Vasile Cosmin Lazar, Jonatan Taminau, Stijn Meganck, David Steenhoff, Alain Coletta, Colin Molter, Virginie De Schaetzen Van Brienen, Robin Duqué, Hugues Bersini, Ann Nowe
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.
Lazar, VC, Taminau, J, Meganck, S, Steenhoff, D, Coletta, A, Molter, C, De Schaetzen Van Brienen, V, Duqué, R, Bersini, H & Nowe, A 2012, 'A survey on filter techniques for feature selection in gene expression microarray analysis', IEEE/ACM Transactions on Computational Biology and Bioinformatics, vol. 99. <http://www.ncbi.nlm.nih.gov/pubmed/22350210>
Lazar, V. C., Taminau, J., Meganck, S., Steenhoff, D., Coletta, A., Molter, C., De Schaetzen Van Brienen, V., Duqué, R., Bersini, H., & Nowe, A. (2012). A survey on filter techniques for feature selection in gene expression microarray analysis. IEEE/ACM Transactions on Computational Biology and Bioinformatics, 99. http://www.ncbi.nlm.nih.gov/pubmed/22350210
@article{ebc5cf7d5a4f4334953925451929510f,
title = "A survey on filter techniques for feature selection in gene expression microarray analysis",
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.",
keywords = "Feature selection, Information filters, Gene ranking, Biomarker discovery, Gene prioritization, Scoring functions, Statistical significance, Gene expression data, Statistical methods",
author = "Lazar, {Vasile Cosmin} and Jonatan Taminau and Stijn Meganck and David Steenhoff and Alain Coletta and Colin Molter and {De Schaetzen Van Brienen}, Virginie and Robin Duqu{\'e} and Hugues Bersini and Ann Nowe",
year = "2012",
month = feb,
day = "13",
language = "English",
volume = "99",
journal = "IEEE/ACM Transactions on Computational Biology and Bioinformatics",
issn = "1545-5963",
publisher = "Institute of Electrical and Electronics Engineers Inc.",
}