A mathematical programming approach for gene selection and tissue classification

被引:14
作者
Sun, MH [1 ]
Xiong, MM
机构
[1] Univ Texas, Dept Management Sci & Stat, Coll Business, San Antonio, TX 78249 USA
[2] Univ Texas, Hlth Sci Ctr, Ctr Human Genet, Houston, TX 77225 USA
关键词
D O I
10.1093/bioinformatics/btg145
中图分类号
Q5 [生物化学];
学科分类号
071010 ; 081704 ;
摘要
Motivation: Extracting useful information from expression levels of thousands of genes generated with microarry technology needs a variety of analytical techniques. Mathematical programming approaches for classification analysis outperform parametric methods when the data depart from assumptions underlying these methods. Therefore, a mathematical programming approach is developed for gene selection and tissue classification using gene expression profiles. Results: A new mixed integer programming model is formulated for this purpose. The mixed integer programming model simultaneously selects genes and constructs a classification model to classify two groups of tissue samples as accurately as possible. Very encouraging results were obtained with two data sets from the literature as examples. These results show that the mathematical programming approach can rival or outperform traditional classification methods.
引用
收藏
页码:1243 / 1251
页数:9
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