Journal ArticleDOI
A Survey on Filter Techniques for Feature Selection in Gene Expression Microarray Analysis
Cosmin Lazar,Jonatan Taminau,Stijn Meganck,David Steenhoff,Alain Coletta,Colin Molter,V. de Schaetzen,Robin Duque,Hugues Bersini,Ann Nowé +9 more
TLDR
This survey focuses 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, and presents them in a unified framework.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.read more
Citations
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Journal ArticleDOI
A survey on feature selection methods
Girish Chandrashekar,Ferat Sahin +1 more
TL;DR: The objective is to provide a generic introduction to variable elimination which can be applied to a wide array of machine learning problems and focus on Filter, Wrapper and Embedded methods.
Journal ArticleDOI
Feature selection in machine learning: A new perspective
TL;DR: This study discusses several frequently-used evaluation measures for feature selection, and surveys supervised, unsupervised, and semi-supervised feature selection methods, which are widely applied in machine learning problems, such as classification and clustering.
Journal ArticleDOI
Feature Extraction and Selection for Emotion Recognition from EEG
TL;DR: This work reviews feature extraction methods for emotion recognition from EEG based on 33 studies, and results suggest preference to locations over parietal and centro-parietal lobes.
Journal ArticleDOI
A review of microarray datasets and applied feature selection methods
Verónica Bolón-Canedo,Noelia Sánchez-Maroño,Amparo Alonso-Betanzos,José Manuel Benítez,Francisco Herrera,Francisco Herrera +5 more
TL;DR: An experimental evaluation on the most representative datasets using well-known feature selection methods is presented, bearing in mind that the aim is not to provide the best feature selection method, but to facilitate their comparative study by the research community.
Journal ArticleDOI
Correlation and variable importance in random forests
TL;DR: This paper provides a theoretical study of the permutation importance measure for an additive regression model and motivates the use of the recursive feature elimination (RFE) algorithm for variable selection in this context.
References
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Molecular classification of cancer: class discovery and class prediction by gene expression monitoring.
Todd R. Golub,Todd R. Golub,Donna K. Slonim,Pablo Tamayo,Christine Huard,Michelle Gaasenbeek,Jill P. Mesirov,Hilary A. Coller,Mignon L. Loh,James R. Downing,Michael A. Caligiuri,Clara D. Bloomfield,Eric S. Lander +12 more
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Significance analysis of microarrays applied to the ionizing radiation response
TL;DR: A method that assigns a score to each gene on the basis of change in gene expression relative to the standard deviation of repeated measurements is described, suggesting that this repair pathway for UV-damaged DNA might play a previously unrecognized role in repairing DNA damaged by ionizing radiation.
Journal ArticleDOI
Linear Models and Empirical Bayes Methods for Assessing Differential Expression in Microarray Experiments
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