A computational neural approach to support the discovery of gene function and classes of cancer.
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.
Azuaje
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Artificial Intelligence Group and Centre for Health Informatics, Trinity College, Dublin, Ireland.
D000465:Algorithms; D018076:DNA, Complementary / Q000032:analysis; D004273:DNA, Neoplasm / Q000032:analysis; D003936:Diagnosis, Computer-Assisted; D017143:Fuzzy Logic; D015972:Gene Expression Regulation, Neoplastic; D006801:Humans; D016247:Information Storage and Retrieval; D016403:Lymphoma, Large B-Cell, Diffuse / Q000145:classification* / Q000235:genetics*; D009369:Neoplasms / Q000145:classification / Q000235:genetics; D016571:Neural Networks, Computer; D011237:Predictive Value of Tests; D012016:Reference Values; D012680:Sensitivity and Specificity; D014584:User-Computer Interface
D018076:DNA, Complementary; D004273:DNA, Neoplasm