Title

Using Default ARTMAP for Cancer Classification with MicroRNA Expression Signatures

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 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 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.

Meeting Name

International Joint Conference on Neural Networks, IJCNN 2009 (2009: Jun. 14-19, Atlanta, GA)

Department(s)

Electrical and Computer Engineering

Research Center/Lab(s)

Missouri University of Science and Technology. Applied Computational Intelligence Laboratory

International Standard Book Number (ISBN)

9781424435531

Document Type

Article - Conference proceedings

Document Version

Citation

File Type

text

Language(s)

English

Rights

© 2009 Institute of Electrical and Electronics Engineers (IEEE), All rights reserved.


Share

 
COinS