By Topic

An ontology-driven clustering method for supporting gene expression analysis

Sign In

Cookies must be enabled to login.After enabling cookies , please use refresh or reload or ctrl+f5 on the browser for the login options.

Formats Non-Member Member
$31 $13
Learn how you can qualify for the best price for this item!
Become an IEEE Member or Subscribe to
IEEE Xplore for exclusive pricing!
close button

puzzle piece

IEEE membership options for an individual and IEEE Xplore subscriptions for an organization offer the most affordable access to essential journal articles, conference papers, standards, eBooks, and eLearning courses.

Learn more about:

IEEE membership

IEEE Xplore subscriptions

3 Author(s)
Haiying Wang ; Sch. of Comput. & Math., Ulster Univ., Jordanstown, UK ; Azuaje, F. ; Bodenreider, O.

The gene ontology (GO) is an important knowledge resource for biologists and bioinformaticians. This paper explores the integration of similarity information derived from GO into clustering-based gene expression analysis. A system that integrates GO annotations, similarity patterns and expression data in yeast is assessed. In comparison with a clustering model based only on expression data correlation, the proposed framework not only produces consistent results, but also it offers alternative, potentially meaningful views of the biological problem under study. Moreover, it provides the basis for developing other automated, knowledge-driven data mining systems in this and related application areas.

Published in:

Computer-Based Medical Systems, 2005. Proceedings. 18th IEEE Symposium on

Date of Conference:

23-24 June 2005