A novel multi-stage feature selection method for microarray expression data analysis
A novel multi-stage feature selection method for microarray expression data analysis
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一种用于微阵列表达数据分析的新型多阶段特征选择方法
DOI：
10.1504/ijdmb.2013.050977
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发表时间：
2013-01-01
影响因子：
0.3
通讯作者：
Liang, Yanchun
中科院分区：
文献类型：
--
作者：
Du, Wei;Sun, Ying;Liang, Yanchun
随着基因组研究的深入，寻找有效的肿瘤分类方法和生物标志物的检测方法已成为一个具有挑战性的问题。本文提出了一种新的多阶段特征选择方法，该方法综合考虑了原始基因集中的各种基因。该方法剔除了不相关、噪声和冗余基因，并在不同阶段选择相关基因的子集。在白血病、前列腺、结肠、乳腺、神经和DLBCL等基因芯片数据集上，采用不同的分类器对该方法进行了检验，最佳分类准确率分别为100%、98.04%、100%、89.74%、100%和98.28%。
With the development of genome research, finding method to classify cancer and detect biomarkers efficiently has become a challenging problem. In this paper, a novel multi-stage method for feature selection is proposed which considers all kinds of genes in the original gene set. The method eliminates the irrelevant, noisy and redundant genes and selects a subset of relevant genes at different stages. The proposed method is examined on microarray datasets of Leukemia, Prostate, Colon, Breast, Nervous and DLBCL by different classifiers and the best accuracies of the method in these datasets are 100%, 98.04%, 100%, 89.74%, 100% and 98.28%, respectively.