Skip to main content
Download PDF

Volume 15 Supplement 10

UT-KBRIN Bioinformatics Summit 2014: Abstracts

  • Poster presentation
  • Open access
  • Published:

Algorithmic tools for tripartite data analysis

BMC Bioinformatics volume 15, Article number: P32 (2014) Cite this article

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.

Author information

Authors and Affiliations

  1. Department of Electrical Engineering and Computer Science, University of Tennessee, Knoxville, TN, 37996, USA

    Charles A Phillips & Michael A Langston

  2. Bioinformatics Program, School of Engineering and Computer Science, Baylor University, Waco, TX, 76798, USA

    Erich J Baker

  3. The Jackson Laboratory, Bar Harbor, ME, 04609, USA

    Elissa J Chesler

Authors
  1. Charles A Phillips
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Erich J Baker
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Elissa J Chesler
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Michael A Langston
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Michael A Langston.

Rights and permissions

Open Access  This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made.

The images or other third party material in this article are included in the article’s Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article’s Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder.

To view a copy of this licence, visit https://creativecommons.org/licenses/by/4.0/.

The Creative Commons Public Domain Dedication waiver (https://creativecommons.org/publicdomain/zero/1.0/) applies to the data made available in this article, unless otherwise stated in a credit line to the data.

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Phillips, C.A., Baker, E.J., Chesler, E.J. et al. Algorithmic tools for tripartite data analysis. BMC Bioinformatics 15 (Suppl 10), P32 (2014). https://doi.org/10.1186/1471-2105-15-S10-P32

Download citation

  • Published:

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

Keywords

BMC Bioinformatics

ISSN: 1471-2105

Contact us