can occur in edges between pathways from different pathway repositories. [Figure 5] demonstrates one such common sub-pathway shared by Reactome and the HumanCyc data set. The sub-pathway is specific to BMP signaling and aligns both pathways based on the maximal biclique shared between them. [Figure 6] illustrates a similar example between the HumanCyc and curated NCI PID data sets. Here, the maximal intersection between the HumanCyc prostanoid biosynthesis pathway and the PID prostaglandin biosynthesis pathway represents the complete PID prostaglandin biosynthesis pathway. The HumanCyc pathway contains two addition gene components.

Bottom Line: In addition, we illustrate how results from maximal bipartite discovery can be decomposed into hierarchical relationships, allowing shared pathway components to be mapped through various parent-child relationships to help visualization and discovery of emergent kernel driven relationships.Interrogating common relationships among biological networks and conventional GeneWeaver gene lists will increase functional specificity and reliability of the shared biological components.This approach enables self-organization of biological processes through shared biological networks.

View Article: PubMed Central - HTML - PubMed

Affiliation: Department of Computer Science, Baylor University, Waco, TX, USA.

ABSTRACT
The GeneWeaver bipartite data model provides an efficient means to evaluate shared molecular components from sets derived across diverse species, disease states and biological processes. In order to adapt this model for examining related molecular components and biological networks, such as pathway or gene network data, we have developed a means to leverage the bipartite data structure to extract and analyze shared edges. Using the Pathway Commons database we demonstrate the ability to rapidly identify shared connected components among a diverse set of pathways. In addition, we illustrate how results from maximal bipartite discovery can be decomposed into hierarchical relationships, allowing shared pathway components to be mapped through various parent-child relationships to help visualization and discovery of emergent kernel driven relationships. Interrogating common relationships among biological networks and conventional GeneWeaver gene lists will increase functional specificity and reliability of the shared biological components. This approach enables self-organization of biological processes through shared biological networks.

No MeSH data available.