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

By Cosmin Lazar, Jonatan Taminau, Stijn Meganck, David Steenhoff, Alain Coletta, Colin Molter, V. D. Schaetzen, Robin Duque, Hugues Bersini, Ann Nowé. IEEE/ACM Transactions on Computational Biology & Bioinformatics, 2012

588 citations recorded by the source corpus.

Subfields: Molecular Biology

View Paper: https://www.semanticscholar.org/paper/2abd1bc32236d219f10d7cf1f452326b0f465f68

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In Molecular Biology

Citations in the corpus

Feature Extraction and Selection for Emotion Recognition from EEG (2014, methodology): “Generally, results obtained from applying multiple FS methods are considered more robust [50].”
Correlation and variable importance in random forests (2013, unknown): “Two classical ranking criteria are the Pearson correlation coeﬃcient and the mutual information criterion as mentioned in the recent survey of Lazar et al. [22].”
Batch effect removal methods for microarray gene expression data integration: a survey (2013, background): “…proposed is proportional to the number of ‘positive control genes’ (genes that are known apriori to be truly differentially expressed) found in the top k -ranked genes according to a particular method for differentially expressed genes (DEGs) discovery, see [59] for a recent survey on this topic.”
Selection of time instants and intervals with Support Vector Regression for multivariate functional data (2020, background): “See, for instance, Lazar et al. (2012), for a review of these approaches.”
Predictive modeling of structured electronic health records for adverse drug event detection (2015, methodology): “The former utilizes the targeted learning algorithm as a black box to evaluate the usefulness of features according to their predictive performance [36], while the latter selects features according to a score function independent of the chosen learning algorithm [37].”
A review of microarray datasets and applied feature selection methods (2014, methodology): “Lazar et al. [70] present a survey focused on filter feature selection methods 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.”
A Comprehensive Review of Dimensionality Reduction Techniques for Feature Selection and Feature Extraction (2020, methodology): “Recently, another type of evaluation method is developed, i.e., ensemble feature selection [30, 31].”
CuMiDa: An Extensively Curated Microarray Database for Benchmarking and Testing of Machine Learning Approaches in Cancer Research (2019, unknown): “The employment of feature extraction and feature selection methods on gene expression data is also common for dimensionality reduction, data visualization, as a preprocessing step for other algorithms, or to ﬁnd a subset of more relevant genes.”
A survey on feature selection methods (2014, methodology): “Filter approaches applied to various applications can be found in [19–21,18,5].”
Robust Sparse Linear Discriminant Analysis (2019, background): “2799214 for different classification tasks is a challenge work [2], [3].”
Applications of machine learning in animal behaviour studies (2017, unknown): “The relevance of each variable is assessed using measures such as correlation with the outcome and statistical significance for differences across classes (Lazar et al., 2012).”
Learning in high-dimensional multimedia data: the state of the art (2015, background): “The advantages of feature ranking are that it is computationally light and it avoids overfitting and is proven to work well for certain datasets [26, 48].”