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A survey on filter techniques for feature selection in gene expression microarray analysis This publication appears in: IEEE/ACM Transactions on Computational Biology and Bioinformatics Authors: V. Cosmin Lazar, J. Taminau, S. Meganck, D. Frederik Steenhoff, A. Coletta, C. Molter, V. De Schaetzen Van Brienen, R. Duqué, H. Bersini and A. Nowé Volume: 99 Number of Pages: 14 Publication Date: Feb. 2012
Abstract: A plenitude of feature selection (FS) methods is available in the literature, most of them rising as a need to analyse data of very high dimension, usually hundreds or thousands of variables. Such datasets 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 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. External Link.
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