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• W2534598064 abstract "To accurately identify the site of origin of a tumor is crucial to cancer diagnosis and treatment. With the emergence of DNA microarray technologies, constructing gene expression profiles for different cancer types has already become a promising means for cancer classification. In addition to binary classification, the discrimination of multiple tumor types is also important semi-supervised ellipsoid ARTMAP (ssEAM) is a novel neural network architecture rooted in adaptive resonance theory suitable for classification tasks. ssEAM can achieve fast, stable and finite learning and create hyper-ellipsoidal clusters inducing complex nonlinear decision boundaries. Here, we demonstrate the capability of ssEAM to discriminate multi-class cancer through analyzing two publicly available cancer datasets based on their gene expression profiles." @default.
• W2534598064 created "2016-10-28" @default.
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• W2534598064 date "2005-03-21" @default.
• W2534598064 modified "2023-09-24" @default.
• W2534598064 title "Multi-class cancer classification by semi-supervised ellipsoid ARTMAP with gene expression data" @default.
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• W2534598064 doi "https://doi.org/10.1109/iembs.2004.1403123" @default.
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• W2534598064 hasPublicationYear "2005" @default.
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