Using default ARTMAP for cancer classification with microRNA expression signatures

Authors:
Rui Xu;Jie Xu;Donald C. Wunsch
Affiliations:
Applied Computational Intelligence Laboratory, Department of Electrical & Computer Engineering, Missouri University of Science & Technology, Rolla, MO;Department of Industrial Engineering & Management Sciences, Northwestern University, Evanston, IL;Department of Electrical & Computer Engineering, Missouri University of Science & Technology, Rolla, MO
Venue:
IJCNN'09 Proceedings of the 2009 international joint conference on Neural Networks
Year:
2009

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Abstract

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 class of small noncoding 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 different types of cancers based on their miRNA expression fingerprints. 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.