Browse DORAS
Browse Theses
Latest Additions
Creative Commons License
Except where otherwise noted, content on this site is licensed for use under a:

Edge-weighting of gene expression graphs

Kerr, Gráinne and Perrin, Dimitri and Ruskin, Heather J. and Crane, Martin (2009) Edge-weighting of gene expression graphs. Advances in Complex Systems, 13 (2). pp. 217-238. ISSN 0219-5259

Full text available as:

PDF - Requires a PDF viewer such as GSview, Xpdf or Adobe Acrobat Reader


In recent years, considerable research efforts have been directed to micro-array technologies and their role in providing simultaneous information on expression profiles for thousands of genes. These data, when subjected to clustering and classification procedures, can assist in identifying patterns and providing insight on biological processes. To understand the properties of complex gene expression datasets, graphical representations can be used. Intuitively, the data can be represented in terms of a bipartite graph, with weighted edges corresponding to gene-sample node couples in the dataset. Biologically meaningful subgraphs can be sought, but performance can be influenced both by the search algorithm, and, by the graph-weighting scheme and both merit rigorous investigation. In this paper, we focus on edge-weighting schemes for bipartite graphical representation of gene expression. Two novel methods are presented: the first is based on empirical evidence; the second on a geometric distribution. The schemes are compared for several real datasets, assessing efficiency of performance based on four essential properties: robustness to noise and missing values, discrimination, parameter influence on scheme efficiency and reusability. Recommendations and limitations are briefly discussed.

Item Type:Article (Published)
Additional Information:Electronic version of an article published as Advances in Complex Systems, 13, 2, 2009, 217-238. doi:10.1142/S0219525910002505 ©2009 World Scientific Publishing Company.
Uncontrolled Keywords:edge-weighting; weighted graphs; gene expression; bi-clustering;
Subjects:Biological Sciences > Bioinformatics
Mathematics > Numerical analysis
Physical Sciences > Statistical physics
Computer Science > Computer simulation
DCU Faculties and Centres:Research Initiatives and Centres > Scientific Computing and Complex Systems Modelling (Sci-Sym)
DCU Faculties and Schools > Faculty of Engineering and Computing > School of Computing
Publisher:World Scientific Publishing
Official URL:
Copyright Information:©2009 World Scientific Publishing Company
Use License:This item is licensed under a Creative Commons Attribution-NonCommercial-Share Alike 3.0 License. View License
Funders:National Institute for Cellular Biotechnology (NICB), Irish Research Council for Science Engineering and Technology
ID Code:15384
Deposited On:24 May 2010 11:10 by Martin Crane. Last Modified 04 Nov 2016 13:43

Download statistics

Archive Staff Only: edit this record