Journal article
Penalized feature selection and classification in bioinformatics
Briefings in bioinformatics, Vol.9(5), pp.392-403
09/2008
DOI: 10.1093/bib/bbn027
PMCID: PMC2733190
PMID: 18562478
Abstract
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 techniques--which belong to the family of embedded feature selection methods--for 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.
Details
- Title: Subtitle
- Penalized feature selection and classification in bioinformatics
- Creators
- Shuangge Ma - Department of Epidemiology and Public Health, Yale University, USA. shuangge.ma@yale.eduJian Huang
- Resource Type
- Journal article
- Publication Details
- Briefings in bioinformatics, Vol.9(5), pp.392-403
- Publisher
- England
- DOI
- 10.1093/bib/bbn027
- PMID
- 18562478
- PMCID
- PMC2733190
- ISSN
- 1467-5463
- eISSN
- 1477-4054
- Grant note
- R01CA120988-01 / NCI NIH HHS R01 CA120988 / NCI NIH HHS
- Language
- English
- Date published
- 09/2008
- Academic Unit
- Statistics and Actuarial Science
- Record Identifier
- 9983986090102771
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