A Survey on Filter Techniques for Feature Selection in Gene Expression Microarray Analysis

Authors:
Cosmin Lazar;Jonatan Taminau;Stijn Meganck;David Steenhoff;Alain Coletta;Colin Molter;Virginie de Schaetzen;Robin Duque;Hugues Bersini;Ann Nowe
Affiliations:
Vrije Universiteit Brussel, Brussels;Vrije Universiteit Brussel, Brussels;Vrije Universiteit Brussel, Brussels;Vrije Universiteit Brussel, Brussels;Université Libre de Bruxelles, Brussels;Université Libre de Bruxelles, Brussels;Vrije Universiteit Brussel, Brussels;Université Libre de Bruxelles, Brussels;Université Libre de Bruxelles, Brussels;Vrije Universiteit Brussel, Brussels
Venue:
IEEE/ACM Transactions on Computational Biology and Bioinformatics (TCBB)
Year:
2012

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Abstract

A plenitude of feature selection (FS) methods is available in the literature, most of them rising as a need to analyze data of very high dimension, usually hundreds or thousands of variables. Such data sets are now available in various application areas like combinatorial chemistry, text mining, multivariate imaging, or bioinformatics. As a general accepted rule, these methods are grouped in filters, wrappers, and embedded methods. More recently, a new group of methods has been added in the general framework of FS: ensemble techniques. The focus in this survey is on filter feature selection methods for informative feature discovery in gene expression microarray (GEM) analysis, which is also known as differentially expressed genes (DEGs) discovery, gene prioritization, or biomarker discovery. We present them in a unified framework, using standardized notations in order to reveal their technical details and to highlight their common characteristics as well as their particularities.