Multi-class cancer classification by semi-supervised ellipsoid ARTMAP with gene expression data. Xu, R., Anagnostopoulos, G. C., & Wunsch, D. In Engineering in Medicine and Biology Society, 2004. IEMBS '04. 26th Annual International Conference of the IEEE, volume 1, pages 188-191, Sept, 2004.
doi  abstract   bibtex   
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.
@InProceedings{Xu2004,
  author    = {Rui Xu and Anagnostopoulos, Georgios C. and Wunsch, D.C.},
  title     = {Multi-class cancer classification by semi-supervised ellipsoid ARTMAP with gene expression data},
  booktitle = {Engineering in Medicine and Biology Society, 2004. IEMBS '04. 26th Annual International Conference of the IEEE},
  year      = {2004},
  volume    = {1},
  pages     = {188-191},
  month     = {Sept},
  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.},
  doi       = {10.1109/IEMBS.2004.1403123},
  keywords  = {ART neural nets;cancer;genetics;medical diagnostic computing;molecular biophysics;neural net architecture;patient diagnosis;tumours;DNA microarray technologies;adaptive resonance theory;cancer diagnosis;cancer treatment;gene expression;multi-class cancer classification;neural network architecture;semi-supervised ellipsoid ARTMAP;tumor;Cancer;DNA;Drugs;Ellipsoids;Gene expression;Medical treatment;Neoplasms;Neural networks;Resonance;Subspace constraints;Cancer classification;Gene expression data;Semi-supervised Ellipsoid ARTMAP},
}

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