By Topic

Gene expression patterns and cancer classification: a self-adaptive and incremental neural approach

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

1 Author(s)
Azuaje, F. ; Dept. of Comput. Sci., Dublin Univ., Ireland

The automated interpretation of data originating from the human genome may play a crucial role in cancer treatment. In this paper, a new computational approach to the discovery and analysis of gene expression patterns is presented and applied to the recognition of B-cell malignancies as a test set. Using cDNA microarray data, an unsupervised and self-adaptive neural network model known as “growing cell structures” is able to identify normal and diffuse large B-cell lymphoma (DLBCL) patients. Furthermore, it discovers the distinction between patients with molecularly distinct types of DLBCL without previous knowledge of those subclasses

Published in:

Information Technology Applications in Biomedicine, 2000. Proceedings. 2000 IEEE EMBS International Conference on

Date of Conference: