Display Settings:

Format

Send to:

Choose Destination
IEEE/ACM Trans Comput Biol Bioinform. 2012 Jul-Aug;9(4):1106-19. doi: 10.1109/TCBB.2012.33.

A survey on filter techniques for feature selection in gene expression microarray analysis.

Author information

  • 1Computational Modeling Group, Department of Computer Science, Vrije Universiteit Brussel, Pleinlaan 2, Brussels 1050, Belgium. vlazar@vub.ac.be

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.

PMID:
22350210
[PubMed - indexed for MEDLINE]
PubMed Commons home

PubMed Commons

0 comments
How to join PubMed Commons

    Supplemental Content

    Full text links

    Icon for IEEE Computer Society
    Loading ...
    Write to the Help Desk