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.
R. Xu et al., "Multi-class Cancer Classification by Semi-Supervised Ellipsoid ARTMAP with Gene Expression Data," Proceedings of the 26th Annual International Conference of the IEEE Engineering in Medicine and Biology Society, 2004, Institute of Electrical and Electronics Engineers (IEEE), Sep 2004.
The definitive version is available at https://doi.org/10.1109/IEMBS.2004.1403123
26th Annual International Conference of the IEEE Engineering in Medicine and Biology Society, 2004
Electrical and Computer Engineering
Mary K. Finley Missouri Endowment
National Science Foundation (U.S.)
Keywords and Phrases
ART Neural Nets; Cancer; Genetics; Medical Diagnostic Computing; Molecular Biophysics; Neural Net Architecture; Patient Diagnosis; Tumours
Article - Conference proceedings
© 2004 Institute of Electrical and Electronics Engineers (IEEE), All rights reserved.