期刊
BRIEFINGS IN BIOINFORMATICS
卷 9, 期 5, 页码 392-403出版社
OXFORD UNIV PRESS
DOI: 10.1093/bib/bbn027
关键词
bioinformatics application; feature selection; penalization
资金
- NCI NIH HHS [R01 CA120988, R01CA120988-01] Funding Source: Medline
In bioinformatics studies, supervised classification with high-dimensional input variables is frequently encountered. Examples routinely arise in genomic, epigenetic and proteomic studies. Feature selection can be employed along with classifier construction to avoid over-fitting, to generate more reliable classifier and to provide more insights into the underlying causal relationships. In this article, we provide a review of several recently developed penalized feature selection and classification techniqueswhich belong to the family of embedded feature selection methodsfor bioinformatics studies with high-dimensional input. Classification objective functions, penalty functions and computational algorithms are discussed. Our goal is to make interested researchers aware of these feature selection and classification methods that are applicable to high-dimensional bioinformatics data.
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