A review of feature selection techniques in bioinformatics

By Yvan Saeys, Iñaki Inza, Pedro Larrañaga. 2007

4,429 citations recorded by the source corpus.

Feature selection techniques have become an apparent need in many bioinformatics applications. In addition to the large pool of techniques that have already been developed in the machine learning and data mining fields, specific applications in bioinformatics have led to a wealth of newly proposed techniques. In this article, we make the interested reader aware of the possibilities of feature selection, providing a basic taxonomy of feature selection techniques, and discussing their use, variety and potential in a number of both common as well as upcoming bioinformatics applications.

Subfields: Molecular Biology

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Citations in the corpus

Benchmark and Survey of Automated Machine Learning Frameworks (2019, methodology): “Algorithms like univariate selection, variance threshold, feature importance, correlation matrices (Saeys et al., 2007) or stability selection (Meinshausen & Bühlmann, 2010) are already integrated in modern AutoML frameworks (Thornton et al., 2013; Komer et al., 2014; Feurer et al., 2015a; Olson &…”
Survey on Automated Machine Learning (2019, methodology): “Consequently, many different algorithms for feature selection exist—like univariate selection, variance threshold, feature importance or correlation matrices (Saeys et al., 2007)—and are already integrated in modern AutoML frameworks (Komer et al.”
On Similarity Preserving Feature Selection (2013, methodology): “As an important technique for dimensionality reduction, feature selection has been applied to various areas, including computer vision [10], text mining [13], and bioinformatics [32], to name a few.”
Machine learning in cell biology – teaching computers to recognize phenotypes (2013, background): “This is referred to as the ‘curse of dimensionality’ (Hastie et al., 2005) and can be addressed by algorithms that reduce dimensionality, for example, by selecting the most informative features (Loo et al., 2007; Saeys et al., 2007).”
Partially supervised feature selection with regularized linear models (2009, methodology): “Feature selection techniques are commonly used in this context, both to increase the interpretability of the predictive model and possibly to reduce its cost (Guyon & Elis-seef, 2003; Saeys et al., 2007).”
From Data to Optimal Decision Making: A Data-Driven, Probabilistic Machine Learning Approach to Decision Support for Patients With Sepsis (2015, neutral): “The five measurement variables were summarized by their mean and standard deviation (STD) across the trajectory of each patient [37].”
Robust Feature Selection Using Ensemble Feature Selection Techniques (2008, methodology): “They often provide a good trade-off between performance and computational cost [1,3].”
Gradient boosted feature selection (2014, background): “, bio-informatics [21] or neuroscience [12], researchers hope to gain insight by analyzing how a classifier can predict a label and what features it uses.”
Close-range hyperspectral image analysis for the early detection of stress responses in individual plants in a high-throughput phenotyping platform (2018, background): “This may be more appropriate to preserve the biophysical meaning of the variables to be interpreted by a domain expert (Saeys et al., 2007).”
Feature Selection for Classification: A Review (2014, methodology): “Embedded Models embedding feature selection with classifier construction, have the advantages of (1) wrapper models they include the interaction with the classification model and (2) filter models - they are far less computationally intensive than wrapper methods [40, 57, 46].”
A review on machine learning approaches and trends in drug discovery (2021, positive): “This provides a justiﬁca-tion that is understandable at a biological level and that is why a large majority of researchers use these techniques in their experimental designs [16].”