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BMC Bioinformatics

, 15:P32 | Cite as

Algorithmic tools for tripartite data analysis

  • Charles A Phillips
  • Erich J Baker
  • Elissa J Chesler
  • Michael A Langston
Open Access
Poster presentation

Keywords

Bipartite Graph Search Tree Binary Search Maximum Clique General Graph 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

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.

Copyright information

© 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.

Authors and Affiliations

  • Charles A Phillips
    • 1
  • Erich J Baker
    • 2
  • Elissa J Chesler
    • 3
  • Michael A Langston
    • 1
  1. 1.Department of Electrical Engineering and Computer ScienceUniversity of TennesseeKnoxvilleUSA
  2. 2.Bioinformatics ProgramSchool of Engineering and Computer Science, Baylor UniversityWacoUSA
  3. 3.The Jackson LaboratoryBar HarborUSA

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