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International Journal of Machine Learning and Cybernetics

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01-12-2012 | Original Article

Null space based feature selection method for gene expression data

Authors: Alok Sharma, Seiya Imoto, Satoru Miyano, Vandana Sharma

Published in: International Journal of Machine Learning and Cybernetics | Issue 4/2012

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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.

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Most of the datasets are downloaded from the Kent Ridge Bio-medical Dataset (KRBD) (http://datam.i2r.a-star.edu.sg/datasets/krbd/). The datasets are transformed or reformatted and made available by KRBD repository and we have used them without any further preprocessing. Some datasets which are not available on KRBD repository are downloaded and directly used from respective authors’ supplement link. The URL addresses for all the datasets are given in the Reference Section.

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Title: Null space based feature selection method for gene expression data
Authors: Alok Sharma
Seiya Imoto
Satoru Miyano
Vandana Sharma
Publication date: 01-12-2012
Publisher: Springer-Verlag
Published in: International Journal of Machine Learning and Cybernetics / Issue 4/2012
Print ISSN: 1868-8071
Electronic ISSN: 1868-808X
DOI: https://doi.org/10.1007/s13042-011-0061-9

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