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Issue No.04 - July-Aug. (2012 vol.9)
pp: 1106-1119
A. Nowe , Dept. of Comput. Sci., Vrije Univ. Brussel, Brussels, Belgium
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.
INDEX TERMS
information filters, arrays, bioinformatics, genetics, standardized notations, gene expression microarray analysis, combinatorial chemistry, text mining, multivariate imaging, bioinformatics, filter feature selection methods, GEM analysis, differentially expressed gene discovery, gene prioritization, biomarker discovery, Gene expression, Taxonomy, Bioinformatics, Measurement, Search methods, Computational biology, gene expression data., Feature selection, information filters, gene ranking, biomarker discovery, gene prioritization, scoring functions, statistical methods
CITATION
A. Nowe, "A Survey on Filter Techniques for Feature Selection in Gene Expression Microarray Analysis", IEEE/ACM Transactions on Computational Biology and Bioinformatics, vol.9, no. 4, pp. 1106-1119, July-Aug. 2012, doi:10.1109/TCBB.2012.33
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