Effective feature selection framework for cluster analysis of microarray data.
Effective feature selection framework for cluster analysis of microarray data.
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DOI：
10.6026/97320630004385
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发表时间：
2010-02-28
期刊：
影响因子：
1.9
通讯作者：
Ryu KH
中科院分区：
文献类型：
--
作者：
Pok G;Liu JC;Ryu KH
微阵列技术已经成为同时检测在不同环境下测量的所有基因表达的标准手段。由于微阵列数据的典型特征是高维特征和少量样本，因此需要结合特征选择来确定对生物学解释有意义并对样本变异负责的基因子集。在这篇文章中，我们提出了一种简单而有效的特征选择框架，适用于二维微阵列数据。我们的基于相关性的非参数方法允许用少量的基因紧凑地表示特定于类的属性。我们使用公开的实验数据对我们的方法进行了评估，并获得了良好的结果。
The microarray technique has become a standard means in simultaneously examining expression of all genes measured in different circumstances. As microarray data are typically characterized by high dimensional features with a small number of samples, feature selection needs to be incorporated to identify a subset of genes that are meaningful for biological interpretation and accountable for the sample variation. In this article, we present a simple, yet effective feature selection framework suitable for two-dimensional microarray data. Our correlation-based, nonparametric approach allows compact representation of class-specific properties with a small number of genes. We evaluated our method using publicly available experimental data and obtained favorable results.