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

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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:

2000

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