A computational neural approach to support the discovery of gene function and classes of cancer

Author

Azuaje, Francisco

Author_Institution

Centre for Health Inf., Trinity Coll., Dublin, Ireland

Volume

48

Issue

3

fYear

2001

fDate

3/1/2001 12:00:00 AM

Firstpage

332

Lastpage

339

Abstract

Advances in molecular classification of tumours may play a central role in cancer treatment. Here, a novel approach to genome expression pattern interpretation is described and applied to the recognition of B-cell malignancies as a test set. Using cDNA microarrays data generated by a previous study, a neural network model known as 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.

Keywords

DNA; cancer; data mining; fuzzy neural nets; genetics; medical diagnostic computing; tumours; B-cell malignancies recognition; cDNA microarrays data; cancer classes; computational neural approach; diffuse large B-cell lymphoma patients; gene function discovery; genome expression pattern interpretation; neural network model; simplified fuzzy ARTMAP; tumours molecular classification; Bioinformatics; Biological information theory; Cancer; Data mining; Gene expression; Genomics; Neural networks; Organisms; Probes; Tumors; Algorithms; DNA, Complementary; DNA, Neoplasm; Diagnosis, Computer-Assisted; Fuzzy Logic; Gene Expression Regulation, Neoplastic; Humans; Information Storage and Retrieval; Lymphoma, Large B-Cell, Diffuse; Neoplasms; Neural Networks (Computer); Predictive Value of Tests; Reference Values; Sensitivity and Specificity; User-Computer Interface;

fLanguage

English

Journal_Title

Biomedical Engineering, IEEE Transactions on

Publisher

ieee

ISSN

0018-9294

Type

jour

DOI

10.1109/10.914796

Filename

914796