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Selecting a smaller subset of informative genes from microarray data via a three-stage method

机译:通过三阶段方法从微阵列数据中选择较小的信息基因子集

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摘要

Microarray data produced by microarray are useful for cancer classification. However, the process of gene selection for the classification faces with a major problem due to the properties of the data such as the small number of samples compared to the huge number of genes (higher-dimensional data), irrelevant genes, and noisy data. Hence, this paper proposes a three-stage gene selection method to select a smaller subset of informative genes that is most relevant for the cancer classification. It has three stages: I) pre-selecting genes using a filter method to produce a subset of genes; 2) optimising the gene subset using a multi-objective hybrid method to yield near-optimal subsets of genes; 3) analysing the frequency of appearance of each gene in the different near-optimal gene subsets to produce a smaller (final) subset of informative genes. Two microarray data sets are used to test the effectiveness of the proposed method. Experimental results show that the performance of the proposed method is superior to other experimental methods and related previous works. A list of informative genes in the final gene subset is also presented for biological usage.
机译:由微阵列产生的微阵列数据可用于癌症分类。但是,由于数据的特性,例如与大量基因(高维数据),不相关基因和嘈杂数据相比,样本数量少,用于分类的基因选择过程面临着一个主要问题。因此,本文提出了一种三阶段基因选择方法,以选择与癌症分类最相关的较小信息基因子集。它包括三个阶段:I)使用过滤方法预选基因以产生基因的子集; 2)使用多目标杂交方法优化基因子集,以产生接近最优的基因子集; 3)分析每个基因在不同的近优基因子集中出现的频率,以产生较小(最终)的信息基因子集。使用两个微阵列数据集来测试所提出方法的有效性。实验结果表明,该方法的性能优于其他实验方法和相关的先前工作。还提供了最终基因子集中的信息基因列表,供生物学使用。

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