Null space based feature selection method for gene expression data
International Journal of Machine Learning and CyberneticsPublished 28 November 2011
Alok Sharma, Seiya Imoto, Satoru Miyano, Vandana Sharma
Citations74
SJR quartileQ2
SJR score0.69
SNIP0.96
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TL;DR
A null space based feature selection method for gene expression data in terms of supervised classification that discards the redundant genes by applying the information of null space of scatter matrices is proposed.
Abstract
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.
Keywords
Computer ScienceBiochemistry, Genetics and Molecular Biology
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