Null space based feature selection method for gene expression data
Null space based feature selection method for gene expression data
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DOI：
10.1007/s13042-011-0061-9
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发表时间：
2012-12-01
影响因子：
5.6
通讯作者：
Sharma, Vandana
中科院分区：
文献类型：
--
作者：
Sharma, Alok;Imoto, Seiya;Sharma, Vandana
在基因表达数据分析中，特征选择是一个非常重要的过程.特征选择方法从数千个基因中丢弃不重要的基因，以寻找针对癌症等目标生物现象的重要基因或途径。所获得的基因子集用于预测的统计分析，例如存活以及用于理解生物学特性的功能分析。在本文中，我们提出了一种基于零空间的基因表达数据的监督分类的特征选择方法。该方法利用散布矩阵的零空间信息，剔除冗余基因。我们从理论上推导了该方法，并在几个DNA基因表达数据集上证明了其有效性。该方法易于实现，计算效率高。
Feature selection is quite an important process in gene expression data analysis. Feature selection methods discard unimportant genes from several thousands of genes for finding important genes or pathways for the target biological phenomenon like cancer. The obtained gene subset is used for statistical analysis for prediction such as survival as well as functional analysis for understanding biological characteristics. In this paper we propose a null space based feature selection method for gene expression data in terms of supervised classification. The proposed method discards the redundant genes by applying the information of null space of scatter matrices. We derive the method theoretically and demonstrate its effectiveness on several DNA gene expression datasets. The method is easy to implement and computationally efficient.