Edge-weighting of gene expression graphs

Kerr, Grainne, Perrin, Dimitri, Ruskin, Heather J, & Crane, Martin (2010) Edge-weighting of gene expression graphs. Advances in Complex Systems, 13(2), pp. 217-238.

View at publisher


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. Keywords: Edge-weighting; weighted graphs; gene expression; bi-clustering

Impact and interest:

2 citations in Scopus
Search Google Scholar™

Citation counts are sourced monthly from Scopus and Web of Science® citation databases.

These databases contain citations from different subsets of available publications and different time periods and thus the citation count from each is usually different. Some works are not in either database and no count is displayed. Scopus includes citations from articles published in 1996 onwards, and Web of Science® generally from 1980 onwards.

Citations counts from the Google Scholar™ indexing service can be viewed at the linked Google Scholar™ search.

Full-text downloads:

18 since deposited on 24 Mar 2015
2 in the past twelve months

Full-text downloads displays the total number of times this work’s files (e.g., a PDF) have been downloaded from QUT ePrints as well as the number of downloads in the previous 365 days. The count includes downloads for all files if a work has more than one.

ID Code: 82672
Item Type: Journal Article
Refereed: No
DOI: 10.1142/S0219525910002505
ISSN: 1793-6802
Divisions: Current > QUT Faculties and Divisions > Science & Engineering Faculty
Copyright Owner: World Scientific Publishing Company
Deposited On: 24 Mar 2015 03:22
Last Modified: 24 Jun 2017 09:01

Export: EndNote | Dublin Core | BibTeX

Repository Staff Only: item control page