MicroRNA expression profile based cancer classification using Default ARTMAP

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 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.