Stable Feature Selection for Biomarker Discovery

By Zengyou He, Weichuan Yu. Comput. Biol. Chem., 2010

319 citations recorded by the source corpus.

Feature selection techniques have been used as the workhorse in biomarker discovery applications for a long time. Surprisingly, the stability of feature selection with respect to sampling variations has long been under-considered. It is only until recently that this issue has received more and more attention. In this article, we review existing stable feature selection methods for biomarker discovery using a generic hierarchical framework. We have two objectives: (1) providing an overview on this new yet fast growing topic for a convenient reference; (2) categorizing existing methods under an expandable framework for future research and development.

Subfields: Molecular Biology

View Paper: https://www.semanticscholar.org/paper/1cbfb48f339cb9281a76e83b342d526c46d3e168

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In Molecular Biology

1. Huan Liu
2. Sébastien Da Veiga
3. David Hsu
4. Clément Bénard
5. Gérard Biau
6. Jiliang Tang
7. Jundong Li
8. Tao Li

Citations in the corpus

• Identifying key variables and interactions in statistical models of building energy consumption using regularization (2015, methodology): “Studies of the stability of lasso-based feature selection using bootstrapping are discussed in He and Yu [50], Meinshausen and Bühlmann [51] and Tibshirani [52].”
• Ensembles for feature selection: A review and future trends (2019, unknown): “Therefore, the stability of ensembles for feature selection has been gaining attention in recent years [53, 54, 55, 7].”
• Feature Selection (2016, background): “Considering its importance in practical usage, stability of feature selection algorithms has received increasing attention in the community (Kalousis et al., 2007; He and Yu, 2010).”
• SIRUS: making random forests interpretable (2019, methodology): “We take advantage of a dissimilarity measure between two sets, the so-called Dice-Sorensen index, often used to assess the stability of variable selection methods (Chao et al., 2006; Zucknick et al., 2008; Boulesteix and Slawski, 2009; He and Yu, 2010; Alelyani et al., 2011).”
• Interpretable Random Forests via Rule Extraction (2020, methodology): “Such a metric is known as the Dice-Sorensen index, and is often used to assess variable selection procedures (Chao et al., 2006; Zucknick et al., 2008; Boulesteix & Slawski, 2009; He & Yu, 2010; Alelyani et al., 2011).”
• Recent advances in feature selection and its applications (2017, background): “A comprehensive survey on stable feature selection can be found in [34].”