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BMC Bioinformatics

Open Access

Algorithmic tools for tripartite data analysis

  • Charles A Phillips1,
  • Erich J Baker2,
  • Elissa J Chesler3 and
  • Michael A Langston1Email author
BMC Bioinformatics201415(Suppl 10):P32

https://doi.org/10.1186/1471-2105-15-S10-P32

Published: 29 September 2014

Background

Bipartite graphs have many applications. Examples include the modeling of gene-disease associations, substrate-enzyme relationships and protein-protein interactions. Numerous algorithms have been proposed to extract dense subgraphs from bipartite graphs.

Materials and methods

In this work, tripartite graphs are considered. Applications include comparing two sets of many gene-many disease associations. An algorithm is described that finds a maximum triclique in such a graph. It employs a branching strategy inspired by maximum clique algorithms for general graphs. A binary search tree is used, in which branch nodes in the tree represent vertices in the tripartite graph, and in which branching decisions are based on whether a vertex is in or out of a maximum triclique. A reduction rule is also introduced to filter out irrelevant vertices. This algorithm was developed in the context of GeneWeaver, an online system for the integration of functional genomics experimental results. In this system triclique extraction will enable fast transitive association of diseases based on the similarity of gene-disease associations from many experiments. Computational experience with huge volumes of experimental data is described.

Authors’ Affiliations

(1)
Department of Electrical Engineering and Computer Science, University of Tennessee
(2)
Bioinformatics Program, School of Engineering and Computer Science, Baylor University
(3)
The Jackson Laboratory

Copyright

© Phillips et al; licensee BioMed Central Ltd. 2014

This article is published under license to BioMed Central Ltd. This is an Open Access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/4.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. The Creative Commons Public Domain Dedication waiver (http://creativecommons.org/publicdomain/zero/1.0/) applies to the data made available in this article, unless otherwise stated.

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