MinCAR-Classifier for classifying lung cancer gene expression dataset

Wael Zakaria, Y. Kotb, F. Ghaleb
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引用次数: 1

Abstract

DNA microarray technology assists researchers to learn more about different diseases especially the study of the cancer diseases. Using the microarray technology, it will be possible for the researchers to further classify the types of cancer on the basis of the patterns of gene activity (gene expression) in the tumor cells. This will tremendously help the pharmaceutical community to develop more effective drugs as the treatment strategies will be targeted directly to the specific type of cancer. The classification technique is one of the important data mining techniques that is used for classifying the DNA microarray datasets. The aim of this paper is to build an accurate classifier framework called MinCAR-Classifier that mines all minimal high confident class association rules, MinCAR, from cancer microarray datasets. Based on lung cancer microarray dataset, the comparative studies show that our proposed MinCAR-Classifier framework is more accurate than other well-known classifier frameworks.
肺癌基因表达数据集的mincar分类器
DNA微阵列技术有助于研究人员更多地了解不同的疾病,特别是癌症疾病的研究。利用微阵列技术,研究人员将有可能根据肿瘤细胞中基因活性(基因表达)的模式进一步分类癌症的类型。这将极大地帮助医药界开发更有效的药物,因为治疗策略将直接针对特定类型的癌症。分类技术是用于DNA微阵列数据集分类的重要数据挖掘技术之一。本文的目的是建立一个精确的分类器框架,称为MinCAR- classifier,从癌症微阵列数据集中挖掘所有最小高置信度类关联规则MinCAR。基于肺癌微阵列数据集的对比研究表明,我们提出的MinCAR-Classifier框架比其他已知的分类器框架更准确。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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