A massively parallel architecture for a self-organizing neural pattern recognition machine
Computer Vision, Graphics, and Image Processing
Neural Networks
Learning internal representations by error propagation
Parallel distributed processing: explorations in the microstructure of cognition, vol. 1
C4.5: programs for machine learning
C4.5: programs for machine learning
The nature of statistical learning theory
The nature of statistical learning theory
Fast training of support vector machines using sequential minimal optimization
Advances in kernel methods
Neural Networks - 2003 Special issue: Advances in neural networks research — IJCNN'03
IEEE/ACM Transactions on Computational Biology and Bioinformatics (TCBB)
IEEE/ACM Transactions on Computational Biology and Bioinformatics (TCBB)
MicroRNAs and cancer-the search begins!
IEEE Transactions on Information Technology in Biomedicine
Using default ARTMAP for cancer classification with microRNA expression signatures
IJCNN'09 Proceedings of the 2009 international joint conference on Neural Networks
Particle Swarm Optimization: Basic Concepts, Variants and Applications in Power Systems
IEEE Transactions on Evolutionary Computation
IEEE Transactions on Neural Networks
Exploring features and classifiers to classify microRNA expression profiles of human cancer
ICONIP'10 Proceedings of the 17th international conference on Neural information processing: models and applications - Volume Part II
BARTMAP: A viable structure for biclustering
Neural Networks
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High-throughput messenger RNA (mRNA) expression profiling with microarray has been demonstrated as a more effective method of cancer diagnosis and treatment than the traditional morphology or clinical parameter based methods. Recently, the discovery of a category of small non-coding RNAs, named microRNAs (miRNAs), provides another promising method of cancer classification. miRNAs play a critical role in the tumorigenic process by functioning either as oncogenes or as tumor suppressors. Here, we apply a neural based classifier, Default ARTMAP, to classify broad types of cancers based on their miRNA expression fingerprints. As the miRNA expression data usually have high dimensionalities, particle swarm optimization (PSO) is used for selecting important miRNAs that contribute to the discrimination of different cancer types. Experimental results on the multiple human cancers show that Default ARTMAP performs consistently well on all the data, and the classification accuracy is better than or comparable to that of the other popular classifiers. Also, the selection of informative miRNAs can further improve the performance of classifiers and provide meaningful insights into cancer researchers.