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

Content Provider	IEEE Xplore Digital Library
Author	Lazar, C. Taminau, J. Meganck, S. Steenhoff, D. Coletta, A. Molter, C. de Schaetzen, V. Duque, R. Bersini, H. Nowe, A.
Copyright Year	2004
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.
Sponsorship	IEEE Computer Society
Page Count	14
File Size	1762094
Starting Page	1106
Ending Page	1119
File Format	PDF
ISSN	15455963
Volume Number	9
Issue Number	4
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2012-07-01
Publisher Place	U.S.A.
Access Restriction	One Nation One Subscription (ONOS)
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	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
Content Type	Text
Resource Type	Article
Subject	Applied Mathematics Genetics Biotechnology

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