A massively parallel architecture for a self-organizing neural pattern recognition machine
Computer Vision, Graphics, and Image Processing
Neural Networks
Adaptive resonance theory microchips: circuit design techniques
Adaptive resonance theory microchips: circuit design techniques
Tissue classification with gene expression profiles
RECOMB '00 Proceedings of the fourth annual international conference on Computational molecular biology
Swarm intelligence
IEEE Spectrum
Self-Organizing Maps
Parameter Selection in Particle Swarm Optimization
EP '98 Proceedings of the 7th International Conference on Evolutionary Programming VII
Machine learning in DNA microarray analysis for cancer classification
APBC '03 Proceedings of the First Asia-Pacific bioinformatics conference on Bioinformatics 2003 - Volume 19
Hypersphere ART and ARTMAP for Unsupervised and Supervised, Incremental Learning
IJCNN '00 Proceedings of the IEEE-INNS-ENNS International Joint Conference on Neural Networks (IJCNN'00)-Volume 6 - Volume 6
An optoelectronic learning machine
An optoelectronic learning machine
Novel approaches in adaptive resonance theory for machine learning
Novel approaches in adaptive resonance theory for machine learning
Pattern Classification (2nd Edition)
Pattern Classification (2nd Edition)
Mining phenotypes and informative genes from gene expression data
Proceedings of the ninth ACM SIGKDD international conference on Knowledge discovery and data mining
A rank sum test method for informative gene discovery
Proceedings of the tenth ACM SIGKDD international conference on Knowledge discovery and data mining
A study of cross-validation and bootstrap for accuracy estimation and model selection
IJCAI'95 Proceedings of the 14th international joint conference on Artificial intelligence - Volume 2
Acquiring rule sets as a product of learning in a logical neural architecture
IEEE Transactions on Neural Networks
Survey of clustering algorithms
IEEE Transactions on Neural Networks
Using default ARTMAP for cancer classification with microRNA expression signatures
IJCNN'09 Proceedings of the 2009 international joint conference on Neural Networks
Artificial Intelligence in Medicine
Computational Biology and Chemistry
Semi-supervised Bayesian ARTMAP
Applied Intelligence
BARTMAP: A viable structure for biclustering
Neural Networks
Parallel cooperative micro-particle swarm optimization: A master-slave model
Applied Soft Computing
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It is crucial for cancer diagnosis and treatment to accurately identify the site of origin of a tumor. With the emergence and rapid advancement of DNA microarray technologies, constructing gene expression profiles for different cancer types has already become a promising means for cancer classification. In addition to research on binary classification such as normal versus tumor samples, which attracts numerous efforts from a variety of disciplines, the discrimination of multiple tumor types is also important. Meanwhile, the selection of genes which are relevant to a certain cancer type not only improves the performance of the classifiers, but also provides molecular insights for treatment and drug development. Here, we use Semisupervised Ellipsoid ARTMAP (ssEAM) for multiclass cancer discrimination and particle swarm optimization for informative gene selection. ssEAM is a neural network architecture rooted in Adaptive Resonance Theory and suitable for classification tasks. ssEAM features fast, stable, and finite learning and creates hyperellipsoidal clusters, inducing complex nonlinear decision boundaries. PSO is an evolutionary algorithm-based technique for global optimization. A discrete binary version of PSO is employed to indicate whether genes are chosen or not. The effectiveness of ssEAM/PSO for multiclass cancer diagnosis is demonstrated by testing it on three publicly available multiple-class cancer data sets. ssEAM/PSO achieves competitive performance on all these data sets, with results comparable to or better than those obtained by other classifiers.