A review of feature selection techniques in bioinformatics

A review of feature selection techniques in bioinformatics

Vol. 23 no. 19 2007, pages 2507–2517 | Yvan Saeys, Iñaki Inza and Pedro Larrañaga
This article provides a comprehensive review of feature selection techniques in bioinformatics, highlighting their importance and applications. Feature selection is crucial for improving model performance, reducing computational complexity, and enhancing interpretability. The review covers three main categories of feature selection methods: filter, wrapper, and embedded techniques, each with its own advantages and disadvantages. Filter methods assess feature relevance independently of the classifier, wrapper methods integrate feature selection with model construction, and embedded methods select features directly within the classifier. The article discusses these methods in the context of sequence analysis, microarray analysis, and mass spectrometry, emphasizing their application in bioinformatics. It also addresses challenges such as small sample sizes and the need for robust evaluation criteria. Finally, the review explores future directions, including the development of ensemble feature selection approaches and the extension of feature selection techniques to new bioinformatics domains like single nucleotide polymorphism (SNP) analysis and text and literature mining.This article provides a comprehensive review of feature selection techniques in bioinformatics, highlighting their importance and applications. Feature selection is crucial for improving model performance, reducing computational complexity, and enhancing interpretability. The review covers three main categories of feature selection methods: filter, wrapper, and embedded techniques, each with its own advantages and disadvantages. Filter methods assess feature relevance independently of the classifier, wrapper methods integrate feature selection with model construction, and embedded methods select features directly within the classifier. The article discusses these methods in the context of sequence analysis, microarray analysis, and mass spectrometry, emphasizing their application in bioinformatics. It also addresses challenges such as small sample sizes and the need for robust evaluation criteria. Finally, the review explores future directions, including the development of ensemble feature selection approaches and the extension of feature selection techniques to new bioinformatics domains like single nucleotide polymorphism (SNP) analysis and text and literature mining.
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