Making Genome Expression Data Meaningful: Prediction and Discovery of Classes of Cancer through a Connectionist Learning Approach

Authors:
F. Azuaje
Affiliations:
-
Venue:
BIBE '00 Proceedings of the 1st IEEE International Symposium on Bioinformatics and Biomedical Engineering
Year:
2000

Citing 0
Cited 2

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Abstract

Despite more than 30 years of experimental research, there have been no generic models to classify tumours and identify new types of cancer. Similarly, advances in the molecular classification of tumours may play a central role in cancer treatment. In this paper, a new approach to genome expression pattern interpretation is described and applied to the recognition of B-cell malignancies as a test set. Using DNA microarray data generated by A.A. Alizadeh et al. (2000), a neural network model known as a simplified fuzzy ARTMAP is able to identify normal and diffuse large B-cell lymphoma (DLBCL) patients. Furthermore, it discovers the distinction between patients with molecularly distinct forms of DLBCL without previous knowledge of those subtypes.