Penalized feature selection and classification in bioinformatics
Penalized feature selection and classification in bioinformatics
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DOI：
10.1093/bib/bbn027
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发表时间：
2008-09-01
影响因子：
9.5
通讯作者：
Huang, Jian
中科院分区：
文献类型：
--
作者：
Ma, Shuangge;Huang, Jian
在生物信息学研究中，经常遇到高维输入变量的监督分类。例子经常出现在基因组、表观遗传学和蛋白质组学研究中。特征选择可以与分类器构建一起使用，以避免过度拟合，生成更可靠的分类器，并提供对潜在因果关系的更多见解。在本文中，我们回顾了最近开发的几种惩罚特征选择和分类技术，这些技术属于用于高维输入生物信息学研究的嵌入式特征选择方法家族。讨论了分类目标函数、罚函数和计算算法。我们的目标是让感兴趣的研究人员了解这些适用于高维生物信息学数据的特征选择和分类方法。
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.