Xu Rui, Xu Jie, Wunsch Donald C
Applied Computational Intelligence Laboratory, Department of Electrical and Computer Engineering, Missouri University of Science & Technology, MO 65409, USA.
Neural Netw. 2009 Jul-Aug;22(5-6):774-80. doi: 10.1016/j.neunet.2009.06.018. Epub 2009 Jul 1.
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.
与基于传统形态学或临床参数的方法相比，利用微阵列进行的高通量信使核糖核酸（mRNA）表达谱分析已被证明是一种更有效的癌症诊断和治疗方法。最近，一类名为微小核糖核酸（miRNAs）的小非编码RNA的发现，为癌症分类提供了另一种有前景的方法。miRNAs通过作为癌基因或肿瘤抑制因子发挥作用，在肿瘤发生过程中起着关键作用。在此，我们应用基于神经网络的分类器Default ARTMAP，根据其miRNA表达指纹对多种类型的癌症进行分类。由于miRNA表达数据通常具有高维度，因此使用粒子群优化（PSO）来选择有助于区分不同癌症类型的重要miRNAs。对多种人类癌症的实验结果表明，Default ARTMAP在所有数据上的表现都始终良好，分类准确率优于或与其他流行分类器相当。此外，信息性miRNAs的选择可以进一步提高分类器的性能，并为癌症研究人员提供有意义的见解。
