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

Document Type

Article

Publication Date

10-13-2014

JAX Source

BMC Proc 2014 Oct 13;8(Suppl 6):S4.

Volume

8

Issue

Suppl 6 Proceedings of the Great Lakes Bioinformatics Confer

First Page

4

Last Page

4

ISSN

1753-6561

PMID

25374613

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. BMC Proc 2014 Oct 13;8(Suppl 6):S4.

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