A Computational Neural Approach to Support the Discovery of Gene Function and Classes of Cancer

Author(s): Azuaje, F. |

Year: 2001

Citation: IEEE Transactions on Biomedical Engineering, Vol. 48, No. 3, March 2001

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.

Topics: Machine Learning, Applications: Medical Diagnosis, Models: Fuzzy ARTMAP,

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Cross References

  1. Fuzzy ARTMAP: A neural network architecture for incremental supervised learning of analog multidimensional maps
    A new neural network architecture is introduced for incremental supervised learning of recognition categories and multidimensional maps in response to arbitrary sequences of analog or binary input vectors, which may ... Article Details