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 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.
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Filter feature selection methods ignore high-order interactions, which may be present and important in complex systems.
“These univariate measures are intuitive and computationally inexpensive; however, they ignore high-order interactions, which may be present, and important, in complex systems.”
From Applications of machine learning in animal behaviour studies (2017) · cites this paper as Lazar et al., 2012
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- Authors
- Lazar, C., Taminau, J., Meganck, S., Steenhoff, D., Coletta, A., Molter, C., de Schaetzen, V., Duque, R., Bersini, H., & Nowe, A.
- Affiliations
- Vrije Universiteit Brussel (BE)
- Université Libre de Bruxelles (BE)
- Venue
- IEEE/ACM Transactions on Computational Biology and Bioinformatics
- Published
- 2012-07
- Volume
- 9
- Issue
- 4
- Pages
- 1106-1119
- ISSN
- 1545-5963 (ISSN-L)
- OpenAlex
- W1964042173
- Cited by
- 619
- References
- 67
- Citations by year
- 2026: 20 · 2025: 32 · 2024: 50 · 2023: 54 · 2022: 63 · 2021: 66 · 2020: 47 · 2019: 59 · 2018: 54 · 2017: 45 · 2016: 45 · 2015: 33 · 2014: 29 · 2013: 18 · 2012: 2
- FWCI
- 10.53
- Type
- journal article
- Language
- en
- Open access
- closed
- Locations
- IEEE Transactions on Computational Biology and Bioinformatics (publishedVersion)
- PubMed (publishedVersion)
- Dépôt institutionnel de l'Université libre de Bruxelles (Université Libre de Bruxelles) (submittedVersion)
- VUBIR (Vrije Universiteit Brussel) (submittedVersion)
- Funders
- Trường Đại học Bách Khoa Hà Nội
- Vrije Universiteit Brussel
- Innoviris
- Université de Reims Champagne-Ardenne
- Topics
- Life Sciences › Biochemistry, Genetics and Molecular Biology › Molecular Biology › Gene expression and cancer classification
- Life Sciences › Biochemistry, Genetics and Molecular Biology › Molecular Biology › Bioinformatics and Genomic Networks
- Life Sciences › Biochemistry, Genetics and Molecular Biology › Cancer Research › Breast Cancer Treatment Studies
- Keywords
- feature selection, filter methods, differentially expressed genes, gene prioritization, biomarker discovery, high-dimensional data, ensemble feature selection
- MeSH terms
- Analysis of Variance, Bayes Theorem, Genetic Markers, Information Theory, ROC Curve, Models, Statistical, Statistics, Nonparametric, Computational Biology, Oligonucleotide Array Sequence Analysis, Gene Expression Profiling
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